34,565 results on '"Wei, Lin"'
Search Results
2. Fully strange tetraquark resonant states as the cousins of $X(6900)$
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Ma, Yao, Wu, Wei-Lin, Meng, Lu, Chen, Yan-Ke, and Zhu, Shi-Lin
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High Energy Physics - Phenomenology ,High Energy Physics - Experiment ,High Energy Physics - Lattice ,Nuclear Theory - Abstract
We conduct systematic calculations of the S-wave fully strange systems with ``normal" $\left(J^{P C}=0^{++}, 1^{+-}, 2^{++}\right)$ and ``exotic" $\left(J^{P C}=0^{+-}, 1^{++}, 2^{+-}\right)$ C-parities, which are the strange analogue of the fully charmed tetraquark state $X(6900)$. Within a constituent quark potential model, we employ the Gaussian expansion method to solve the four-body Schr\"odinger equation and the complex scaling method to identify resonant states. We obtain a series of resonant states and zero-width states in the mass range of 2.7 to 3.3 GeV, with their widths ranging from less than 1 MeV to about 50 MeV. Their rms radii strongly indicate that they are compact tetraquark states. Among these states, the $T_{4s,2^{++}}(2714)$ may be the most likely one to be observed experimentally. We urge the experimental exploration of the $2^{++}$ $s s \bar{s} \bar{s}$ state around 2.7 GeV in the $\phi\phi$ channel. Since the lowest S-wave $s s \bar{s} \bar{s}$ state is around 2.7 GeV, the compact P-wave $s s \bar{s} \bar{s}$ states are expected to be heavier. Hence, $\phi(2170)$ and $X(2370)$ are unlikely to be compact tetraquark states., Comment: 9 pages, 2 figures. Comments are welcomed
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- 2024
3. Data Contamination Report from the 2024 CONDA Shared Task
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Sainz, Oscar, García-Ferrero, Iker, Jacovi, Alon, Campos, Jon Ander, Elazar, Yanai, Agirre, Eneko, Goldberg, Yoav, Chen, Wei-Lin, Chim, Jenny, Choshen, Leshem, D'Amico-Wong, Luca, Dell, Melissa, Fan, Run-Ze, Golchin, Shahriar, Li, Yucheng, Liu, Pengfei, Pahwa, Bhavish, Prabhu, Ameya, Sharma, Suryansh, Silcock, Emily, Solonko, Kateryna, Stap, David, Surdeanu, Mihai, Tseng, Yu-Min, Udandarao, Vishaal, Wang, Zengzhi, Xu, Ruijie, and Yang, Jinglin
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Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community., Comment: https://huggingface.co/spaces/CONDA-Workshop/Data-Contamination-Database
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- 2024
4. RouteLLM: Learning to Route LLMs with Preference Data
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Ong, Isaac, Almahairi, Amjad, Wu, Vincent, Chiang, Wei-Lin, Wu, Tianhao, Gonzalez, Joseph E., Kadous, M Waleed, and Stoica, Ion
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
Large language models (LLMs) exhibit impressive capabilities across a wide range of tasks, yet the choice of which model to use often involves a trade-off between performance and cost. More powerful models, though effective, come with higher expenses, while less capable models are more cost-effective. To address this dilemma, we propose several efficient router models that dynamically select between a stronger and a weaker LLM during inference, aiming to optimize the balance between cost and response quality. We develop a training framework for these routers leveraging human preference data and data augmentation techniques to enhance performance. Our evaluation on widely-recognized benchmarks shows that our approach significantly reduces costs-by over 2 times in certain cases-without compromising the quality of responses. Interestingly, our router models also demonstrate significant transfer learning capabilities, maintaining their performance even when the strong and weak models are changed at test time. This highlights the potential of these routers to provide a cost-effective yet high-performance solution for deploying LLMs.
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- 2024
5. Fully heavy tetraquark resonant states with different flavors
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Wu, Wei-Lin, Ma, Yao, Chen, Yan-Ke, Meng, Lu, and Zhu, Shi-Lin
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High Energy Physics - Phenomenology ,High Energy Physics - Experiment ,High Energy Physics - Lattice - Abstract
We use the quark potential model to calculate the mass spectrum of the S-wave fully heavy tetraquark systems with different flavors, including the $ bc\bar b\bar c, bb\bar c\bar c, cc\bar c\bar b $ and $ bb\bar b\bar c $ systems. We employ the Gaussian expansion method to solve the four-body Schr\"odinger equation, and the complex scaling method to identify resonant states. The $ bc\bar b\bar c, bb\bar c\bar c, cc\bar c\bar b $ and $ bb\bar b\bar c $ resonant states are obtained in the mass regions of $ (13.2,13.5) $, $ (13.3,13.6) $, $ (10.0,10.3) $, $ (16.5,16.7) $ GeV, respectively. Among these states, the $ bc\bar b\bar c $ tetraquark states are the most promising ones to be discovered in the near future. We recommend the experimental exploration of the $ 1^{++} $ and $ 2^{++} $ $ bc\bar b\bar c $ states with masses near $ 13.3 $ GeV in the $ J/\psi\Upsilon $ channel. From the root-mean-square radii, we find that all the resonant states we have identified are compact tetraquark states., Comment: 10 pages,7 figures,8 tables
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- 2024
6. InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
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Wei, Zhepei, Chen, Wei-Lin, and Meng, Yu
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Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Retrieval-augmented generation (RAG) has shown promising potential to enhance the accuracy and factuality of language models (LMs). However, imperfect retrievers or noisy corpora can introduce misleading or even erroneous information to the retrieved contents, posing a significant challenge to the generation quality. Existing RAG methods typically address this challenge by directly predicting final answers despite potentially noisy inputs, resulting in an implicit denoising process that is difficult to interpret and verify. On the other hand, the acquisition of explicit denoising supervision is often costly, involving significant human efforts. In this work, we propose InstructRAG, where LMs explicitly learn the denoising process through self-synthesized rationales -- First, we instruct the LM to explain how the ground-truth answer is derived from retrieved documents. Then, these rationales can be used either as demonstrations for in-context learning of explicit denoising or as supervised fine-tuning data to train the model. Compared to standard RAG approaches, InstructRAG requires no additional supervision, allows for easier verification of the predicted answers, and effectively improves generation accuracy. Experiments show InstructRAG consistently outperforms existing RAG methods in both training-free and trainable scenarios, achieving a relative improvement of 8.3% over the best baseline method on average across five knowledge-intensive benchmarks. Extensive analysis indicates that InstructRAG scales well with increased numbers of retrieved documents and consistently exhibits robust denoising ability even in out-of-domain datasets, demonstrating strong generalizability., Comment: Code: https://github.com/weizhepei/InstructRAG
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- 2024
7. From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
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Li, Tianle, Chiang, Wei-Lin, Frick, Evan, Dunlap, Lisa, Wu, Tianhao, Zhu, Banghua, Gonzalez, Joseph E., and Stoica, Ion
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
The rapid evolution of language models has necessitated the development of more challenging benchmarks. Current static benchmarks often struggle to consistently distinguish between the capabilities of different models and fail to align with real-world user preferences. On the other hand, live crowd-sourced platforms like the Chatbot Arena collect a wide range of natural prompts and user feedback. However, these prompts vary in sophistication and the feedback cannot be applied offline to new models. In order to ensure that benchmarks keep up with the pace of LLM development, we address how one can evaluate benchmarks on their ability to confidently separate models and their alignment with human preference. Under these principles, we developed BenchBuilder, a living benchmark that filters high-quality prompts from live data sources to enable offline evaluation on fresh, challenging prompts. BenchBuilder identifies seven indicators of a high-quality prompt, such as the requirement for domain knowledge, and utilizes an LLM annotator to select a high-quality subset of prompts from various topic clusters. The LLM evaluation process employs an LLM judge to ensure a fully automated, high-quality, and constantly updating benchmark. We apply BenchBuilder on prompts from the Chatbot Arena to create Arena-Hard-Auto v0.1: 500 challenging user prompts from a wide range of tasks. Arena-Hard-Auto v0.1 offers 3x tighter confidence intervals than MT-Bench and achieves a state-of-the-art 89.1% agreement with human preference rankings, all at a cost of only $25 and without human labelers. The BenchBuilder pipeline enhances evaluation benchmarks and provides a valuable tool for developers, enabling them to extract high-quality benchmarks from extensive data with minimal effort.
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- 2024
8. Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
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Tseng, Yu-Min, Huang, Yu-Chao, Hsiao, Teng-Yun, Chen, Wei-Lin, Huang, Chao-Wei, Meng, Yu, and Chen, Yun-Nung
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Computer Science - Computation and Language - Abstract
The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., personalized search, LLM-as-a-judge). However, the growing research on leveraging persona in LLMs is relatively disorganized and lacks a systematic taxonomy. To close the gap, we present a comprehensive survey to categorize the current state of the field. We identify two lines of research, namely (1) LLM Role-Playing, where personas are assigned to LLMs, and (2) LLM Personalization, where LLMs take care of user personas. Additionally, we introduce existing methods for LLM personality evaluation. To the best of our knowledge, we present the first survey for role-playing and personalization in LLMs under the unified view of persona. We continuously maintain a paper collection to foster future endeavors: https://github.com/MiuLab/PersonaLLM-Survey, Comment: 8-page version
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- 2024
9. OR-Bench: An Over-Refusal Benchmark for Large Language Models
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Cui, Justin, Chiang, Wei-Lin, Stoica, Ion, and Hsieh, Cho-Jui
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Large Language Models (LLMs) require careful safety alignment to prevent malicious outputs. While significant research focuses on mitigating harmful content generation, the enhanced safety often come with the side effect of over-refusal, where LLMs may reject innocuous prompts and become less helpful. Although the issue of over-refusal has been empirically observed, a systematic measurement is challenging due to the difficulty of crafting prompts that appear harmful but are benign. This study proposes a novel method for automatically generating large-scale sets of "seemingly toxic prompts" (benign prompts likely rejected by LLMs). Leveraging this technique, we introduce OR-Bench, the first large-scale over-refusal benchmark. OR-Bench comprises 80,000 seemingly toxic prompts across 10 common rejection categories, a subset of around 1,000 hard prompts that are challenging even for state-of-the-art LLMs, and an additional 600 toxic prompts to prevent indiscriminate responses. We then conduct a comprehensive study to measure the over-refusal of 25 popular LLMs across 8 model families. Our datasets are available at https://huggingface.co/datasets/bench-llm/or-bench and the demo can be found at https://huggingface.co/spaces/bench-llm/or-bench. We hope this benchmark can help the community develop better safety aligned models., Comment: version 2, 10 pages main, 22 pages total
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- 2024
10. M\'elange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity
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Griggs, Tyler, Liu, Xiaoxuan, Yu, Jiaxiang, Kim, Doyoung, Chiang, Wei-Lin, Cheung, Alvin, and Stoica, Ion
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Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Machine Learning - Abstract
Large language models (LLMs) are increasingly integrated into many online services, yet they remain cost-prohibitive to deploy due to the requirement of expensive GPU instances. Prior work has addressed the high cost of LLM serving by improving the inference engine, but less attention has been given to selecting the most cost-efficient GPU type(s) for a specific LLM service. There is a large and growing landscape of GPU types and, within these options, higher cost does not always lead to increased performance. Instead, through a comprehensive investigation, we find that three key LLM service characteristics (request size, request rate, SLO) strongly influence GPU cost efficiency, and differing GPU types are most cost efficient for differing LLM service settings. As a result, the most cost-efficient allocation for a given service is typically a mix of heterogeneous GPU types. Based on this analysis, we introduce M\'elange, a GPU allocation framework that navigates these diverse LLM service characteristics and heterogeneous GPU option space to automatically and efficiently derive the minimal-cost GPU allocation for a given LLM service. We formulate the GPU allocation task as a cost-aware bin packing problem where GPUs are bins and items are slices of the service workload. Our formulation's constraints account for a service's unique characteristics, allowing M\'elange to be flexible to support diverse service settings and heterogeneity-aware to adapt the GPU allocation to a specific service. Compared to using only a single GPU type, M\'elange reduces deployment costs by up to 77% in conversational settings, 33% in document-based settings, and 51% in a mixed setting.
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- 2024
11. Measuring Taiwanese Mandarin Language Understanding
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Chen, Po-Heng, Cheng, Sijia, Chen, Wei-Lin, Lin, Yen-Ting, and Chen, Yun-Nung
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Computer Science - Computation and Language - Abstract
The evaluation of large language models (LLMs) has drawn substantial attention in the field recently. This work focuses on evaluating LLMs in a Chinese context, specifically, for Traditional Chinese which has been largely underrepresented in existing benchmarks. We present TMLU, a holistic evaluation suit tailored for assessing the advanced knowledge and reasoning capability in LLMs, under the context of Taiwanese Mandarin. TMLU consists of an array of 37 subjects across social science, STEM, humanities, Taiwan-specific content, and others, ranging from middle school to professional levels. In addition, we curate chain-of-thought-like few-shot explanations for each subject to facilitate the evaluation of complex reasoning skills. To establish a comprehensive baseline, we conduct extensive experiments and analysis on 24 advanced LLMs. The results suggest that Chinese open-weight models demonstrate inferior performance comparing to multilingual proprietary ones, and open-weight models tailored for Taiwanese Mandarin lag behind the Simplified-Chinese counterparts. The findings indicate great headrooms for improvement, and emphasize the goal of TMLU to foster the development of localized Taiwanese-Mandarin LLMs. We release the benchmark and evaluation scripts for the community to promote future research., Comment: Preprint. Under review
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- 2024
12. Topological edge modes and phase transition in the critical fermionic chain with long-range interaction
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Zhong, Wen-Hao, Li, Wei-Lin, Chen, Yong-Chang, and Yu, Xue-Jia
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Condensed Matter - Strongly Correlated Electrons ,Condensed Matter - Statistical Mechanics - Abstract
The long-range interaction can fundamentally alter properties in gapped topological phases such as emergent massive edge modes. However, recent research has shifted attention to topological nontrivial critical points or phases, and it is natural to explore how long-range interaction influences them. In this work, we investigate the topological behavior and phase transition of extended Kitaev chains with long-range interactions, which can be derived from the critical Ising model via the Jordan-Wigner transformation in the short-range limit. Specifically, we analytically find the critical edge modes at the critical point remain stable against long-range interaction. More importantly, we observe these critical edge modes remain massless even when long-range interactions become substantially strong. As a byproduct, we numerically find that the critical behavior of the long-range model belongs to the free Majorana fermion universality class, which is entirely different from the long-range universality class in usual long-range spin models. Our work could shed new light on the interplay between long-range interactions (frustrated) and the gapless topological phases of matter., Comment: 13 pages, 11 figures. Any comments or suggestions are welcome !
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- 2024
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13. Quantum phase transition between topologically distinct quantum critical points
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Yu, Xue-Jia and Li, Wei-Lin
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Condensed Matter - Strongly Correlated Electrons - Abstract
By constructing an exactly solvable spin model, we investigate the critical behaviors of transverse field Ising chains interpolated with cluster interactions, which exhibit various types of topologically distinct Ising critical points. Using fidelity susceptibility as an indicator, we establish the global phase diagram, including ferromagnetic, trivial paramagnetic, and symmetry-protected topological phases. Different types of critical points exist between these phases, encompassing both topologically trivial and non-trivial Ising critical points, as well as Gaussian critical points. Importantly, we demonstrate the existence of a Lifshitz transition between these topologically distinct Ising critical points, with central charge and critical exponents determined through finite-size scaling. This work serves as a valuable reference for further research on phase transitions within the gapless quantum phase of matter., Comment: 13 pages, 9 figures. Any comments or suggestions are welcome!
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- 2024
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14. Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
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Chiang, Wei-Lin, Zheng, Lianmin, Sheng, Ying, Angelopoulos, Anastasios Nikolas, Li, Tianle, Li, Dacheng, Zhang, Hao, Zhu, Banghua, Jordan, Michael, Gonzalez, Joseph E., and Stoica, Ion
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Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences. Our methodology employs a pairwise comparison approach and leverages input from a diverse user base through crowdsourcing. The platform has been operational for several months, amassing over 240K votes. This paper describes the platform, analyzes the data we have collected so far, and explains the tried-and-true statistical methods we are using for efficient and accurate evaluation and ranking of models. We confirm that the crowdsourced questions are sufficiently diverse and discriminating and that the crowdsourced human votes are in good agreement with those of expert raters. These analyses collectively establish a robust foundation for the credibility of Chatbot Arena. Because of its unique value and openness, Chatbot Arena has emerged as one of the most referenced LLM leaderboards, widely cited by leading LLM developers and companies. Our demo is publicly available at \url{https://chat.lmsys.org}.
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- 2024
15. Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond
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Liu, Yuanyuan, Wang, Ke, Wei, Lin, Chen, Jingying, Zhan, Yibing, Tao, Dapeng, and Chen, Zhe
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Computer Science - Human-Computer Interaction - Abstract
Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient communication. However, there is a noticeable absence of a comprehensive summary of recent advancements in affective computing for healthcare, which could pose difficulties for researchers entering this field. To address this, our paper aims to provide an extensive literature review of related studies published in the last five years. We begin by analyzing trends, benefits, and limitations of recent datasets and affective computing methods devised for healthcare. Subsequently, we highlight several healthcare application hotspots of current technologies that could be promising for real-world deployment. Through our analysis, we identify and discuss some ongoing challenges in the field as evidenced by the literature. Concluding with a thorough review, we further offer potential future research directions and hope our findings and insights could guide related researchers to make better contributions to the evolution of affective computing in healthcare.
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- 2024
16. Benchmark calculations of fully heavy compact and molecular tetraquark states
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Wu, Wei-Lin, Chen, Yan-Ke, Meng, Lu, and Zhu, Shi-Lin
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High Energy Physics - Phenomenology ,High Energy Physics - Experiment ,High Energy Physics - Lattice ,Nuclear Theory - Abstract
We calculate the mass spectrum of the S-wave fully heavy tetraquark systems $ QQ\bar Q\bar Q~(Q=c,b) $ with both normal $ (J^{PC}=0^{++},1^{+-},2^{++}) $ and exotic $ (J^{PC}=0^{+-},1^{++},2^{+-}) $ C-parities using three different quark potential models (AL1, AP1, BGS). The exotic C-parity systems refer to the ones that cannot be composed of two S-wave ground heavy quarkonia. We incorporate the molecular dimeson and compact diquark-antidiquark spatial correlations simultaneously, thereby discerning the actual configurations of the states. We employ the Gaussian expansion method to solve the four-body Schr\"odinger equation, and the complex scaling method to identify the resonant states. The mass spectra in three different models qualitatively agree with each other. We obtain several resonant states with $ J^{PC} = 0^{++}, 1^{+-}, 2^{++}, 1^{++} $ in the mass region $(6.92,7.30)\, \mathrm{GeV}$, some of which are good candidates of the experimentally observed $X(6900)$ and $X(7200)$. We also obtain several exotic C-parity zero-width states with $ J^{PC}=0^{+-} $ and $ 2^{+-} $. These zero-width states have no corresponding S-wave diquarkonium threshold and can only decay strongly to final states with P-wave quarkonia. With the notation $T_{4Q,J(C)}(M)$, we deduce from the root mean square radii that the $ X(7200) $ candidates $ T_{4c,0(+)}(7173), T_{4c,2(+)}(7214) $ and the state $ T_{4c,1(-)}(7191) $ look like molecular states although most of the resonant and zero-width states are compact states., Comment: 16 pages, 6 figures, 10 tables. Version accepted by PRD
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- 2024
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17. Distance Learners' Emotional Intelligence and Perceptions of Their Situational Barriers in Learning English
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Teoh, George Boon Sai and Liau, Agnes Wei Lin
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Students face various barriers in learning English via distance education. The purpose of this explanatory mixed methods study was to collect quantitative and qualitative data regarding students' emotional intelligence (EI) and the situational barriers (SB) they faced in learning English via distance education. In phase one, 238 students voluntarily submitted their questionnaires containing Schutte's Self-Report Inventory Emotional Intelligence scale. The mean EI score of the distance learners was 130. Based on the EI scores, three subgroups of respondents; HighEI, MidEI, and LowEI were identified. In phase two, 18 participants chosen based on their EI scores were interviewed. The interviews were transcribed and analyzed using content analysis. The qualitative data revealed eight major situational barriers. Out of these, SB1 Job, SB2 Family and Home, and SB4 Time were most frequently cited. The study also found that distance learners' EI was significantly related to their perceptions of the situational barriers to learning English through distance education. Studies affirmed higher EI students are better at moderating the negative effects of challenging circumstances and achieving their goals. Thus, it is crucial to fortify students' EI through training, mentoring, and counselling programs to empower them to moderate their barriers, persevere, communicate with others, and make informed decisions to achieve academic success.
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- 2023
18. Preparation and Rheological Characterization of Long Chain Branching Polyglycolide
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Liu, Wei-Lin, Lyu, Ming-Fu, Zhang, Heng-Yuan, Liu, Jian-Ye, and Zhang, Shi-Jun
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- 2024
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19. Age-adjusted Charlson Comorbidity Index as an effective tool for the choice between simultaneous or staged bilateral total knee arthroplasty
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Chang, Wei-Lin, Lee, Kun-Han, Tsai, Shang-Wen, Chen, Cheng-Fong, Wu, Po-Kuei, and Chen, Wei-Ming
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- 2024
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20. Left ventricular hypertrophy and left atrial diameter are associated with mortality risk in haemodialysis patients: a retrospective cohort study
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Bai, Jiuxu, Huang, Wanqing, Zhang, Yanping, Wei, Lin, Zhao, Chen, Ren, Zhuo, Wang, Qian, Ren, Kaiming, and Cao, Ning
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- 2024
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21. Effects of Humic Acid-Copper Interactions on Growth, Nutrient Absorption, and Photosynthetic Performance of Citrus sinensis Seedlings in Sand Culture
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Huang, Wei-Tao, Shen, Qian, Yang, Hui, Chen, Xu-Feng, Huang, Wei-Lin, Wu, Han-Xue, Lai, Ning-Wei, Yang, Lin-Tong, Huang, Zeng-Rong, and Chen, Li-Song
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- 2024
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22. Clarifying the four core effects of high-entropy materials
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Hsu, Wei-Lin, Tsai, Che-Wei, Yeh, An-Chou, and Yeh, Jien-Wei
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- 2024
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23. Anisotropy measurements and characterization of the Qingshankou shale
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Li, Qing-feng, Yan, Xue-hong, Yan, Wei-lin, Ren, Li, Wang, Peng, Han, Jian-qiang, Xia, Xue, and Chen, Hao
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- 2024
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24. Itô-Taylor Expansion Method of European Spread Option Pricing for Multivariate Diffusions with Jumps
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Wang, Ge, Lu, Yu-xuan, Zhou, Qing, and Xiao, Wei-lin
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- 2024
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25. Management of oncology-related emergencies at the emergency department: A long-term undertaking
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Chua, Wei Lin Tallie, Chan, Shi En Joanna, Lai, Gillianne, Tracy Yong, Looi Yee, Kanesvaran, Ravindran, and Anantharaman, Venkataraman
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- 2023
26. Rethinking Benchmark and Contamination for Language Models with Rephrased Samples
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Yang, Shuo, Chiang, Wei-Lin, Zheng, Lianmin, Gonzalez, Joseph E., and Stoica, Ion
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Large language models are increasingly trained on all the data ever produced by humans. Many have raised concerns about the trustworthiness of public benchmarks due to potential contamination in pre-training or fine-tuning datasets. While most data decontamination efforts apply string matching (e.g., n-gram overlap) to remove benchmark data, we show that these methods are insufficient, and simple variations of test data (e.g., paraphrasing, translation) can easily bypass these decontamination measures. Furthermore, we demonstrate that if such variation of test data is not eliminated, a 13B model can easily overfit a test benchmark and achieve drastically high performance, on par with GPT-4. We validate such observations in widely used benchmarks such as MMLU, GSK8k, and HumanEval. To address this growing risk, we propose a stronger LLM-based decontamination method and apply it to widely used pre-training and fine-tuning datasets, revealing significant previously unknown test overlap. For example, in pre-training sets such as RedPajama-Data-1T and StarCoder-Data, we identified that 8-18\% of the HumanEval benchmark overlaps. Interestingly, we also find such contamination in synthetic dataset generated by GPT-3.5/4, suggesting a potential risk of unintentional contamination. We urge the community to adopt stronger decontamination approaches when using public benchmarks. Moreover, we call for the community to actively develop fresh one-time exams to evaluate models accurately. Our decontamination tool is publicly available at https://github.com/lm-sys/llm-decontaminator.
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- 2023
27. Unified description of the $Qs \bar q \bar q$ molecular bound states, molecular resonances and compact tetraquark states in the quark potential model
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Chen, Yan-Ke, Wu, Wei-Lin, Meng, Lu, and Zhu, Shi-Lin
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High Energy Physics - Phenomenology ,High Energy Physics - Experiment ,High Energy Physics - Lattice ,Nuclear Theory - Abstract
We calculate the mass spectrum of the $Qs\bar q \bar q$ $(Q=c, b)$ tetraquark states with $J^P=(0,1,2)^+$ using the AL1 quark potential model, which successfully describes the conventional hadron spectrum. We employ the Gaussian expansion method to solve the four-body Schr\"odinger equation, and use the complex scaling method to identify the resonances. With the notation $T_{Q s, I(J)}^{\text {Theo. }}(M)$, we find several near-threshold bound states and resonances, including $T_{cs,0(0)}^{\mathrm{Theo.}}(2350)$, $T_{cs,0(0)}^{\mathrm{Theo.}}(2906)$, $T_{bs,0(0)}^{\mathrm{Theo.}}(5781)$, $T_{bs,0(1)}^{\mathrm{Theo.}}(5840)$, and $T_{bs,0(0)}^{\mathrm{Theo.}}(6240)$ which are close to the $D\bar{K}$, $D^*\bar{K}^*$, $\bar{B}\bar{K}$, $\bar{B}^*\bar{K}$ and $\bar{B}^*\bar{K}^*$ thresholds, respectively. Furthermore, their spatial structures clearly support their molecular natures. The resonance $T_{cs,0(0)}^{\mathrm{Theo.}}(2906)$ has a mass of $2906$ MeV, a width of $20$ MeV, and quantum numbers $I(J^P)=0(0^+)$, which may serve as a good candidate for the experimental $T_{cs0}(2900)$ state. We strongly urge the experimental search of the predicted states., Comment: 9 pages, 4 figures, 3 tables. Version accepted by PRD
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- 2023
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28. Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation
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Chen, Wei-Lin, Wu, Cheng-Kuang, Chen, Hsin-Hsi, and Chen, Chung-Chi
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Computer Science - Computation and Language - Abstract
In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. We propose a new decoding method called Fidelity-Enriched Contrastive Search (FECS), which augments the contrastive search framework with context-aware regularization terms. FECS promotes tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text. We demonstrate its effectiveness across two tasks prone to hallucination: abstractive summarization and dialogue generation. Results show that FECS consistently enhances faithfulness across various language model sizes while maintaining output diversity comparable to well-performing decoding algorithms., Comment: Accepted as a short paper at EMNLP 2023
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- 2023
29. Programmable order by disorder effect and underlying phases through dipolar quantum simulators
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Wu, Huan-Kuang, Suzuki, Takafumi, Kawashima, Naoki, and Tu, Wei-Lin
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Condensed Matter - Quantum Gases ,Condensed Matter - Strongly Correlated Electrons ,Quantum Physics - Abstract
In this work, we study two different quantum simulators composed of molecules with dipole-dipole interaction through various theoretical and numerical tools. Our first result provides knowledge upon the quantum order by disorder effect of the $S=1/2$ system, which is programmable in a quantum simulator composed of circular Rydberg atoms in the triangular optical lattice with a controllable diagonal anisotropy. When the numbers of up spins and down spins are equal, a set of sub-extensive degenerate ground states is present in the classical limit, composed of continuous strings whose configuration enjoys a large degree of freedom. Adopting the the real space perturbation theory, our calculation demonstrates a lifting of the degeneracy, favoring the stripe configuration. When $J$ becomes larger, we adopt the infinite projected entangled-pair state~(iPEPS) and numerically check the effect of degeneracy lifting. The iPEPS results show that even when the spin exchange coupling is strong the stripe pattern is still favored. Next, we study the dipolar bosonic model with tilted polar angle which can be realized through a quantum simulator composed of cold atomic gas with dipole-dipole interaction in an optical lattice. By placing the atoms in a triangular lattice and tilting the polar angle, the diagonal anisotropy can also be realized in the bosonic system. With our cluster mean-field theory calculation, we provide various phase diagrams with different tilted angles, showing the abundant underlying phases including the supersolid. Our proposal indicates realizable scenarios through quantum simulators in studying the quantum effect as well as extraordinary phases. We believe that our results indicated here can also become a good benchmark for the two-dimensional quantum simulators., Comment: 15 pages, 6 figures, 2 tables
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- 2023
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30. LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
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Zheng, Lianmin, Chiang, Wei-Lin, Sheng, Ying, Li, Tianle, Zhuang, Siyuan, Wu, Zhanghao, Zhuang, Yonghao, Li, Zhuohan, Lin, Zi, Xing, Eric P., Gonzalez, Joseph E., Stoica, Ion, and Zhang, Hao
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications. In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website. We offer an overview of the dataset's content, including its curation process, basic statistics, and topic distribution, highlighting its diversity, originality, and scale. We demonstrate its versatility through four use cases: developing content moderation models that perform similarly to GPT-4, building a safety benchmark, training instruction-following models that perform similarly to Vicuna, and creating challenging benchmark questions. We believe that this dataset will serve as a valuable resource for understanding and advancing LLM capabilities. The dataset is publicly available at https://huggingface.co/datasets/lmsys/lmsys-chat-1m.
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- 2023
31. Learning Motivation, Outcomes, and Anxiety in Programming Courses--A Computational Thinking-Centered Method
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Lung-Chun Chang, Hon-Ren Lin, and Jian-Wei Lin
- Abstract
Many students want to enroll in programming courses but fear the challenges ahead. They aspire to design quality systems or games after acquiring related skills but report concerns that programming logic is too difficult to learn because memorization of the syntax is required. Thus, they experience anxiety, are demotivated to learn, and, regretfully, may never enroll in programming courses. Computational thinking (CT) is a favorable method currently used in learning logic. This study proposed an easily implementable standard operating procedure for CT and incorporated it into programming courses; students were instructed and enabled to clarify the logical sequence before beginning to write a program. The standard operating procedure for CT contains five training steps, identify the problem, formulate the problem-solving steps, organize and summarize, draw a flowchart, and write a program. This kind of training can help students clarify the logical order more clearly to facilitate writing programs, thereby improving motivation to learn, reducing learning anxiety, and ultimately improving learning outcomes. The experiment results revealed significant results regarding the learning outcomes, motivation to learn, and learning anxiety of the experimental group learning programming through CT-centered teaching in comparison with those of the group learning through conventional teaching. Additionally, for female students, who were revealed to be initially less capable of logical thinking than male students, the following post intervention improvements were observed: adequate improvement in learning outcomes, increased motivation to learn, and reduced learning anxiety.
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- 2024
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32. Wall segmentation in house plans: fusion of deep learning and traditional methods
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Wei, Lin and Lai, Chenghui
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- 2024
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33. Different wavelengths of LED irradiation promote secondary metabolite production in Pycnoporus sanguineus for antioxidant and immunomodulatory applications
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Lim, Chui Li, Yang, Chao-Hsun, Pan, Xin-Yu, Tsai, Hsiao-Yun, Chen, Cheng-Yu, and Chen, Wei-Lin
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- 2024
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34. TRIM38 Induced in Respiratory Syncytial Virus-infected Cells Downregulates Type I Interferon Expression by Competing with TRIM25 to Bind RIG-I
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Sun, Qingqing, Han, Xiao, Meng, Lingtong, Li, Hongru, Chen, Yijia, Yin, Lizheng, Wang, Chang, Wang, Jiachao, Li, Miao, Gao, Xue, Li, Wenjian, Wei, Lin, and Ma, Cuiqing
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- 2024
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35. High-Efficiency Multiphase Stacked Interleaved DC-DC Buck Converter with Very Low Output Current Ripple and Low Current–Voltage Stress
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Pang, Yi, Li, Wei-Lin, Sun, He-Xu, Pan, Lei, Meng, Fan-Tao, and Liang, Yin
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- 2024
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36. Single Particle Colorimetric Acid Phosphatase Activity Assay with CeO2-modified Gold Nanoparticles
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Zhu, Tang Jiayao, Yuan, Xiang, Mao, Guojiang, Wei, Lin, and Xiao, Lehui
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- 2024
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37. Multisensory hallucinations and other unusual sensory experiences in the context of migraine: a systematic review
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Li, Yixuan, Yolland, Caitlin O. B., Rossell, Susan L., Sommer, Iris E. C., and Toh, Wei Lin
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- 2024
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38. Exceptional Thermal Stability and Properties of Amorphous High-Entropy SiNbTaTiZr Thin Films
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Hsu, Wei-Lin, Cheng, Chun-Yang, and Yeh, Jien-Wei
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- 2024
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39. Boron deficiency decreased the root activity of Ga-exposed rice seedlings by reducing iron accumulation and increasing Ga in iron plaque
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Ren, Wei-Lin, Li, Cheng-Zhi, Ullah, Abid, and Yu, Xiao-Zhang
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- 2024
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40. Greenhouse Evaluation of Biochar-Based Controlled-Release Nitrogen Fertilizer in Corn Production
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Rubel, Robiul Islam, Wei, Lin, Wu, Yajun, Gupta, Surbhi, Alanazi, Salman, Sobhan, Abdus, Osabutey, Augustina, and Yang, Xufei
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- 2024
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41. Large Language Models Perform Diagnostic Reasoning
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Wu, Cheng-Kuang, Chen, Wei-Lin, and Chen, Hsin-Hsi
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Computer Science - Computation and Language - Abstract
We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the diagnostic accuracy improves by 15% comparing to standard prompting. Moreover, the gap reaches a pronounced 18% in out-domain settings. Our findings suggest expert-knowledge reasoning in large language models can be elicited through proper promptings., Comment: Accepted as a Tiny Paper at ICLR 2023 (10 pages, 5 figures)
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- 2023
42. Generating function for projected entangled-pair states
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Tu, Wei-Lin, Vanderstraeten, Laurens, Schuch, Norbert, Lee, Hyun-Yong, Kawashima, Naoki, and Chen, Ji-Yao
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Condensed Matter - Strongly Correlated Electrons ,Quantum Physics - Abstract
Diagrammatic summation is a common bottleneck in modern applications of projected entangled-pair states, especially in computing low-energy excitations of a two-dimensional quantum many-body system. To solve this problem, here we extend the generating function approach for tensor network diagrammatic summation, a scheme previously proposed in the context of matrix product states. Taking the form of a one-particle excitation, we show that the excited state can be computed efficiently in the generating function formalism, which can further be used in evaluating the dynamical structure factor of the system. Our benchmark results for the spin-$1/2$ transverse-field Ising model and Heisenberg model on the square lattice provide a desirable accuracy, showing good agreement with known results. We then study the spin-$1/2$ $J_1$-$J_2$ model on the same lattice and investigate the dynamical properties of the putative gapless spin liquid phase. We conclude with a discussion on generalizations to multi-particle excitations., Comment: 13 pages, 6 figures
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- 2023
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43. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
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Zheng, Lianmin, Chiang, Wei-Lin, Sheng, Ying, Zhuang, Siyuan, Wu, Zhanghao, Zhuang, Yonghao, Lin, Zi, Li, Zhuohan, Li, Dacheng, Xing, Eric P., Zhang, Hao, Gonzalez, Joseph E., and Stoica, Ion
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, including position, verbosity, and self-enhancement biases, as well as limited reasoning ability, and propose solutions to mitigate some of them. We then verify the agreement between LLM judges and human preferences by introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot Arena, a crowdsourced battle platform. Our results reveal that strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80% agreement, the same level of agreement between humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain. Additionally, we show our benchmark and traditional benchmarks complement each other by evaluating several variants of LLaMA and Vicuna. The MT-bench questions, 3K expert votes, and 30K conversations with human preferences are publicly available at https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge., Comment: NeurIPS 2023 Datasets and Benchmarks Track
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- 2023
44. Myeloid-derived suppressor cells-induced exhaustion of CD8 + T-cell participates in rejection after liver transplantation
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Zhou, Liu-Xin, Jiang, Yi-Zhou, Li, Xin-Qiang, Zhang, Jin-Ming, Li, Shi-Peng, Wei, Lin, Zhang, Hai-Ming, Zhou, Guang-Peng, Chen, Xiao-Jie, Sun, Li-Ying, and Zhu, Zhi-Jun
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- 2024
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45. A scoping review of determinants of indigenous health and health disparities in Taiwan: present evidence and paradigms
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Chen, Jen-Hao, Chen, Wei-Lin, and Shiu, Cheng-Shi
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- 2024
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46. A novel Bayesian generative approach for estimating tumor dynamics from published studies
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Arya Pourzanjani, Saurabh Modi, Jamie Connarn, Xinwen Zhang, Vijay Upreti, Chih‐Wei Lin, and Khamir Mehta
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Therapeutics. Pharmacology ,RM1-950 - Abstract
Abstract Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure–response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression‐free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR data is introduced to learn TGI model parameters using only published summary data. The parameterized model can describe the tumor dynamics, quantify treatment effect, and account for differences in the study population. The utility of this model is shown by applying it to several published studies, and learned parameters are combined to simulate an in silico trial of a novel combination therapy in a novel setting.
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- 2024
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47. Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot
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Shuai Chen, Zhongliang Zhou, Kunqi Hou, Xihu Wu, Qiang He, Cindy G. Tang, Ting Li, Xiujuan Zhang, Jiansheng Jie, Zhiyi Gao, Nripan Mathews, and Wei Lin Leong
- Subjects
Science - Abstract
Abstract The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, such devices remain bulky and lack reliable competence to functionalize further synaptic devices with proprioceptive feedback. Here, we report an artificial organic afferent nerve with low operating bias (−0.6 V) achieved by integrating a pressure-activated organic electrochemical synaptic transistor and artificial mechanoreceptors. The dendritic integration function for neurorobotics is achieved to perceive directional movement of object, further reducing the control complexity by exploiting the distributed and parallel networks. An intelligent robot assembled with artificial afferent nerve, coupled with a closed-loop feedback program is demonstrated to rapidly implement slip recognition and prevention actions upon occurrence of object slippage. The spatiotemporal features of tactile patterns are well differentiated with a high recognition accuracy after processing spike-encoded signals with deep learning model. This work represents a breakthrough in mimicking synaptic behaviors, which is essential for next-generation intelligent neurorobotics and low-power biomimetic electronics.
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- 2024
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48. Exploring the relationship between gut microbiota and breast cancer risk in European and East Asian populations using Mendelian randomization
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Wei Lin, Chenghao Gu, Zheyin Chen, Shihang Xue, Haiyan Wu, and Liuhai Zeng
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Gut microbiota ,Breast cancer ,Mendelian randomization ,Europeans ,East Asians ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Background Several studies have explored the potential link between gut microbiota and breast cancer; nevertheless, the causal relationship between gut microbiota and breast cancer remains unclear. Methods We utilized summary statistics from genome-wide association studies (GWAS) of the gut microbiome from the MiBioGen project with summary data from GWAS on breast cancer from the FinnGen consortium and the IEU database, with the IEU data sourced from the Biobank Japan. Preliminary statistical analyses were conducted using inverse variance weighting (IVW), supplemented by various sensitivity analysis methods, including MR-Egger regression, weighted median, weighted mode, simple median, and simple mode, to ensure the robustness of our findings. Heterogeneity and pleiotropy were assessed to avoid misleading conclusions caused by unconsidered confounders or non-specific effects of genetic variants, ensuring that the results reflect a genuine causal relationship. Results In European populations, four types of gut microbiota were associated with breast cancer. The genus Erysipelatoclostridium was positively associated with the risk of breast cancer, with an odds ratio (OR) of 1.21 (95% confidence interval [CI] 1.083–1.358), false discovery rate (FDR) = 0.0039. The class Coriobacteriia, order Coriobacteriales, and family Coriobacteriaceae, which belong to the same phylogenetic system, showed a consistent inversely association with breast cancer risk, with an OR of 0.757 (95% CI 0.616–0.930), FDR = 0.0281. In East Asian populations, three types of gut microbiota were related to breast cancer. The Eubacterium ruminantium group was positively associated with breast cancer risk, with an OR of 1.259 (95% CI 1.056–1.499), FDR = 0.0497. The families Porphyromonadaceae and Ruminococcaceae were inversely associated with breast cancer risk, with ORs of 0.304 (95% CI 0.155–0.596), FDR = 0.0005, and 0.674 (95% CI 0.508–0.895), FDR = 0.03173, respectively. However, these two taxa had limited instrumental variables, restricting the statistical power and potentially affecting the interpretation of the results. Conclusion This MR analysis demonstrated a probable causal link between specific gut microbiota and breast cancer. This study, through Mendelian randomization analysis comparing European and East Asian populations, reveals that gut microbiota may influence breast cancer risk differently across populations, providing potential directions for developing targeted prevention and treatment methods.
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- 2024
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49. Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients
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Tsair-Fwu Lee, Chu-Ho Chang, Chih-Hsuan Chi, Yen-Hsien Liu, Jen-Chung Shao, Yang-Wei Hsieh, Pei-Ying Yang, Chin-Dar Tseng, Chien-Liang Chiu, Yu-Chang Hu, Yu-Wei Lin, Pei-Ju Chao, Shen-Hao Lee, and Shyh-An Yeh
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Artificial intelligence ,Radiomics ,Dosiomics ,Radiation dermatitis ,Breast cancer ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Purpose This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT). Materials and methods This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value
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- 2024
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50. FGF4 ameliorates the liver inflammation by reducing M1 macrophage polarization in experimental autoimmune hepatitis
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Jing Lin, Hong-wei Lin, Yu-xing Wang, Yan Fang, Hui-mian Jiang, Ting Li, Jia Huang, Hua-dong Zhang, Da-zhi Chen, and Yong-ping Chen
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Fibroblast growth factor 4 ,Experimental autoimmune hepatitis ,M1 macrophage polarization ,PI3K/AKT signal pathway ,Medicine - Abstract
Abstract Background The global prevalence of autoimmune hepatitis (AIH) is increasing due in part to the lack of effective pharmacotherapies. Growing evidence suggests that fibroblast growth factor 4 (FGF4) is crucial for diverse aspects of liver pathophysiology. However, its role in AIH remains unknown. Therefore, we investigated whether FGF4 can regulate M1 macrophage and thereby help treat liver inflammation in AIH. Methods We obtained transcriptome-sequencing and clinical data for patients with AIH. Mice were injected with concanavalin A to induce experimental autoimmune hepatitis (EAH). The mechanism of action of FGF4 was examined using macrophage cell lines and bone marrow-derived macrophages. Results We observed higher expression of markers associated with M1 and M2 macrophages in patients with AIH than that in individuals without AIH. EAH mice showed greater M1-macrophage polarization than control mice. The expression of M1-macrophage markers correlated positively with FGF4 expression. The loss of hepatic Fgf4 aggravated hepatic inflammation by increasing the abundance of M1 macrophages. In contrast, the pharmacological administration of FGF4 mitigated hepatic inflammation by reducing M1-macrophage levels. The efficacy of FGF4 treatment was compromised following the in vivo clearance of macrophage populations. Mechanistically, FGF4 treatment activated the phosphatidylinositol 3-kinase (PI3K)–protein kinase B (AKT)-signal pathway in macrophages, which led to reduced M1 macrophages and hepatic inflammation. Conclusion We identified FGF4 as a novel M1/M2 macrophage-phenotype regulator that acts through the PI3K–AKT-signaling pathway, suggesting that FGF4 may represent a novel target for treating inflammation in patients with AIH.
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- 2024
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