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This white paper is the output of a multidisciplinary workshop in Nairobi (Nov 2023). Led by a cross-organisational team including Microsoft Research, NEPAD, Lelapa AI, and University of Oxford. The workshop brought together diverse thought-leaders from various sectors and backgrounds to discuss the implications of Generative AI for the future of work in Africa. Discussions centred around four key themes: Macroeconomic Impacts; Jobs, Skills and Labour Markets; Workers' Perspectives and Africa-Centris AI Platforms. The white paper provides an overview of the current state and trends of generative AI and its applications in different domains, as well as the challenges and risks associated with its adoption and regulation. It represents a diverse set of perspectives to create a set of insights and recommendations which aim to encourage debate and collaborative action towards creating a dignified future of work for everyone across Africa.

The exponential growth of knowledge and the increasing complexity of interdisciplinary research pose significant challenges for researchers, including information overload and difficulties in exploring novel ideas. The advancements in large language models (LLMs), such as GPT-4, have shown great potential in enhancing idea proposals, but how to effectively utilize large models for reasonable idea proposal has not been thoroughly explored. This paper proposes a scientific paper idea proposer (SciPIP). Based on a user-provided research background, SciPIP retrieves helpful papers from a literature database while leveraging the capabilities of LLMs to generate more novel and feasible ideas. To this end, 1) we construct a literature retrieval database, extracting lots of papers' multi-dimension information for fast access. Then, a literature retrieval method based on semantics, entity, and citation co-occurrences is proposed to search relevant literature from multiple aspects based on the user-provided background. 2) After literature retrieval, we introduce dual-path idea proposal strategies, where one path infers solutions from the retrieved literature and the other path generates original ideas through model brainstorming. We then combine the two to achieve a good balance between feasibility and originality. Through extensive experiments on the natural language processing (NLP) field, we demonstrate that SciPIP can retrieve citations similar to those of existing top conference papers and generate many ideas consistent with them. Additionally, we evaluate the originality of other ideas generated by SciPIP using large language models, further validating the effectiveness of our proposed method. The code and the database are released at https://github.com/cheerss/SciPIP., Comment: 25 pages, 5 figures, 19 tables

Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often struggles to match the underlying academic concepts between queries and documents, which is critical for paper search. To enable effective academic concept matching for paper search, we propose Taxonomy-guided Semantic Indexing (TaxoIndex) framework. TaxoIndex extracts key concepts from papers and organizes them as a semantic index guided by an academic taxonomy, and then leverages this index as foundational knowledge to identify academic concepts and link queries and documents. As a plug-and-play framework, TaxoIndex can be flexibly employed to enhance existing dense retrievers. Extensive experiments show that TaxoIndex brings significant improvements, even with highly limited training data, and greatly enhances interpretability., Comment: EMNLP'24

The patent domain is gaining attention in natural language processing research, offering practical applications in streamlining the patenting process and providing challenging benchmarks for large language models (LLMs). However, the generation of the description sections of patents, which constitute more than 90% of the patent document, has not been studied to date. We address this gap by introducing the task of outline-guided paper-to-patent generation, where an academic paper provides the technical specification of the invention and an outline conveys the desired patent structure. We present PAP2PAT, a new challenging benchmark of 1.8k patent-paper pairs with document outlines, collected using heuristics that reflect typical research lab practices. Our experiments with current open-weight LLMs and outline-guided chunk-based generation show that they can effectively use information from the paper but struggle with repetitions, likely due to the inherent repetitiveness of patent language. We release our data and code.

The scientific ideation process often involves blending salient aspects of existing papers to create new ideas. To see if large language models (LLMs) can assist this process, we contribute Scideator, a novel mixed-initiative tool for scientific ideation. Starting from a user-provided set of papers, Scideator extracts key facets (purposes, mechanisms, and evaluations) from these and relevant papers, allowing users to explore the idea space by interactively recombining facets to synthesize inventive ideas. Scideator also helps users to gauge idea novelty by searching the literature for potential overlaps and showing automated novelty assessments and explanations. To support these tasks, Scideator introduces four LLM-powered retrieval-augmented generation (RAG) modules: Analogous Paper Facet Finder, Faceted Idea Generator, Idea Novelty Checker, and Idea Novelty Iterator. In a within-subjects user study, 19 computer-science researchers identified significantly more interesting ideas using Scideator compared to a strong baseline combining a scientific search engine with LLM interaction.

The title of a research paper communicates in a succinct style the main theme and, sometimes, the findings of the paper. Coming up with the right title is often an arduous task, and therefore, it would be beneficial to authors if title generation can be automated. In this paper, we fine-tune pre-trained language models to generate titles of papers from their abstracts. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate paper titles. The performance of the models is measured with ROUGE, METEOR, MoverScore, BERTScore and SciBERTScore metrics. We find that fine-tuned PEGASUS-large outperforms the other models, including fine-tuned LLaMA-3-8B and GPT-3.5-turbo, across most metrics. We also demonstrate that ChatGPT can generate creative titles for papers. Our observations suggest that AI-generated paper titles are generally accurate and appropriate.

The exponential growth of scientific publications in recent years has posed a significant challenge in effective and efficient categorization. This paper introduces a novel approach that combines instance-based learning and ensemble learning techniques for classifying scientific papers into relevant research fields. Working with a classification system with a group of research fields, first a number of typical seed papers are allocated to each of the fields manually. Then for each paper that needs to be classified, we compare it with all the seed papers in every field. Contents and citations are considered separately. An ensemble-based method is then employed to make the final decision. Experimenting with the datasets from DBLP, our experimental results demonstrate that the proposed classification method is effective and efficient in categorizing papers into various research areas. We also find that both content and citation features are useful for the classification of scientific papers.

We participated in the KDD CUP 2024 paper source tracing competition and achieved the 3rd place. This competition tasked participants with identifying the reference sources (i.e., ref-sources, as referred to by the organizers of the competition) of given academic papers. Unlike most teams that addressed this challenge by fine-tuning pre-trained neural language models such as BERT or ChatGLM, our primary approach utilized closed-source large language models (LLMs). With recent advancements in LLM technology, closed-source LLMs have demonstrated the capability to tackle complex reasoning tasks in zero-shot or few-shot scenarios. Consequently, in the absence of GPUs, we employed closed-source LLMs to directly generate predicted reference sources from the provided papers. We further refined these predictions through ensemble learning. Notably, our method was the only one among the award-winning approaches that did not require the use of GPUs for model training. Code available at https://github.com/Cklwanfifa/KDDCUP2024-PST.

Generative Artificial Intelligence (AI) models may carry societal transformation to an extent demanding a delicate balance between opportunity and risk. This manuscript is the first of a series of White Papers informing the development of IEEE-SA's p3995: `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence (AI) Models', Chair: Marina Cort\^{e}s (https://standards.ieee.org/ieee/3395/11378/). In this first horizon-scanning we identify key attention areas for standards activities in AI. We examine different principles for regulatory efforts, and review notions of accountability, privacy, data rights and mis-use. As a safeguards standard we devote significant attention to the stability of global infrastructures and consider a possible overdependence on cloud computing that may result from densely coupled AI components. We review the recent cascade-failure-like Crowdstrike event in July 2024, as an illustration of potential impacts on critical infrastructures from AI-induced incidents in the (near) future. It is the first of a set of articles intended as White Papers informing the audience on the standard development. Upcoming articles will focus on regulatory initiatives, technology evolution and the role of AI in specific domains., Comment: This is an interim version of our p3395 working group White Paper. We will update this version, until publication by the Institute of Electrical and Electronics Engineers, Standards Association (IEEE-SA), Sponsor Committee - Artificial Intelligence Standards Committee (C/AISC); https://standards.ieee.org/ieee/3395/11378/

The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive guidelines for the development and oversight of Artificial Intelligence (AI) and Machine Learning (ML) systems, with significant implications for Graph Neural Networks (GNNs). This paper addresses the unique challenges posed by the AI Act for GNNs, which operate on complex graph-structured data. The legislation's requirements for data management, data governance, robustness, human oversight, and privacy necessitate tailored strategies for GNNs. Our study explores the impact of these requirements on GNN training and proposes methods to ensure compliance. We provide an in-depth analysis of bias, robustness, explainability, and privacy in the context of GNNs, highlighting the need for fair sampling strategies and effective interpretability techniques. Our contributions fill the research gap by offering specific guidance for GNNs under the new legislative framework and identifying open questions and future research directions.

Peer review is a critical process for ensuring the integrity of published scientific research. Confidence in this process is predicated on the assumption that experts in the relevant domain give careful consideration to the merits of manuscripts which are submitted for publication. With the recent rapid advancements in the linguistic capabilities of large language models (LLMs), a new potential risk to the peer review process is that negligent reviewers will rely on LLMs to perform the often time consuming process of reviewing a paper. In this study, we investigate the ability of existing AI text detection algorithms to distinguish between peer reviews written by humans and different state-of-the-art LLMs. Our analysis shows that existing approaches fail to identify many GPT-4o written reviews without also producing a high number of false positive classifications. To address this deficiency, we propose a new detection approach which surpasses existing methods in the identification of GPT-4o written peer reviews at low levels of false positive classifications. Our work reveals the difficulty of accurately identifying AI-generated text at the individual review level, highlighting the urgent need for new tools and methods to detect this type of unethical application of generative AI.

This study investigates efficient deduplication techniques for a large NLP dataset of economic research paper titles. We explore various pairing methods alongside established distance measures (Levenshtein distance, cosine similarity) and a sBERT model for semantic evaluation. Our findings suggest a potentially low prevalence of duplicates based on the observed semantic similarity across different methods. Further exploration with a human-annotated ground truth set is completed for a more conclusive assessment. The result supports findings from the NLP, LLM based distance metrics., Comment: 6 pages, 1 figure

Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited. This study evaluates two LLMs, Mistral-7B and Llama3-70B, using structured electronic health record data on three diagnosis tasks. We examined three current methods of extracting LLM probability estimations and revealed their limitations. We aim to highlight the need for improved techniques in LLM confidence estimation., Comment: Accepted to GenAI4Health Workshop at NeurIPS 2024

We present an outline of the first large language model (LLM) based chatbot application in the context of patient-reported outcome measures (PROMs) for diabetic retinopathy. By utilizing the capabilities of current LLMs, we enable patients to provide feedback about their quality of life and treatment progress via an interactive application. The proposed framework offers significant advantages over the current approach, which encompasses only qualitative collection of survey data or a static survey with limited answer options. Using the PROBot LLM-PROM application, patients will be asked tailored questions about their individual challenges, and can give more detailed feedback on the progress of their treatment. Based on this input, we will use machine learning to infer conventional PROM scores, which can be used by clinicians to evaluate the treatment status. The goal of the application is to improve adherence to the healthcare system and treatments, and thus ultimately reduce cases of subsequent vision impairment. The approach needs to be further validated using a survey and a clinical study.

This paper presents CodeRefine, a novel framework for automatically transforming research paper methodologies into functional code using Large Language Models (LLMs). Our multi-step approach first extracts and summarizes key text chunks from papers, analyzes their code relevance, and creates a knowledge graph using a predefined ontology. Code is then generated from this structured representation and enhanced through a proposed retrospective retrieval-augmented generation approach. CodeRefine addresses the challenge of bridging theoretical research and practical implementation, offering a more accurate alternative to LLM zero-shot prompting. Evaluations on diverse scientific papers demonstrate CodeRefine's ability to improve code implementation from the paper, potentially accelerating the adoption of cutting-edge algorithms in real-world applications.

Machine learning is widely utilized across various industries. Identifying the appropriate machine learning models and datasets for specific tasks is crucial for the effective industrial application of machine learning. However, this requires expertise in both machine learning and the relevant domain, leading to a high learning cost. Therefore, research focused on extracting combinations of tasks, machine learning models, and datasets from academic papers is critically important, as it can facilitate the automatic recommendation of suitable methods. Conventional information extraction methods from academic papers have been limited to identifying machine learning models and other entities as named entities. To address this issue, this study proposes a methodology extracting tasks, machine learning methods, and dataset names from scientific papers and analyzing the relationships between these information by using LLM, embedding model, and network clustering. The proposed method's expression extraction performance, when using Llama3, achieves an F-score exceeding 0.8 across various categories, confirming its practical utility. Benchmarking results on financial domain papers have demonstrated the effectiveness of this method, providing insights into the use of the latest datasets, including those related to ESG (Environmental, Social, and Governance) data., Comment: 10 pages, 8 figures

Teaching Computer Science (CS) by having students write programs by hand on paper has key pedagogical advantages: It allows focused learning and requires careful thinking compared to the use of Integrated Development Environments (IDEs) with intelligent support tools or "just trying things out". The familiar environment of pens and paper also lessens the cognitive load of students with no prior experience with computers, for whom the mere basic usage of computers can be intimidating. Finally, this teaching approach opens learning opportunities to students with limited access to computers. However, a key obstacle is the current lack of teaching methods and support software for working with and running handwritten programs. Optical character recognition (OCR) of handwritten code is challenging: Minor OCR errors, perhaps due to varied handwriting styles, easily make code not run, and recognizing indentation is crucial for languages like Python but is difficult to do due to inconsistent horizontal spacing in handwriting. Our approach integrates two innovative methods. The first combines OCR with an indentation recognition module and a language model designed for post-OCR error correction without introducing hallucinations. This method, to our knowledge, surpasses all existing systems in handwritten code recognition. It reduces error from 30\% in the state of the art to 5\% with minimal hallucination of logical fixes to student programs. The second method leverages a multimodal language model to recognize handwritten programs in an end-to-end fashion. We hope this contribution can stimulate further pedagogical research and contribute to the goal of making CS education universally accessible. We release a dataset of handwritten programs and code to support future research at https://github.com/mdoumbouya/codeocr

Seeking answers to questions within long scientific research articles is a crucial area of study that aids readers in quickly addressing their inquiries. However, existing question-answering (QA) datasets based on scientific papers are limited in scale and focus solely on textual content. To address this limitation, we introduce SPIQA (Scientific Paper Image Question Answering), the first large-scale QA dataset specifically designed to interpret complex figures and tables within the context of scientific research articles across various domains of computer science. Leveraging the breadth of expertise and ability of multimodal large language models (MLLMs) to understand figures, we employ automatic and manual curation to create the dataset. We craft an information-seeking task involving multiple images that cover a wide variety of plots, charts, tables, schematic diagrams, and result visualizations. SPIQA comprises 270K questions divided into training, validation, and three different evaluation splits. Through extensive experiments with 12 prominent foundational models, we evaluate the ability of current multimodal systems to comprehend the nuanced aspects of research articles. Additionally, we propose a Chain-of-Thought (CoT) evaluation strategy with in-context retrieval that allows fine-grained, step-by-step assessment and improves model performance. We further explore the upper bounds of performance enhancement with additional textual information, highlighting its promising potential for future research and the dataset's impact on revolutionizing how we interact with scientific literature., Comment: preprint

Occlusal contacts are the locations at which the occluding surfaces of the maxilla and the mandible posterior teeth meet. Occlusal contact detection is a vital tool for restoring the loss of masticatory function and is a mandatory assessment in the field of dentistry, with particular importance in prosthodontics and restorative dentistry. The most common method for occlusal contact detection is articulating paper. However, this method can indicate significant medically false positive and medically false negative contact areas, leaving the identification of true occlusal indications to clinicians. To address this, we propose a multiclass Vision Transformer and Fully Convolutional Network ensemble semantic segmentation model with a combination hierarchical loss function, which we name as Hierarchical Fully Convolutional Branch Transformer (H-FCBFormer). We also propose a method of generating medically true positive semantic segmentation masks derived from expert annotated articulating paper masks and gold standard masks. The proposed model outperforms other machine learning methods evaluated at detecting medically true positive contacts and performs better than dentists in terms of accurately identifying object-wise occlusal contact areas while taking significantly less time to identify them. Code is available at https://github.com/Banksylel/H-FCBFormer., Comment: 15 pages, 5 figures, 2 tables, 5 equations, peer reviewed and accepted to Medical Imaging Understanding and Analysis (MIUA 2024)

An emerging area of research in situated and multimodal interactive conversations (SIMMC) includes interactions in scientific papers. Since scientific papers are primarily composed of text, equations, figures, and tables, SIMMC methods must be developed specifically for each component to support the depth of inquiry and interactions required by research scientists. This work introduces Conversational Papers (cPAPERS), a dataset of conversational question-answer pairs from reviews of academic papers grounded in these paper components and their associated references from scientific documents available on arXiv. We present a data collection strategy to collect these question-answer pairs from OpenReview and associate them with contextual information from LaTeX source files. Additionally, we present a series of baseline approaches utilizing Large Language Models (LLMs) in both zero-shot and fine-tuned configurations to address the cPAPERS dataset., Comment: 14 pages, 1 figure

We identify two major issues in the SoftDist paper (Xia et al.): (1) the failure to run all steps of different baselines on the same hardware environment, and (2) the use of inconsistent time measurements when comparing to other baselines. These issues lead to flawed conclusions. When all steps are executed in the same hardware environment, the primary claim made in SoftDist is no longer supported., Comment: comment on arXiv:2406.03503, 4 pages, 1 figure and 1 table

With the rapid growth of scholarly archives, researchers subscribe to "paper alert" systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users' research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers., Comment: Accepted to CHI 2024

The cross-prompt injection attack (XPIA) is an effective technique that can be used for data exfiltration, and that has seen increasing use. In this attack, the attacker injects a malicious instruction into third party data which an LLM is likely to consume when assisting a user, who is the victim. XPIA is often used as a means for data exfiltration, and the estimated cost of the average data breach for a business is nearly $4.5 million, which includes breaches such as compromised enterprise credentials. With the rise of gradient-based attacks such as the GCG suffix attack, the odds of an XPIA occurring which uses a GCG suffix are worryingly high. As part of my work in Microsoft's AI Red Team, I demonstrated a viable attack model using a GCG suffix paired with an injection in a simulated XPIA scenario. The results indicate that the presence of a GCG suffix can increase the odds of successful data exfiltration by nearly 20%, with some caveats., Comment: 8 pages, 8 figures. Conducted as part of employment at Microsoft Corporation

The iterative character of work in machine learning (ML) and artificial intelligence (AI) and reliance on comparisons against benchmark datasets emphasize the importance of reproducibility in that literature. Yet, resource constraints and inadequate documentation can make running replications particularly challenging. Our work explores the potential of using downstream citation contexts as a signal of reproducibility. We introduce a sentiment analysis framework applied to citation contexts from papers involved in Machine Learning Reproducibility Challenges in order to interpret the positive or negative outcomes of reproduction attempts. Our contributions include training classifiers for reproducibility-related contexts and sentiment analysis, and exploring correlations between citation context sentiment and reproducibility scores. Study data, software, and an artifact appendix are publicly available at https://github.com/lamps-lab/ccair-ai-reproducibility ., Comment: 9 pages, 4 figures

As AI promises to accelerate scientific discovery, it remains unclear whether fully AI-driven research is possible and whether it can adhere to key scientific values, such as transparency, traceability and verifiability. Mimicking human scientific practices, we built data-to-paper, an automation platform that guides interacting LLM agents through a complete stepwise research process, while programmatically back-tracing information flow and allowing human oversight and interactions. In autopilot mode, provided with annotated data alone, data-to-paper raised hypotheses, designed research plans, wrote and debugged analysis codes, generated and interpreted results, and created complete and information-traceable research papers. Even though research novelty was relatively limited, the process demonstrated autonomous generation of de novo quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts which recapitulate peer-reviewed publications without major errors in about 80-90%, yet as goal complexity increases, human co-piloting becomes critical for assuring accuracy. Beyond the process itself, created manuscripts too are inherently verifiable, as information-tracing allows to programmatically chain results, methods and data. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency and verifiability.

Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge is accessible, verifiable, and built upon over time. Recently, there has been immense speculation about how many people are using large language models (LLMs) like ChatGPT in their academic writing, and to what extent this tool might have an effect on global scientific practices. However, we lack a precise measure of the proportion of academic writing substantially modified or produced by LLMs. To address this gap, we conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time. Our statistical estimation operates on the corpus level and is more robust than inference on individual instances. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers (up to 17.5%). In comparison, Mathematics papers and the Nature portfolio showed the least LLM modification (up to 6.3%). Moreover, at an aggregate level, our analysis reveals that higher levels of LLM-modification are associated with papers whose first authors post preprints more frequently, papers in more crowded research areas, and papers of shorter lengths. Our findings suggests that LLMs are being broadly used in scientific writings.

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers. We compile a dataset called \textbf{\textit{LimGen}}, encompassing 4068 research papers and their associated limitations from the ACL anthology. We investigate several approaches to harness large language models (LLMs) for producing suggestive limitations, by thoroughly examining the related challenges, practical insights, and potential opportunities. Our LimGen dataset and code can be accessed at \url{https://github.com/arbmf/LimGen}., Comment: Accepted at ECML-PKDD 2024

This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion. The list of all accepted papers is available on the workshop website.

Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N=21) and authors of selected papers (N=12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.

This paper discusses the effectiveness of leveraging Chatbot: Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research papers for effective writing of scientific literature surveys. The study selected the \textit{Application of Artificial Intelligence in Breast Cancer Treatment} as the research topic. Research papers related to this topic were collected from three major publication databases Google Scholar, Pubmed, and Scopus. ChatGPT models were used to identify the category, scope, and relevant information from the research papers for automatic identification of relevant papers related to Breast Cancer Treatment (BCT), organization of papers according to scope, and identification of key information for survey paper writing. Evaluations performed using ground truth data annotated using subject experts reveal, that GPT-4 achieves 77.3\% accuracy in identifying the research paper categories and 50\% of the papers were correctly identified by GPT-4 for their scopes. Further, the results demonstrate that GPT-4 can generate reasons for its decisions with an average of 27\% new words, and 67\% of the reasons given by the model were completely agreeable to the subject experts.

Failure Mode, Effects and Criticality Analysis (FMECA) is one of the safety analysis methods recommended by most of the international standards. The classical FMECA is made in a form of a table filled in either manually or by using safety analysis tools. In both cases, the design engineers have to choose the trade-offs between safety and other development constraints. In the case of complex cyber-physical systems (CPS) with thousands of specified constraints, this may lead to severe problems and significantly impact the overall criticality of CPS. In this paper, we propose to adopt optimization techniques to automate the decision making process conducted after FMECA of CPS. We describe a multi-agent based optimization method which extends classical FMECA for offering optimal solutions in terms of criticality and development constraints of CPS., Comment: 13 pages, 2 figures, 1 table, "2nd International Workshop on Emerging Ideas and Trends in Engineering of Cyber-Physical Systems, part of Cyber-Physical Systems Week, April 2015, Seattle, USA"

This is a summary paper of a use case of a Robotdog dedicated to guide visually impaired people in complex environment like a smart intersection. In such scenarios, the Robotdog has to autonomously decide whether it is safe to cross the intersection or not in order to further guide the human. We leverage data sharing and collaboration between the Robotdog and other autonomous systems operating in the same environment. We propose a system architecture for autonomous systems through a separation of a collaborative decision layer, to enable collective decision making processes, where data about the environment, relevant to the Robotdog decision, together with evidences for trustworthiness about other systems and the environment are shared.

Large Language Model (LLM) -in-the-loop applications have been shown to effectively interpret the human user's commands, make plans, and operate external tools/systems accordingly. Still, the operation scope of the LLM agent is limited to passively following the user, requiring the user to frame his/her needs with regard to the underlying tools/systems. We note that the potential of an LLM-Agent User Interface (LAUI) is much greater. A user mostly ignorant to the underlying tools/systems should be able to work with a LAUI to discover an emergent workflow. Contrary to the conventional way of designing an explorable GUI to teach the user a predefined set of ways to use the system, in the ideal LAUI, the LLM agent is initialized to be proficient with the system, proactively studies the user and his/her needs, and proposes new interaction schemes to the user. To illustrate LAUI, we present Flute X GPT, a concrete example using an LLM agent, a prompt manager, and a flute-tutoring multi-modal software-hardware system to facilitate the complex, real-time user experience of learning to play the flute.

Over the course of the recent decade, tremendous progress has been made in the areas of machine learning and natural language processing, which opened up vast areas of potential application use cases, including hiring and human resource management. We review the use cases for text analytics in the realm of human resources/personnel management, including actually realized as well as potential but not yet implemented ones, and we analyze the opportunities and risks of these., Comment: 10 pages, 2 figures, 1 table

Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs). A considerable amount of research exists proposing more effective jailbreak attacks, including the recent Greedy Coordinate Gradient (GCG) attack, jailbreak template-based attacks such as using "Do-Anything-Now" (DAN), and multilingual jailbreak. In contrast, the defensive side has been relatively less explored. This paper proposes a lightweight yet practical defense called SELFDEFEND, which can defend against all existing jailbreak attacks with minimal delay for jailbreak prompts and negligible delay for normal user prompts. Our key insight is that regardless of the kind of jailbreak strategies employed, they eventually need to include a harmful prompt (e.g., "how to make a bomb") in the prompt sent to LLMs, and we found that existing LLMs can effectively recognize such harmful prompts that violate their safety policies. Based on this insight, we design a shadow stack that concurrently checks whether a harmful prompt exists in the user prompt and triggers a checkpoint in the normal stack once a token of "No" or a harmful prompt is output. The latter could also generate an explainable LLM response to adversarial prompts. We demonstrate our idea of SELFDEFEND works in various jailbreak scenarios through manual analysis in GPT-3.5/4. We also list three future directions to further enhance SELFDEFEND., Comment: Fixed the bibliography reference issue in our LLM jailbreak defense vision paper submitted on 24 Feb 2024

As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous. In this paper, we develop a method to automatically assign survey papers to a taxonomy. We collect the metadata of 144 LLM survey papers and explore three paradigms to classify papers within the taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models in two paradigms; pre-trained language models' fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our model surpasses an average human recognition level and that fine-tuning LLMs using weak labels generated by a smaller model, such as the GCN in this study, can be more effective than using ground-truth labels, revealing the potential of weak-to-strong generalization in the taxonomy classification task., Comment: TL;DR: We collected metadata about LLM surveys and developed a method for categorizing them into a taxonomy, indicating the superiority of graph representation learning over language models and revealing the efficacy of fine-tuning using weak labels

A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers. In such collusion rings, reviewers who have also submitted their own papers to the conference work together to manipulate the conference's paper assignment, with the aim of being assigned to review each other's papers. The most straightforward way that colluding reviewers can manipulate the paper assignment is by indicating their interest in each other's papers through strategic paper bidding. One potential approach to solve this important problem would be to detect the colluding reviewers from their manipulated bids, after which the conference can take appropriate action. While prior work has developed effective techniques to detect other kinds of fraud, no research has yet established that detecting collusion rings is even possible. In this work, we tackle the question of whether it is feasible to detect collusion rings from the paper bidding. To answer this question, we conduct empirical analysis of two realistic conference bidding datasets, including evaluations of existing algorithms for fraud detection in other applications. We find that collusion rings can achieve considerable success at manipulating the paper assignment while remaining hidden from detection: for example, in one dataset, undetected colluders are able to achieve assignment to up to 30% of the papers authored by other colluders. In addition, when 10 colluders bid on all of each other's papers, no detection algorithm outputs a group of reviewers with more than 31% overlap with the true colluders. These results suggest that collusion cannot be effectively detected from the bidding using popular existing tools, demonstrating the need to develop more complex detection algorithms as well as those that leverage additional metadata (e.g., reviewer-paper text-similarity scores).

Recent advancements in large foundation models have remarkably enhanced our understanding of sensory information in open-world environments. In leveraging the power of foundation models, it is crucial for AI research to pivot away from excessive reductionism and toward an emphasis on systems that function as cohesive wholes. Specifically, we emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. The emerging field of Agent AI spans a wide range of existing embodied and agent-based multimodal interactions, including robotics, gaming, and healthcare systems, etc. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model. On top of this idea, we discuss how agent AI exhibits remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Furthermore, we discuss the potential of Agent AI from an interdisciplinary perspective, underscoring AI cognition and consciousness within scientific discourse. We believe that those discussions serve as a basis for future research directions and encourage broader societal engagement., Comment: 22 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:2401.03568

This paper presents a method to leverage arbitrary neural network architecture for control variates. Control variates are crucial in reducing the variance of Monte Carlo integration, but they hinge on finding a function that both correlates with the integrand and has a known analytical integral. Traditional approaches rely on heuristics to choose this function, which might not be expressive enough to correlate well with the integrand. Recent research alleviates this issue by modeling the integrands with a learnable parametric model, such as a neural network. However, the challenge remains in creating an expressive parametric model with a known analytical integral. This paper proposes a novel approach to construct learnable parametric control variates functions from arbitrary neural network architectures. Instead of using a network to approximate the integrand directly, we employ the network to approximate the anti-derivative of the integrand. This allows us to use automatic differentiation to create a function whose integration can be constructed by the antiderivative network. We apply our method to solve partial differential equations using the Walk-on-sphere algorithm. Our results indicate that this approach is unbiased and uses various network architectures to achieve lower variance than other control variate methods.

This paper introduces a simple extension of mixup (Zhang et al., 2018) data augmentation to enhance generalization in visual recognition tasks. Unlike the vanilla mixup method, which blends entire images, our approach focuses on combining regions from multiple images., Comment: Published as a Tiny Paper at ICLR 2024

Contrastive Language-Image Pre-training (CLIP) has been shown to improve zero-shot generalization capabilities of language and vision models. In this paper, we extend CLIP for efficient knowledge distillation, by utilizing embeddings as teachers. Typical knowledge distillation frameworks require running forward passes through a teacher model, which is often prohibitive in the case of billion or trillion parameter teachers. In these cases, using only the embeddings of the teacher models to guide the distillation can yield significant computational savings. Our preliminary findings show that CLIP-based knowledge distillation with embeddings can outperform full scale knowledge distillation using $9\times$ less memory and $8\times$ less training time. Code available at: https://github.com/lnairGT/CLIP-Distillation/, Comment: Short paper - 5 pages; 5 figures

As the boundaries of human computer interaction expand, Generative AI emerges as a key driver in reshaping user interfaces, introducing new possibilities for personalized, multimodal and cross-platform interactions. This integration reflects a growing demand for more adaptive and intuitive user interfaces that can accommodate diverse input types such as text, voice and video, and deliver seamless experiences across devices. This paper explores the integration of generative AI in modern user interfaces, examining historical developments and focusing on multimodal interaction, cross-platform adaptability and dynamic personalization. A central theme is the interface dilemma, which addresses the challenge of designing effective interactions for multimodal large language models, assessing the trade-offs between graphical, voice-based and immersive interfaces. The paper further evaluates lightweight frameworks tailored for mobile platforms, spotlighting the role of mobile hardware in enabling scalable multimodal AI. Technical and ethical challenges, including context retention, privacy concerns and balancing cloud and on-device processing are thoroughly examined. Finally, the paper outlines future directions such as emotionally adaptive interfaces, predictive AI driven user interfaces and real-time collaborative systems, underscoring generative AI's potential to redefine adaptive user-centric interfaces across platforms., Comment: 13 pages, 4 figures

In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain. Systematic tests are conducted on English and Chinese legal cases, and the results are analyzed in depth. Through systematic testing of legal cases from common law systems and China, this paper explores the strengths and weaknesses of LLMs in understanding and applying legal texts, reasoning through legal issues, and predicting judgments. The experimental results highlight both the potential and limitations of LLMs in legal applications, particularly in terms of challenges related to the interpretation of legal language and the accuracy of legal reasoning. Finally, the paper provides a comprehensive analysis of the advantages and disadvantages of various types of models, offering valuable insights and references for the future application of AI in the legal field.

Climate change poses an urgent global threat, needing the rapid identification and deployment of innovative solutions. We hypothesise that many of these solutions already exist within scientific literature but remain underutilised. To address this gap, this study employs a curated dataset sourced from OpenAlex, a comprehensive repository of scientific papers. Utilising Large Language Models (LLMs), such as GPT4-o from OpenAI, we evaluate title-abstract pairs from scientific papers on seven dimensions, covering climate change mitigation potential, stage of technological development, and readiness for deployment. The outputs of the language models are then compared with human evaluations to assess their effectiveness in identifying promising yet overlooked climate innovations. Our findings suggest that these LLM-based models can effectively augment human expertise, uncovering climate solutions that are potentially impactful but with far greater speed, throughput and consistency. Here, we focused on UK-based solutions, but the workflow is region-agnostic. This work contributes to the discovery of neglected innovations in scientific literature and demonstrates the potential of AI in enhancing climate action strategies., Comment: 10 pages. Accepted in the LatinX in AI workshop at NeurIPS 2024

In 2020, we introduced a deep reinforcement learning method capable of generating superhuman chip layouts, which we then published in Nature and open-sourced on GitHub. AlphaChip has inspired an explosion of work on AI for chip design, and has been deployed in state-of-the-art chips across Alphabet and extended by external chipmakers. Even so, a non-peer-reviewed invited paper at ISPD 2023 questioned its performance claims, despite failing to run our method as described in Nature. For example, it did not pre-train the RL method (removing its ability to learn from prior experience), used substantially fewer compute resources (20x fewer RL experience collectors and half as many GPUs), did not train to convergence (standard practice in machine learning), and evaluated on test cases that are not representative of modern chips. Recently, Igor Markov published a meta-analysis of three papers: our peer-reviewed Nature paper, the non-peer-reviewed ISPD paper, and Markov's own unpublished paper (though he does not disclose that he co-authored it). Although AlphaChip has already achieved widespread adoption and impact, we publish this response to ensure that no one is wrongly discouraged from innovating in this impactful area.

Multimodal image fusion (MMIF) aims to integrate information from different modalities to obtain a comprehensive image, aiding downstream tasks. However, existing methods tend to prioritize natural image fusion and focus on information complementary and network training strategies. They ignore the essential distinction between natural and medical image fusion and the influence of underlying components. This paper dissects the significant differences between the two tasks regarding fusion goals, statistical properties, and data distribution. Based on this, we rethink the suitability of the normalization strategy and convolutional kernels for end-to-end MMIF.Specifically, this paper proposes a mixture of instance normalization and group normalization to preserve sample independence and reinforce intrinsic feature correlation.This strategy promotes the potential of enriching feature maps, thus boosting fusion performance. To this end, we further introduce the large kernel convolution, effectively expanding receptive fields and enhancing the preservation of image detail. Moreover, the proposed multipath adaptive fusion module recalibrates the decoder input with features of various scales and receptive fields, ensuring the transmission of crucial information. Extensive experiments demonstrate that our method exhibits state-of-the-art performance in multiple fusion tasks and significantly improves downstream applications. The code is available at https://github.com/HeDan-11/LKC-FUNet.

The hallucination problem in multimodal large language models (MLLMs) remains a common issue. Although image tokens occupy a majority of the input sequence of MLLMs, there is limited research to explore the relationship between image tokens and hallucinations. In this paper, we analyze the distribution of attention scores for image tokens across each layer and head of the model, revealing an intriguing and common phenomenon: most hallucinations are closely linked to the pattern of attention sinks in the self-attention matrix of image tokens, where shallow layers exhibit dense attention sinks and deeper layers show sparse attention sinks. We further analyze the attention heads of different layers and find that heads with high-density attention sink in the image part play a positive role in alleviating hallucinations. In this paper, we propose a training-free method named \textcolor{red}{\textbf{E}}nhancing \textcolor{red}{\textbf{A}}ttention \textcolor{red}{\textbf{H}}eads (EAH), an approach designed to enhance the convergence of image tokens attention sinks in the shallow layers. EAH identifies the attention head that shows the vision sink in a shallow layer and extracts its attention matrix. This attention map is then broadcast to other heads in the layer, thereby strengthening the layer to pay more attention to the image itself. With extensive experiments, EAH shows significant hallucination-mitigating performance on different MLLMs and metrics, proving its effectiveness and generality.

Avatar modelling has broad applications in human animation and virtual try-ons. Recent advancements in this field have focused on high-quality and comprehensive human reconstruction but often overlook the separation of clothing from the body. To bridge this gap, this paper introduces GGAvatar (Garment-separated 3D Gaussian Splatting Avatar), which relies on monocular videos. Through advanced parameterized templates and unique phased training, this model effectively achieves decoupled, editable, and realistic reconstruction of clothed humans. Comparative evaluations with other costly models confirm GGAvatar's superior quality and efficiency in modelling both clothed humans and separable garments. The paper also showcases applications in clothing editing, as illustrated in Figure 1, highlighting the model's benefits and the advantages of effective disentanglement. The code is available at https://github.com/J-X-Chen/GGAvatar/., Comment: MMAsia'24 Accepted

In this paper, we explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance automated design and software development in the automotive industry. We present two case studies: a standardization compliance chatbot and a design copilot, both utilizing RAG to provide accurate, context-aware responses. We evaluate four LLMs-GPT-4o, LLAMA3, Mistral, and Mixtral -- comparing their answering accuracy and execution time. Our results demonstrate that while GPT-4 offers superior performance, LLAMA3 and Mistral also show promising capabilities for local deployment, addressing data privacy concerns in automotive applications. This study highlights the potential of RAG-augmented LLMs in improving design workflows and compliance in automotive engineering., Comment: Conference paper accepted in IEEE FLLM 2024

We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most important information from bidders to maximize efficiency. The SOTA ML-based algorithms elicit bidders' preferences via value queries (i.e., "What is your value for the bundle $\{A,B\}$?"). However, the most popular iterative combinatorial auction in practice elicits information via more practical \emph{demand queries} (i.e., "At prices $p$, what is your most preferred bundle of items?"). In this paper, we examine the advantages of value and demand queries from both an auction design and an ML perspective. We propose a novel ML algorithm that provably integrates the full information from both query types. As suggested by our theoretical analysis, our experimental results verify that combining demand and value queries results in significantly better learning performance. Building on these insights, we present MLHCA, the most efficient ICA ever designed. MLHCA substantially outperforms the previous SOTA in realistic auction settings, delivering large efficiency gains. Compared to the previous SOTA, MLHCA reduces efficiency loss by up to a factor of 10, and in the most challenging and realistic domain, MLHCA outperforms the previous SOTA using 30% fewer queries. Thus, MLHCA achieves efficiency improvements that translate to welfare gains of hundreds of millions of USD, while also reducing the cognitive load on the bidders, establishing a new benchmark both for practicability and for economic impact.