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Aligning diffusion models with downstream objectives is essential for their practical applications. However, standard alignment methods often struggle with step generalization when directly applied to few-step diffusion models, leading to inconsistent performance across different denoising step scenarios. To address this, we introduce Stepwise Diffusion Policy Optimization (SDPO), a novel alignment method tailored for few-step diffusion models. Unlike prior approaches that rely on a single sparse reward from only the final step of each denoising trajectory for trajectory-level optimization, SDPO incorporates dense reward feedback at every intermediate step. By learning the differences in dense rewards between paired samples, SDPO facilitates stepwise optimization of few-step diffusion models, ensuring consistent alignment across all denoising steps. To promote stable and efficient training, SDPO introduces an online reinforcement learning framework featuring several novel strategies designed to effectively exploit the stepwise granularity of dense rewards. Experimental results demonstrate that SDPO consistently outperforms prior methods in reward-based alignment across diverse step configurations, underscoring its robust step generalization capabilities. Code is avaliable at https://github.com/ZiyiZhang27/sdpo.

With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to revolutionize fields from healthcare to finance, NLP techniques such as tokenization, text classification, and entity recognition are essential for processing and understanding human language. This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models. Additionally, it highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness. By addressing key aspects of data processing and model fine-tuning, this work aims to provide insights into deploying effective and ethically sound AI solutions., Comment: 255 pages

Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity $\mathcal{O}(1/(1-\gamma)^2)$. We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity $\mathcal{O}(1/(1-\gamma))$. Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization., Comment: Accepted by NeurIPS 2024

Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filtering techniques, and pattern recognition. By integrating frameworks like the Discrete Fourier Transform (DFT), Z-Transform, and Fourier Transform methods, we enable robust data manipulation and feature extraction essential for AI-driven tasks. Using Python, we implement algorithms that optimize real-time data processing, forming a foundation for scalable, high-performance solutions in computer vision. This work illustrates the potential of ML and DL to advance DSP and DIP methodologies, contributing to artificial intelligence, automated feature extraction, and applications across diverse domains., Comment: 293 pages

This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and practical application, focusing on Python as the primary programming language for implementing key algorithms and data structures. The book covers a wide range of topics, including basic and advanced Python programming, fundamental mathematical operations, matrix operations, linear algebra, and optimization techniques crucial for training ML and DL models. Advanced subjects like neural networks, optimization algorithms, and frequency domain methods are also explored, along with real-world applications of large language models (LLMs) and artificial intelligence (AI) in big data management. Designed for both beginners and advanced learners, the book emphasizes the critical role of mathematical principles in developing scalable AI solutions. Practical examples and Python code are provided throughout, ensuring readers gain hands-on experience in applying theoretical knowledge to solve complex problems in ML, DL, and big data analytics., Comment: 298 pages

This book offers an in-depth exploration of object detection and semantic segmentation, combining theoretical foundations with practical applications. It covers state-of-the-art advancements in machine learning and deep learning, with a focus on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches like DETR. The book also delves into the integration of artificial intelligence (AI) techniques and large language models for enhanced object detection in complex environments. A thorough discussion of big data analysis is presented, highlighting the importance of data processing, model optimization, and performance evaluation metrics. By bridging the gap between traditional methods and modern deep learning frameworks, this book serves as a comprehensive guide for researchers, data scientists, and engineers aiming to leverage AI-driven methodologies in large-scale object detection tasks., Comment: 167 pages

This article provides a detailed exploration of blockchain technology and its applications across various fields. It begins with an introduction to cryptography fundamentals, including symmetric and asymmetric encryption, and their roles in ensuring security and trust within blockchain systems. The article then delves into the structure and mechanics of Bitcoin and Ethereum, covering topics such as proof-of-work, proof-of-stake, and smart contracts. Additionally, it highlights practical applications of blockchain in industries like decentralized finance (DeFi), supply chain management, and identity authentication. The discussion also extends to consensus mechanisms and scalability challenges in blockchain, offering insights into emerging technologies like Layer 2 solutions and cross-chain interoperability. The article concludes by addressing the current state of academic research on blockchain and its potential future developments., Comment: This book contains 241 pages and 5 figures

This manuscript presents a comprehensive guide to Automated Machine Learning (AutoML), covering fundamental principles, practical implementations, and future trends. The paper is structured to assist both beginners and experienced practitioners, with detailed discussions on popular AutoML tools such as TPOT, AutoGluon, and Auto-Keras. It also addresses emerging topics like Neural Architecture Search (NAS) and AutoML's applications in deep learning. We believe this work will contribute to ongoing research and development in the field of AI and machine learning., Comment: This book contains 170 pages and 5 figures

Hallucinations in Large Language Models (LLMs) remain a major obstacle, particularly in high-stakes applications where factual accuracy is critical. While representation editing and reading methods have made strides in reducing hallucinations, their heavy reliance on specialised tools and training on in-domain samples, makes them difficult to scale and prone to overfitting. This limits their accuracy gains and generalizability to diverse datasets. This paper presents a lightweight method, Norm Voting (NoVo), which harnesses the untapped potential of attention head norms to dramatically enhance factual accuracy in zero-shot multiple-choice questions (MCQs). NoVo begins by automatically selecting truth-correlated head norms with an efficient, inference-only algorithm using only 30 random samples, allowing NoVo to effortlessly scale to diverse datasets. Afterwards, selected head norms are employed in a simple voting algorithm, which yields significant gains in prediction accuracy. On TruthfulQA MC1, NoVo surpasses the current state-of-the-art and all previous methods by an astounding margin -- at least 19 accuracy points. NoVo demonstrates exceptional generalization to 20 diverse datasets, with significant gains in over 90\% of them, far exceeding all current representation editing and reading methods. NoVo also reveals promising gains to finetuning strategies and building textual adversarial defence. NoVo's effectiveness with head norms opens new frontiers in LLM interpretability, robustness and reliability.

This book presents a comprehensive exploration of GPGPU (General Purpose Graphics Processing Unit) and its applications in deep learning and machine learning. It focuses on how parallel computing, particularly through the use of CUDA (Compute Unified Device Architecture), can unlock unprecedented computational power for complex tasks. The book provides detailed discussions on CPU and GPU architectures, data flow in deep learning, and advanced GPU features like streams, concurrency, and dynamic parallelism. Furthermore, it delves into practical applications of GPGPU in various domains such as scientific computing, machine learning acceleration, real-time rendering, and cryptocurrency mining. The authors also emphasize the importance of selecting the right parallel architecture (e.g., GPU, FPGA, TPU, ASIC) based on specific tasks, offering insights into optimizing algorithms for these platforms. The book also provides practical examples with popular machine learning frameworks like PyTorch, TensorFlow, and XGBoost, demonstrating how to efficiently leverage GPU resources in both training and inference. This resource is valuable for both beginners and advanced readers who are looking to deepen their understanding of GPU-based parallel computing and its significant role in modern machine learning and AI applications., Comment: 106 pages

This book, Design Patterns in Machine Learning and Deep Learning: Advancing Big Data Analytics Management, presents a comprehensive study of essential design patterns tailored for large-scale machine learning and deep learning applications. The book explores the application of classical software engineering patterns, Creational, Structural, Behavioral, and Concurrency Patterns, to optimize the development, maintenance, and scalability of big data analytics systems. Through practical examples and detailed Python implementations, it bridges the gap between traditional object-oriented design patterns and the unique demands of modern data analytics environments. Key design patterns such as Singleton, Factory, Observer, and Strategy are analyzed for their impact on model management, deployment strategies, and team collaboration, providing invaluable insights into the engineering of efficient, reusable, and flexible systems. This volume is an essential resource for developers, researchers, and engineers aiming to enhance their technical expertise in both machine learning and software design., Comment: 138pages

This book serves as an introduction to deep learning and machine learning, focusing on their applications in big data analytics. It covers essential concepts, tools like ChatGPT and Claude, hardware recommendations, and practical guidance on setting up development environments using libraries like PyTorch and TensorFlow. Designed for beginners and advanced users alike, it provides step-by-step instructions, hands-on projects, and insights into AI's future, including AutoML and edge computing., Comment: This book contains 156 pages and 9 figures

Object-Oriented Programming (OOP) has become a crucial paradigm for managing the growing complexity of modern software systems, particularly in fields like machine learning, deep learning, large language models (LLM), and data analytics. This work provides a comprehensive introduction to the integration of OOP techniques within these domains, with a focus on improving code modularity, maintainability, and scalability. We begin by outlining the evolution of computing and the rise of OOP, followed by an in-depth discussion of key OOP principles such as encapsulation, inheritance, polymorphism, and abstraction. The practical application of these principles is demonstrated using Python, a widely adopted language in AI and data science. Furthermore, we examine how design patterns and modular programming can be employed to enhance the structure and efficiency of machine learning systems. In subsequent sections, we apply these OOP concepts to real-world AI tasks, including the encapsulation of preprocessing workflows, machine learning model training, and evaluation. Detailed examples illustrate how OOP can be used to build reusable, scalable machine learning systems while maintaining code clarity and reducing redundancy.This work is intended to serve as a bridge for both beginners and experienced developers, equipping them with the necessary knowledge to apply OOP methodologies in AI-driven projects, ultimately fostering the development of more robust and maintainable systems., Comment: 47pages

This book explores the role of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in driving the progress of big data analytics and management. The book focuses on simplifying the complex mathematical concepts behind deep learning, offering intuitive visualizations and practical case studies to help readers understand how neural networks and technologies like Convolutional Neural Networks (CNNs) work. It introduces several classic models and technologies such as Transformers, GPT, ResNet, BERT, and YOLO, highlighting their applications in fields like natural language processing, image recognition, and autonomous driving. The book also emphasizes the importance of pre-trained models and how they can enhance model performance and accuracy, with instructions on how to apply these models in various real-world scenarios. Additionally, it provides an overview of key big data management technologies like SQL and NoSQL databases, as well as distributed computing frameworks such as Apache Hadoop and Spark, explaining their importance in managing and processing vast amounts of data. Ultimately, the book underscores the value of mastering deep learning and big data management skills as critical tools for the future workforce, making it an essential resource for both beginners and experienced professionals., Comment: This book contains 93 pages and 60 figures

This book focuses on the application of TensorFlow pre-trained models in deep learning, providing detailed guidance on effectively using these models for tasks such as image classification and object detection. It covers practical implementations of modern architectures like ResNet, MobileNet, and EfficientNet, demonstrating the power of transfer learning through real-world examples and experiments. The book compares linear probing and model fine-tuning, offering visualizations using techniques such as PCA, t-SNE, and UMAP to help readers intuitively understand the impact of different approaches. Designed for beginners to advanced users, this book includes complete example code and step-by-step instructions, enabling readers to quickly master how to leverage pre-trained models to improve performance in practical scenarios. By blending theoretical insights with hands-on practice, this book equips readers with the knowledge to confidently tackle various deep learning challenges., Comment: This book contains 148 pages and 7 figures

The rise of Multimodal Large Language Models (MLLMs) has become a transformative force in the field of artificial intelligence, enabling machines to process and generate content across multiple modalities, such as text, images, audio, and video. These models represent a significant advancement over traditional unimodal systems, opening new frontiers in diverse applications ranging from autonomous agents to medical diagnostics. By integrating multiple modalities, MLLMs achieve a more holistic understanding of information, closely mimicking human perception. As the capabilities of MLLMs expand, the need for comprehensive and accurate performance evaluation has become increasingly critical. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs, covering key topics such as foundational concepts, applications, evaluation methodologies, ethical concerns, security, efficiency, and domain-specific applications. Through the classification and analysis of existing literature, we summarize the main contributions and methodologies of various surveys, conduct a detailed comparative analysis, and examine their impact within the academic community. Additionally, we identify emerging trends and underexplored areas in MLLM research, proposing potential directions for future studies. This survey is intended to offer researchers and practitioners a comprehensive understanding of the current state of MLLM evaluation, thereby facilitating further progress in this rapidly evolving field., Comment: The article consists of 22 pages, including 2 figures and 108 references. The paper provides a meta-review of surveys on Multimodal Large Language Models (MLLMs), categorizing findings into key areas such as evaluation, applications, security, and future directions

Model extraction attacks (MEAs) on large language models (LLMs) have received increasing research attention lately. Existing attack methods on LLMs inherit the extraction strategies from those designed for deep neural networks (DNNs) yet neglect the inconsistency of training tasks between MEA and LLMs' alignments. As such, they result in poor attack performances. To tackle this issue, we present Locality Reinforced Distillation (LoRD), a novel model extraction attack algorithm specifically for LLMs. In particular, we design a policy-gradient-style training task, which utilizes victim models' responses as a signal to guide the crafting of preference for the local model. Theoretical analysis has shown that i) LoRD's convergence procedure in MEAs is consistent with the alignments of LLMs, and ii) LoRD can reduce query complexity while mitigating watermark protection through exploration-based stealing. Extensive experiments on domain-specific extractions demonstrate the superiority of our method by examining the extraction of various state-of-the-art commercial LLMs., Comment: Source code: https://github.com/liangzid/alignmentExtraction

In this paper, we study the null controllability for a stochastic semilinear CahnHilliard type equation, whose semilinear term contains first and second order derivatives of solutions. To start with, an improved global Carleman estimate for linear backward stochastic fourth order parabolic equations with $L^2$-valued source terms is derived, which is based on a new fundamental identity for a stochastic fourth order parabolic operator. Based on it, we establish a new global Carleman estimate for linear backward stochastic fourth order parabolic equations with $H^{-2}$-valued source terms, which, together with a fixed point argument, derive the desired null controllability for the stochastic Cahn-Hilliard type equation., Comment: arXiv admin note: substantial text overlap with arXiv:2408.03114

In this paper, we study the null controllability of forward and backward stochastic semilinear complex Ginzburg-Landau equations with global Lipschitz nonlinear terms. For this purpose, by deriving an improved global Carleman estimates for linear systems, we obtain the controllability results for the stochastic linear systems with a $L^2$-valued source term. Based on it, together with a Banach fixed point argument, the desired null controllability of semilinear systems is derived.

Heat shuttling phenomenon is characterized by the presence of a non-zero heat flow between two bodies without net thermal bias on average. It was initially predicted in the context of nonlinear heat conduction within atomic lattices coupled to two time-oscillating thermostats. Recent theoretical works revealed an analog of this effect for heat exchanges mediated by thermal photons between two solids having a temperature dependent emissivity. In this paper, we present the experimental proof of this effect using systems made with composite materials based on phase change materials. By periodically modulating the temperature of one of two solids we report that the system akin to heat pumping with a controllable heat flow direction. Additionally, we demonstrate the effectiveness of a simultaneous modulation of two temperatures to control both the strength and direction of heat shuttling by exploiting the phase delay between these temperatures. These results show that this effect is promising for an active thermal management of solid-state technology, to cool down solids, to insulate them from their background or to amplify heat exchanges.

The involvement of evanescent waves in the near-field regime could greatly enhance the spontaneous thermal radiation, offering a unique opportunity to study nanoscale photon-phonon interaction. However, accurately characterizing this subtle phenomenon is very challenging. This paper proposes a transient all-optical method for rapidly characterizing near-field radiative heat transfer (NFRHT) between macroscopic objects, using the first law of thermodynamics. Significantly, a full measurement at a fixed gap distance is completed within tens of seconds. By simplifying the configuration, the transient all-optical method achieves high measurement accuracy and reliable reproducibility. The proposed method can effectively analyze the NFRHT in various material systems, including SiO2, SiC, and Si, which involve different phonon or plasmon polaritons. Experimental observations demonstrate significant super-Planckian radiation, which arises from the near-field coupling of bounded surface modes. Furthermore, the method achieves excellent agreement with theory, with a minimal discrepancy of less than 2.7% across a wide temperature range. This wireless method could accurately characterize the NFRHT for objects with different sizes or optical properties, enabling the exploration of both fundamental interests and practical applications.

Digital storytelling, essential in entertainment, education, and marketing, faces challenges in production scalability and flexibility. The StoryAgent framework, introduced in this paper, utilizes Large Language Models and generative tools to automate and refine digital storytelling. Employing a top-down story drafting and bottom-up asset generation approach, StoryAgent tackles key issues such as manual intervention, interactive scene orchestration, and narrative consistency. This framework enables efficient production of interactive and consistent narratives across multiple modalities, democratizing content creation and enhancing engagement. Our results demonstrate the framework's capability to produce coherent digital stories without reference videos, marking a significant advancement in automated digital storytelling., Comment: 16 pages, 13 figures

This paper tackles the emerging challenge of training generative models within a self-consuming loop, wherein successive generations of models are recursively trained on mixtures of real and synthetic data from previous generations. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models, including parametric and non-parametric models. Specifically, we derive bounds on the total variation (TV) distance between the synthetic data distributions produced by future models and the original real data distribution under various mixed training scenarios for diffusion models with a one-hidden-layer neural network score function. Our analysis demonstrates that this distance can be effectively controlled under the condition that mixed training dataset sizes or proportions of real data are large enough. Interestingly, we further unveil a phase transition induced by expanding synthetic data amounts, proving theoretically that while the TV distance exhibits an initial ascent, it declines beyond a threshold point. Finally, we present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation., Comment: Accepted at ICML 2024

Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models with human values, reward hacking, also termed reward overoptimization, remains a critical challenge. This issue primarily arises from reward misgeneralization, where reward models (RMs) compute reward using spurious features that are irrelevant to human preferences. In this work, we tackle this problem from an information-theoretic perspective and propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective to filter out irrelevant information. Notably, we further identify a correlation between overoptimization and outliers in the IB latent space of InfoRM, establishing it as a promising tool for detecting reward overoptimization. Inspired by this finding, we propose the Cluster Separation Index (CSI), which quantifies deviations in the IB latent space, as an indicator of reward overoptimization to facilitate the development of online mitigation strategies. Extensive experiments on a wide range of settings and RM scales (70M, 440M, 1.4B, and 7B) demonstrate the effectiveness of InfoRM. Further analyses reveal that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets, signifying a notable advancement in the field of RLHF. The code will be released upon acceptance., Comment: The paper has been accepted by NeurIPS 2024

Bridging the gap between diffusion models and human preferences is crucial for their integration into practical generative workflows. While optimizing downstream reward models has emerged as a promising alignment strategy, concerns arise regarding the risk of excessive optimization with learned reward models, which potentially compromises ground-truth performance. In this work, we confront the reward overoptimization problem in diffusion model alignment through the lenses of both inductive and primacy biases. We first identify a mismatch between current methods and the temporal inductive bias inherent in the multi-step denoising process of diffusion models, as a potential source of reward overoptimization. Then, we surprisingly discover that dormant neurons in our critic model act as a regularization against reward overoptimization while active neurons reflect primacy bias. Motivated by these observations, we propose Temporal Diffusion Policy Optimization with critic active neuron Reset (TDPO-R), a policy gradient algorithm that exploits the temporal inductive bias of diffusion models and mitigates the primacy bias stemming from active neurons. Empirical results demonstrate the superior efficacy of our methods in mitigating reward overoptimization. Code is avaliable at https://github.com/ZiyiZhang27/tdpo., Comment: Accepted to ICML 2024

Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. Current research predominantly focuses on handling autocorrelation within the historical sequence but often neglects its presence in the label sequence. Specifically, emerging forecast models mainly conform to the direct forecast (DF) paradigm, generating multi-step forecasts under the assumption of conditional independence within the label sequence. This assumption disregards the inherent autocorrelation in the label sequence, thereby limiting the performance of DF-based models. In response to this gap, we introduce the Frequency-enhanced Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain. Our experiments demonstrate that FreDF substantially outperforms existing state-of-the-art methods including iTransformer and is compatible with a variety of forecast models.

Supplying data augmentation to conversational question answering (CQA) can effectively improve model performance. However, there is less improvement from single-turn datasets in CQA due to the distribution gap between single-turn and multi-turn datasets. On the other hand, while numerous single-turn datasets are available, we have not utilized them effectively. To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets. The proposed method consists of three parts, namely, a QA pair Generator, a QA pair Reassembler, and a question Rewriter. Given a sample consisting of context and single-turn QA pairs, the Generator obtains candidate QA pairs and a knowledge graph based on the context. The Reassembler utilizes the knowledge graph to get sequential QA pairs, and the Rewriter rewrites questions from a conversational perspective to obtain a multi-turn dataset S2M. Our experiments show that our method can synthesize effective training resources for CQA. Notably, S2M ranks 1st place on the QuAC leaderboard at the time of submission (Aug 24th, 2022).

The near-field radiative heat transfer (NFRHT) between one-dimensional metamaterials comprising phonon dielectric multilayers was experimented. Large sized (1cm x 1cm) near-field samples were fabricated using SiC, SiO2 and Ge layers at a certain gap distance, and the effect of layer stacking order and phonon resonance quality on the NFRHT was examined. The measured results show good agreement with those obtained theoretically employing the transmission matrix method. Super-Planckian blackbody radiation was observed between the emitters and receivers with identical structures. Measurements demonstrate failure of the effective medium theory (EMT) in predicting the near-field heat flux especially in the presence of bounded surface modes, such as the epsilon-near-zero (ENZ) mode. Analyses also indicate that, in certain cases, the EMT can provide reasonable physical insight into the underlying coupling process from the perspective of homogenized media. The conditions to apply the EMT in the near-field regime was also touched upon., Comment: 18 pages,5 figures

Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL). Although methods like resetting and regularization can potentially mitigate plasticity loss, the influences of various components within the VRL framework on the agent's plasticity are still poorly understood. In this work, we conduct a systematic empirical exploration focusing on three primary underexplored facets and derive the following insightful conclusions: (1) data augmentation is essential in maintaining plasticity; (2) the critic's plasticity loss serves as the principal bottleneck impeding efficient training; and (3) without timely intervention to recover critic's plasticity in the early stages, its loss becomes catastrophic. These insights suggest a novel strategy to address the high replay ratio (RR) dilemma, where exacerbated plasticity loss hinders the potential improvements of sample efficiency brought by increased reuse frequency. Rather than setting a static RR for the entire training process, we propose Adaptive RR, which dynamically adjusts the RR based on the critic's plasticity level. Extensive evaluations indicate that Adaptive RR not only avoids catastrophic plasticity loss in the early stages but also benefits from more frequent reuse in later phases, resulting in superior sample efficiency., Comment: ICLR 2024 poster

Previous studies regarding the perception of emotions for embodied virtual agents have shown the effectiveness of using virtual characters in conveying emotions through interactions with humans. However, creating an autonomous embodied conversational agent with expressive behaviors presents two major challenges. The first challenge is the difficulty of synthesizing the conversational behaviors for each modality that are as expressive as real human behaviors. The second challenge is that the affects are modeled independently, which makes it difficult to generate multimodal responses with consistent emotions across all modalities. In this work, we propose a conceptual framework, ACTOR (Affect-Consistent mulTimodal behaviOR generation), that aims to increase the perception of affects by generating multimodal behaviors conditioned on a consistent driving affect. We have conducted a user study with 199 participants to assess how the average person judges the affects perceived from multimodal behaviors that are consistent and inconsistent with respect to a driving affect. The result shows that among all model conditions, our affect-consistent framework receives the highest Likert scores for the perception of driving affects. Our statistical analysis suggests that making a modality affect-inconsistent significantly decreases the perception of driving affects. We also observe that multimodal behaviors conditioned on consistent affects are more expressive compared to behaviors with inconsistent affects. Therefore, we conclude that multimodal emotion conditioning and affect consistency are vital to enhancing the perception of affects for embodied conversational agents.

The swift advancement in the scales and capabilities of Large Language Models (LLMs) positions them as promising tools for a variety of downstream tasks. In addition to the pursuit of better performance and the avoidance of violent feedback on a certain prompt, to ensure the responsibility of the LLM, much attention is drawn to the robustness of LLMs. However, existing evaluation methods mostly rely on traditional question answering datasets with predefined supervised labels, which do not align with the superior generation capabilities of contemporary LLMs. To address this issue, we propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools to evaluate the longer conversation generated from more challenging open questions by LLMs, which we refer to as the Reward Model for Reasonable Robustness Evaluation (TREvaL). Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions, a capability not entirely encompassed by individual words or letters, which may exhibit oversimplification and inherent biases. Our extensive empirical experiments demonstrate that TREvaL provides an innovative method for evaluating the robustness of an LLM. Furthermore, our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage. Notably, we are surprised to discover that robustness tends to decrease as fine-tuning (SFT and RLHF) is conducted. The code of TREval is available in https://github.com/Harry-mic/TREvaL.