pwc-archive/papers-with-abstracts huggingface dataset:
DLGNet: Hyperedge Classification through Directed Line Graphs for Chemical Reactions
Authors: Stefano Fiorini, Giulia M. Bovolenta, Stefano Coniglio, Michele Ciavotta, Pietro Morerio, Michele Parrinello, Alessio Del Bue
Date: 2024-10-09 00:00:00
Tasks: Drug Discovery, Graph Neural Network, hyperedge classification

Graphs and hypergraphs provide powerful abstractions for modeling interactions among a set of entities of interest and have been attracting a growing interest in the literature thanks to many successful applications in several fields. In particular, they are rapidly expanding in domains such as chemistry and biology, especially in the areas of drug discovery and molecule generation. One of the areas witnessing the fasted growth is the chemical reactions field, where chemical reactions can be naturally encoded as directed hyperedges of a hypergraph. In this paper, we address the chemical reaction classification problem by introducing the notation of a Directed Line Graph (DGL) associated with a given directed hypergraph. On top of it, we build the Directed Line Graph Network (DLGNet), the first spectral-based Graph Neural Network (GNN) expressly designed to operate on a hypergraph via its DLG transformation. The foundation of DLGNet is a novel Hermitian matrix, the Directed Line Graph Laplacian, which compactly encodes the directionality of the interactions taking place within the directed hyperedges of the hypergraph thanks to the DLG representation. The Directed Line Graph Laplacian enjoys many desirable properties, including admitting an eigenvalue decomposition and being positive semidefinite, which make it well-suited for its adoption within a spectral-based GNN. Through extensive experiments on chemical reaction datasets, we show that DGLNet significantly outperforms the existing approaches, achieving on a collection of real-world datasets an average relative-percentage-difference improvement of 33.01%, with a maximum improvement of 37.71%.
Uncovering Factor Level Preferences to Improve Human-Model Alignment
Authors: Juhyun Oh, Eunsu Kim, Jiseon Kim, Wenda Xu, Inha Cha, William Yang Wang, Alice Oh
Date: 2024-10-09 00:00:00
Tasks: Language Modelling, Large Language Model, Question Answering, Response Generation

Despite advancements in Large Language Model (LLM) alignment, understanding the reasons behind LLM preferences remains crucial for bridging the gap between desired and actual behavior. LLMs often exhibit biases or tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. However, current methods for evaluating preference alignment often lack explainability, relying on coarse-grained comparisons. To address this, we introduce PROFILE (PRObing Factors of InfLuence for Explainability), a novel framework that uncovers and quantifies the influence of specific factors driving preferences. PROFILE's factor level analysis explains the 'why' behind human-model alignment and misalignment, offering insights into the direction of model improvement. We apply PROFILE to analyze human and LLM preferences across three tasks: summarization, helpful response generation, and document-based question-answering. Our factor level analysis reveals a substantial discrepancy between human and LLM preferences in generation tasks, whereas LLMs show strong alignment with human preferences in evaluation tasks. We demonstrate how leveraging factor level insights, including addressing misaligned factors or exploiting the generation-evaluation gap, can improve alignment with human preferences. This work underscores the importance of explainable preference analysis and highlights PROFILE's potential to provide valuable training signals, driving further improvements in human-model alignment.
ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling
Authors: Deok-Kyeong Jang, Dongseok Yang, Deok-Yun Jang, Byeoli Choi, Donghoon Shin, Sung-Hee Lee
Date: 2024-10-09 00:00:00
Tasks: Data Augmentation

This paper introduces ELMO, a real-time upsampling motion capture framework designed for a single LiDAR sensor. Modeled as a conditional autoregressive transformer-based upsampling motion generator, ELMO achieves 60 fps motion capture from a 20 fps LiDAR point cloud sequence. The key feature of ELMO is the coupling of the self-attention mechanism with thoughtfully designed embedding modules for motion and point clouds, significantly elevating the motion quality. To facilitate accurate motion capture, we develop a one-time skeleton calibration model capable of predicting user skeleton offsets from a single-frame point cloud. Additionally, we introduce a novel data augmentation technique utilizing a LiDAR simulator, which enhances global root tracking to improve environmental understanding. To demonstrate the effectiveness of our method, we compare ELMO with state-of-the-art methods in both image-based and point cloud-based motion capture. We further conduct an ablation study to validate our design principles. ELMO's fast inference time makes it well-suited for real-time applications, exemplified in our demo video featuring live streaming and interactive gaming scenarios. Furthermore, we contribute a high-quality LiDAR-mocap synchronized dataset comprising 20 different subjects performing a range of motions, which can serve as a valuable resource for future research. The dataset and evaluation code are available at {\blue \url{https://movin3d.github.io/ELMO_SIGASIA2024/}}
Self-Boosting Large Language Models with Synthetic Preference Data
Authors: Qingxiu Dong, Li Dong, Xingxing Zhang, Zhifang Sui, Furu Wei
Date: 2024-10-09 00:00:00
Tasks: Instruction Following

Through alignment with human preferences, Large Language Models (LLMs) have advanced significantly in generating honest, harmless, and helpful responses. However, collecting high-quality preference data is a resource-intensive and creativity-demanding process, especially for the continual improvement of LLMs. We introduce SynPO, a self-boosting paradigm that leverages synthetic preference data for model alignment. SynPO employs an iterative mechanism wherein a self-prompt generator creates diverse prompts, and a response improver refines model responses progressively. This approach trains LLMs to autonomously learn the generative rewards for their own outputs and eliminates the need for large-scale annotation of prompts and human preferences. After four SynPO iterations, Llama3-8B and Mistral-7B show significant enhancements in instruction-following abilities, achieving over 22.1% win rate improvements on AlpacaEval 2.0 and ArenaHard. Simultaneously, SynPO improves the general performance of LLMs on various tasks, validated by a 3.2 to 5.0 average score increase on the well-recognized Open LLM leaderboard.
Support Vector Boosting Machine (SVBM): Enhancing Classification Performance with AdaBoost and Residual Connections
Authors: Junbo Jacob Lian
Date: 2024-10-09 00:00:00

In traditional boosting algorithms, the focus on misclassified training samples emphasizes their importance based on difficulty during the learning process. While using a standard Support Vector Machine (SVM) as a weak learner in an AdaBoost framework can enhance model performance by concentrating on error samples, this approach introduces significant challenges. Specifically, SVMs, characterized by their stability and robustness, may require destabilization to fit the boosting paradigm, which in turn can constrain performance due to reliance on the weighted results from preceding iterations. To address these challenges, we propose the Support Vector Boosting Machine (SVBM), which integrates a novel subsampling process with SVM algorithms and residual connection techniques. This method updates sample weights by considering both the current model's predictions and the outputs from prior rounds, allowing for effective sparsity control. The SVBM framework enhances the ability to form complex decision boundaries, thereby improving classification performance. The MATLAB source code for SVBM can be accessed at https://github.com/junbolian/SVBM.
Faithful Interpretation for Graph Neural Networks
Authors: Lijie Hu, Tianhao Huang, Lu Yu, WanYu Lin, Tianhang Zheng, Di Wang
Date: 2024-10-09 00:00:00
Tasks: Graph Attention

Currently, attention mechanisms have garnered increasing attention in Graph Neural Networks (GNNs), such as Graph Attention Networks (GATs) and Graph Transformers (GTs). It is not only due to the commendable boost in performance they offer but also its capacity to provide a more lucid rationale for model behaviors, which are often viewed as inscrutable. However, Attention-based GNNs have demonstrated instability in interpretability when subjected to various sources of perturbations during both training and testing phases, including factors like additional edges or nodes. In this paper, we propose a solution to this problem by introducing a novel notion called Faithful Graph Attention-based Interpretation (FGAI). In particular, FGAI has four crucial properties regarding stability and sensitivity to interpretation and final output distribution. Built upon this notion, we propose an efficient methodology for obtaining FGAI, which can be viewed as an ad hoc modification to the canonical Attention-based GNNs. To validate our proposed solution, we introduce two novel metrics tailored for graph interpretation assessment. Experimental results demonstrate that FGAI exhibits superior stability and preserves the interpretability of attention under various forms of perturbations and randomness, which makes FGAI a more faithful and reliable explanation tool.
A Trilogy of AI Safety Frameworks: Paths from Facts and Knowledge Gaps to Reliable Predictions and New Knowledge
Authors: Simon Kasif
Date: 2024-10-09 00:00:00

AI Safety has become a vital front-line concern of many scientists within and outside the AI community. There are many immediate and long term anticipated risks that range from existential risk to human existence to deep fakes and bias in machine learning systems [1-5]. In this paper, we reduce the full scope and immense complexity of AI safety concerns to a trilogy of three important but tractable opportunities for advances that have the short-term potential to improve AI safety and reliability without reducing AI innovation in critical domains. In this perspective, we discuss this vision based on several case studies that already produced proofs of concept in critical ML applications in biomedical science.
AutoFeedback: An LLM-based Framework for Efficient and Accurate API Request Generation
Authors: Huanxi Liu, Jiaqi Liao, Dawei Feng, Kele Xu, Huaimin Wang
Date: 2024-10-09 00:00:00

Large Language Models (LLMs) leverage external tools primarily through generating the API request to enhance task completion efficiency. The accuracy of API request generation significantly determines the capability of LLMs to accomplish tasks. Due to the inherent hallucinations within the LLM, it is difficult to efficiently and accurately generate the correct API request. Current research uses prompt-based feedback to facilitate the LLM-based API request generation. However, existing methods lack factual information and are insufficiently detailed. To address these issues, we propose AutoFeedback, an LLM-based framework for efficient and accurate API request generation, with a Static Scanning Component (SSC) and a Dynamic Analysis Component (DAC). SSC incorporates errors detected in the API requests as pseudo-facts into the feedback, enriching the factual information. DAC retrieves information from API documentation, enhancing the level of detail in feedback. Based on this two components, Autofeedback implementes two feedback loops during the process of generating API requests by the LLM. Extensive experiments demonstrate that it significantly improves accuracy of API request generation and reduces the interaction cost. AutoFeedback achieves an accuracy of 100.00\% on a real-world API dataset and reduces the cost of interaction with GPT-3.5 Turbo by 23.44\%, and GPT-4 Turbo by 11.85\%.
Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models
Authors: Haoyu Tang, Ye Liu, Xukai Liu, Kai Zhang, Yanghai Zhang, Qi Liu, Enhong Chen
Date: 2024-07-25 00:00:00
Tasks: Contrastive Learning, Machine Unlearning

Recent advancements in machine learning, particularly in Natural Language Processing (NLP), have led to the development of sophisticated models trained on extensive datasets, yet raising concerns about the potential leakage of sensitive information. In response, regulatory measures such as the European Union's General Data Protection Regulation (GDPR) have driven increasing interest in Machine Unlearning techniques, which enable models to selectively forget specific data entries. Early approaches primarily relied on pre-processing methods, while more recent research has shifted towards training-based unlearning techniques. Despite their effectiveness, most existing methods require access to the original training data, which is often inaccessible. Additionally, directly applying unlearning techniques bear the cost of undermining the model's expressive capabilities. To address these challenges, we introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components: A Knowledge Unlearning Induction module designed to remove specific knowledge through an unlearning loss; A Contrastive Learning Enhancement module to preserve the model's expressive capabilities against the pure unlearning goal; And an Iterative Unlearning Refinement module that dynamically assess the unlearning extent on specific data pieces and make iterative update. Experimental results demonstrate the efficacy of our ICU method in unlearning sensitive information while maintaining the model's overall performance, offering a promising solution for privacy-conscious machine learning applications.
Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models
Authors: Daniel Albert, Stephan Billinger
Date: 2024-10-09 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model

In this study, we propose LLM agents as a novel approach in behavioral strategy research, complementing simulations and laboratory experiments to advance our understanding of cognitive processes in decision-making. Specifically, we reproduce a human laboratory experiment in behavioral strategy using large language model (LLM) generated agents and investigate how LLM agents compare to observed human behavior. Our results show that LLM agents effectively reproduce search behavior and decision-making comparable to humans. Extending our experiment, we analyze LLM agents' simulated "thoughts," discovering that more forward-looking thoughts correlate with favoring exploitation over exploration to maximize wealth. We show how this new approach can be leveraged in behavioral strategy research and address limitations.
Compositional Entailment Learning for Hyperbolic Vision-Language Models
Authors: Avik Pal, Max van Spengler, Guido Maria D'Amely di Melendugno, Alessandro Flaborea, Fabio Galasso, Pascal Mettes
Date: 2024-10-09 00:00:00
Tasks: Language Modelling, Representation Learning, Sentence

Image-text representation learning forms a cornerstone in vision-language models, where pairs of images and textual descriptions are contrastively aligned in a shared embedding space. Since visual and textual concepts are naturally hierarchical, recent work has shown that hyperbolic space can serve as a high-potential manifold to learn vision-language representation with strong downstream performance. In this work, for the first time we show how to fully leverage the innate hierarchical nature of hyperbolic embeddings by looking beyond individual image-text pairs. We propose Compositional Entailment Learning for hyperbolic vision-language models. The idea is that an image is not only described by a sentence but is itself a composition of multiple object boxes, each with their own textual description. Such information can be obtained freely by extracting nouns from sentences and using openly available localized grounding models. We show how to hierarchically organize images, image boxes, and their textual descriptions through contrastive and entailment-based objectives. Empirical evaluation on a hyperbolic vision-language model trained with millions of image-text pairs shows that the proposed compositional learning approach outperforms conventional Euclidean CLIP learning, as well as recent hyperbolic alternatives, with better zero-shot and retrieval generalization and clearly stronger hierarchical performance.
Combining Planning and Diffusion for Mobility with Unknown Dynamics
Authors: Yajvan Ravan, Zhutian Yang, Tao Chen, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Date: 2024-10-09 00:00:00
Tasks: Friction, Motion Planning, Robot Manipulation

Manipulation of large objects over long horizons (such as carts in a warehouse) is an essential skill for deployable robotic systems. Large objects require mobile manipulation which involves simultaneous manipulation, navigation, and movement with the object in tow. In many real-world situations, object dynamics are incredibly complex, such as the interaction of an office chair (with a rotating base and five caster wheels) and the ground. We present a hierarchical algorithm for long-horizon robot manipulation problems in which the dynamics are partially unknown. We observe that diffusion-based behavior cloning is highly effective for short-horizon problems with unknown dynamics, so we decompose the problem into an abstract high-level, obstacle-aware motion-planning problem that produces a waypoint sequence. We use a short-horizon, relative-motion diffusion policy to achieve the waypoints in sequence. We train mobile manipulation policies on a Spot robot that has to push and pull an office chair. Our hierarchical manipulation policy performs consistently better, especially when the horizon increases, compared to a diffusion policy trained on long-horizon demonstrations or motion planning assuming a rigidly-attached object (success rate of 8 (versus 0 and 5 respectively) out of 10 runs). Importantly, our learned policy generalizes to new layouts, grasps, chairs, and flooring that induces more friction, without any further training, showing promise for other complex mobile manipulation problems. Project Page: https://yravan.github.io/plannerorderedpolicy/
Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing Courses
Authors: Hieke Keuning, Isaac Alpizar-Chacon, Ioanna Lykourentzou, Lauren Beehler, Christian Köppe, Imke de Jong, Sergey Sosnovsky
Date: 2024-10-09 00:00:00
Tasks: Ethics

Investigation of students' perceptions and opinions on the use of generative artificial intelligence (GenAI) in education is a topic gaining much interest. Studies addressing this are typically conducted with large heterogeneous groups, at one moment in time. However, how students perceive and use GenAI tools can potentially depend on many factors, including their background knowledge, familiarity with the tools, and the learning goals and policies of the courses they are taking. In this study we explore how students following computing courses use GenAI for programming-related tasks across different programs and courses: Bachelor and Master, in courses in which learning programming is the learning goal, courses that require programming as a means to achieve another goal, and in courses in which programming is optional, but can be useful. We are also interested in changes over time, since GenAI capabilities are changing at a fast pace, and users are adopting GenAI increasingly. We conducted three consecutive surveys (fall `23, winter `23, and spring `24) among students of all computing programs of a large European research university. We asked questions on the use in education, ethics, and job prospects, and we included specific questions on the (dis)allowed use of GenAI tools in the courses they were taking at the time. We received 264 responses, which we quantitatively and qualitatively analyzed, to find out how students have employed GenAI tools across 59 different computing courses, and whether the opinion of an average student about these tools evolves over time. Our study contributes to the emerging discussion of how to differentiate GenAI use across different courses, and how to align its use with the learning goals of a computing course.
Understanding Model Ensemble in Transferable Adversarial Attack
Authors: Wei Yao, Zeliang Zhang, Huayi Tang, Yong liu
Date: 2024-10-09 00:00:00
Tasks: Adversarial Attack, Diversity, model

Model ensemble adversarial attack has become a powerful method for generating transferable adversarial examples that can target even unknown models, but its theoretical foundation remains underexplored. To address this gap, we provide early theoretical insights that serve as a roadmap for advancing model ensemble adversarial attack. We first define transferability error to measure the error in adversarial transferability, alongside concepts of diversity and empirical model ensemble Rademacher complexity. We then decompose the transferability error into vulnerability, diversity, and a constant, which rigidly explains the origin of transferability error in model ensemble attack: the vulnerability of an adversarial example to ensemble components, and the diversity of ensemble components. Furthermore, we apply the latest mathematical tools in information theory to bound the transferability error using complexity and generalization terms, contributing to three practical guidelines for reducing transferability error: (1) incorporating more surrogate models, (2) increasing their diversity, and (3) reducing their complexity in cases of overfitting. Finally, extensive experiments with 54 models validate our theoretical framework, representing a significant step forward in understanding transferable model ensemble adversarial attacks.
A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering
Authors: Qihan Qi, Xinsong Yang, Gang Xia, Daniel W. C. Ho, Pengyang Tang
Date: 2024-10-09 00:00:00
Tasks: Reinforcement Learning (RL), Safe Reinforcement Learning, SMAC, SMAC+

This paper proposes a safety modulator actor-critic (SMAC) method to address safety constraint and overestimation mitigation in model-free safe reinforcement learning (RL). A safety modulator is developed to satisfy safety constraints by modulating actions, allowing the policy to ignore safety constraint and focus on maximizing reward. Additionally, a distributional critic with a theoretical update rule for SMAC is proposed to mitigate the overestimation of Q-values with safety constraints. Both simulation and real-world scenarios experiments on Unmanned Aerial Vehicles (UAVs) hovering confirm that the SMAC can effectively maintain safety constraints and outperform mainstream baseline algorithms.
Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity
Authors: Mutian He, Philip N. Garner
Date: 2024-10-09 00:00:00
Tasks: Language Modeling, Language Modelling, Mamba

Architectures such as Linformer and Mamba have recently emerged as competitive linear time replacements for transformers. However, corresponding large pretrained models are often unavailable, especially in non-text domains. To remedy this, we present a Cross-Architecture Layerwise Distillation (CALD) approach that jointly converts a transformer model to a linear time substitute and fine-tunes it to a target task. We also compare several means to guide the fine-tuning to optimally retain the desired inference capability from the original model. The methods differ in their use of the target model and the trajectory of the parameters. In a series of empirical studies on language processing, language modeling, and speech processing, we show that CALD can effectively recover the result of the original model, and that the guiding strategy contributes to the result. Some reasons for the variation are suggested.