pwc-archive/papers-with-abstracts huggingface dataset:
Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation
Authors: Zhuohao Yu, Weizheng Gu, Yidong Wang, Xingru Jiang, Zhengran Zeng, Jindong Wang, Wei Ye, Shikun Zhang
Date: 2024-12-19 00:00:00
Tasks: Code Generation

Large Language Models excel at code generation yet struggle with complex programming tasks that demand sophisticated reasoning. To bridge this gap, traditional process supervision relies on learned reward models requiring costly training data and suffering from reward misalignment, while outcome supervision fails for complex tasks needing coordinated intermediate steps. We introduce Outcome Refining Process Supervision, which unifies process and outcome supervision by leveraging executable verification: a tree-structured search framework generates strategic alternatives, profiles execution metrics, and scores candidates via self-critique mechanisms that integrate runtime feedback with reasoning. Experiments across 5 models and 3 benchmarks show consistent gains, with 26.9% higher correctness and 42.2% improved code efficiency. The results demonstrate that ORPS enables LLMs to overcome local optima in code generation, suggesting a promising direction for combining verifiable outcomes with structured reasoning to tackle complex challenges. We open-source at: https://github.com/zhuohaoyu/ORPS
Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment
Authors: Qiyang Sun, Yupei Li, Emran Alturki, Sunil Munthumoduku Krishna Murthy, Björn W. Schuller
Date: 2024-12-19 00:00:00
Tasks: Fairness

As Artificial Intelligence (AI) continues to advance rapidly, Friendly AI (FAI) has been proposed to advocate for more equitable and fair development of AI. Despite its importance, there is a lack of comprehensive reviews examining FAI from an ethical perspective, as well as limited discussion on its potential applications and future directions. This paper addresses these gaps by providing a thorough review of FAI, focusing on theoretical perspectives both for and against its development, and presenting a formal definition in a clear and accessible format. Key applications are discussed from the perspectives of eXplainable AI (XAI), privacy, fairness and affective computing (AC). Additionally, the paper identifies challenges in current technological advancements and explores future research avenues. The findings emphasise the significance of developing FAI and advocate for its continued advancement to ensure ethical and beneficial AI development.
Associative memory inspires improvements for in-context learning using a novel attention residual stream architecture
Authors: Thomas F Burns, Tomoki Fukai, Christopher J Earls
Date: 2024-12-19 00:00:00
Tasks: In-Context Learning

Large language models (LLMs) demonstrate an impressive ability to utilise information within the context of their input sequences to appropriately respond to data unseen by the LLM during its training procedure. This ability is known as in-context learning (ICL). Humans and non-human animals demonstrate similar abilities, however their neural architectures differ substantially from LLMs. Despite this, a critical component within LLMs, the attention mechanism, resembles modern associative memory models, widely used in and influenced by the computational neuroscience community to model biological memory systems. Using this connection, we introduce an associative memory model capable of performing ICL. We use this as inspiration for a novel residual stream architecture which allows information to directly flow between attention heads. We test this architecture during training within a two-layer Transformer and show its ICL abilities manifest more quickly than without this modification. We then apply our architecture in small language models with 8 million parameters, focusing on attention head values, with results also indicating improved ICL performance at this larger and more naturalistic scale.
Exploiting sparse structures and synergy designs to advance situational awareness of electrical power grid
Authors: Shimiao Li
Date: 2024-12-19 00:00:00

The growing threats of uncertainties, anomalies, and cyberattacks on power grids are driving a critical need to advance situational awareness which allows system operators to form a complete and accurate picture of the present and future state. Simulation and estimation are foundational tools in this process. However, existing tools lack the robustness and efficiency required to achieve the level of situational awareness needed for the ever-evolving threat landscape. Industry-standard (steady-state) simulators are not robust to blackouts, often leading to non-converging or non-actionable results. Estimation tools lack robustness to anomalous data, returning erroneous system states. Efficiency is the other major concern as nonlinearities and scalability issues make large systems slow to converge. This thesis addresses robustness and efficiency gaps through a dual-fold contribution. We first address the inherent limitations in the existing physics-based and data-driven worlds; and then transcend the boundaries of conventional algorithmic design in the direction of a new paradigm -- Physics-ML Synergy -- which integrates the strengths of the two worlds. Our approaches are built on circuit formulation which provides a unified framework that applies to both transmission and distribution. Sparse optimization acts as the key enabler to make these tools intrinsically robust and immune to random threats, pinpointing dominant sources of (random) blackouts and data errors. Further, we explore sparsity-exploiting optimizations to develop lightweight ML models whose prediction and detection capabilities are a complement to physics-based tools; and whose lightweight designs advance generalization and scalability. Finally, Physics-ML Synergy brings robustness and efficiency further against targeted cyberthreats, by interconnecting our physics-based tools with lightweight ML.
A Cross-Domain Study of the Use of Persuasion Techniques in Online Disinformation
Authors: João A. Leite, Olesya Razuvayevskaya, Carolina Scarton, Kalina Bontcheva
Date: 2024-12-19 00:00:00
Tasks: Persuasion Strategies

Disinformation, irrespective of domain or language, aims to deceive or manipulate public opinion, typically through employing advanced persuasion techniques. Qualitative and quantitative research on the weaponisation of persuasion techniques in disinformation has been mostly topic-specific (e.g., COVID-19) with limited cross-domain studies, resulting in a lack of comprehensive understanding of these strategies. This study employs a state-of-the-art persuasion technique classifier to conduct a large-scale, multi-domain analysis of the role of 16 persuasion techniques in disinformation narratives. It shows how different persuasion techniques are employed disproportionately in different disinformation domains. We also include a detailed case study on climate change disinformation, highlighting how linguistic, psychological, and cultural factors shape the adaptation of persuasion strategies to fit unique thematic contexts.
A Full Transformer-based Framework for Automatic Pain Estimation using Videos
Authors: Stefanos Gkikas, Manolis Tsiknakis
Date: 2024-12-19 00:00:00
Tasks: Management

The automatic estimation of pain is essential in designing an optimal pain management system offering reliable assessment and reducing the suffering of patients. In this study, we present a novel full transformer-based framework consisting of a Transformer in Transformer (TNT) model and a Transformer leveraging cross-attention and self-attention blocks. Elaborating on videos from the BioVid database, we demonstrate state-of-the-art performances, showing the efficacy, efficiency, and generalization capability across all the primary pain estimation tasks.
Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
Authors: Haoran Liu, Youzhi Luo, Tianxiao Li, James Caverlee, Martin Renqiang Min
Date: 2024-12-19 00:00:00
Tasks: 3D Molecule Generation, Drug Discovery

We consider the conditional generation of 3D drug-like molecules with \textit{explicit control} over molecular properties such as drug-like properties (e.g., Quantitative Estimate of Druglikeness or Synthetic Accessibility score) and effectively binding to specific protein sites. To tackle this problem, we propose an E(3)-equivariant Wasserstein autoencoder and factorize the latent space of our generative model into two disentangled aspects: molecular properties and the remaining structural context of 3D molecules. Our model ensures explicit control over these molecular attributes while maintaining equivariance of coordinate representation and invariance of data likelihood. Furthermore, we introduce a novel alignment-based coordinate loss to adapt equivariant networks for auto-regressive de-novo 3D molecule generation from scratch. Extensive experiments validate our model's effectiveness on property-guided and context-guided molecule generation, both for de-novo 3D molecule design and structure-based drug discovery against protein targets.
AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling
Authors: Zihan Liu, Yang Chen, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping
Date: 2024-12-19 00:00:00
Tasks: Math

In this paper, we introduce AceMath, a suite of frontier math models that excel in solving complex math problems, along with highly effective reward models capable of evaluating generated solutions and reliably identifying the correct ones. To develop the instruction-tuned math models, we propose a supervised fine-tuning (SFT) process that first achieves competitive performance across general domains, followed by targeted fine-tuning for the math domain using a carefully curated set of prompts and synthetically generated responses. The resulting model, AceMath-72B-Instruct greatly outperforms Qwen2.5-Math-72B-Instruct, GPT-4o and Claude-3.5 Sonnet. To develop math-specialized reward model, we first construct AceMath-RewardBench, a comprehensive and robust benchmark for evaluating math reward models across diverse problems and difficulty levels. After that, we present a systematic approach to build our math reward models. The resulting model, AceMath-72B-RM, consistently outperforms state-of-the-art reward models. Furthermore, when combining AceMath-72B-Instruct with AceMath-72B-RM, we achieve the highest average rm@8 score across the math reasoning benchmarks. We release model weights, training data, and evaluation benchmarks at: https://research.nvidia.com/labs/adlr/acemath
GIRAFE: Glottal Imaging Dataset for Advanced Segmentation, Analysis, and Facilitative Playbacks Evaluation
Authors: G. Andrade-Miranda, K. Chatzipapas, J. D. Arias-Londoño, J. I. Godino-Llorente
Date: 2024-12-19 00:00:00
Tasks: Segmentation, Semantic Segmentation

The advances in the development of Facilitative Playbacks extracted from High-Speed videoendoscopic sequences of the vocal folds are hindered by a notable lack of publicly available datasets annotated with the semantic segmentations corresponding to the area of the glottal gap. This fact also limits the reproducibility and further exploration of existing research in this field. To address this gap, GIRAFE is a data repository designed to facilitate the development of advanced techniques for the semantic segmentation, analysis, and fast evaluation of High-Speed videoendoscopic sequences of the vocal folds. The repository includes 65 high-speed videoendoscopic recordings from a cohort of 50 patients (30 female, 20 male). The dataset comprises 15 recordings from healthy controls, 26 from patients with diagnosed voice disorders, and 24 with an unknown health condition. All of them were manually annotated by an expert, including the masks corresponding to the semantic segmentation of the glottal gap. The repository is also complemented with the automatic segmentation of the glottal area using different state-of-the-art approaches. This data set has already supported several studies, which demonstrates its usefulness for the development of new glottal gap segmentation algorithms from High-Speed-Videoendoscopic sequences to improve or create new Facilitative Playbacks. Despite these advances and others in the field, the broader challenge of performing an accurate and completely automatic semantic segmentation method of the glottal area remains open.
Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI
Authors: Isadora Krsek, Anubha Kabra, Yao Dou, Tarek Naous, Laura A. Dabbish, Alan Ritter, Wei Xu, Sauvik Das
Date: 2024-12-19 00:00:00

In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy risks are more abstract (e.g., will my partner be able to tell that this is me?). Prior work has sought to develop natural language processing (NLP) tools that help users identify potentially risky self-disclosures in their text, but none have been designed for or evaluated with the users they hope to protect. Absent this assessment, these tools will be limited by the social-technical gap: users need assistive tools that help them make informed decisions, not paternalistic tools that tell them to avoid self-disclosure altogether. To bridge this gap, we conducted a study with N = 21 Reddit users; we had them use a state-of-the-art NLP disclosure detection model on two of their authored posts and asked them questions to understand if and how the model helped, where it fell short, and how it could be improved to help them make more informed decisions. Despite its imperfections, users responded positively to the model and highlighted its use as a tool that can help them catch mistakes, inform them of risks they were unaware of, and encourage self-reflection. However, our work also shows how, to be useful and usable, AI for supporting privacy decision-making must account for posting context, disclosure norms, and users' lived threat models, and provide explanations that help contextualize detected risks.
From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security
Authors: Enna Basic, Alberto Giaretta
Date: 2024-12-19 00:00:00
Tasks: Data Poisoning, Systematic Literature Review

Large Language Models (LLMs) have emerged as powerful tools for automating various programming tasks, including security-related ones, such as detecting and fixing vulnerabilities. Despite their promising capabilities, when required to produce or modify pre-existing code, LLMs could introduce vulnerabilities unbeknown to the programmer. When analyzing code, they could miss clear vulnerabilities or signal nonexistent ones. In this Systematic Literature Review (SLR), we aim to investigate both the security benefits and potential drawbacks of using LLMs for a variety of code-related tasks. In particular, first we focus on the types of vulnerabilities that could be introduced by LLMs, when used for producing code. Second, we analyze the capabilities of LLMs to detect and fix vulnerabilities, in any given code, and how the prompting strategy of choice impacts their performance in these two tasks. Last, we provide an in-depth analysis on how data poisoning attacks on LLMs can impact performance in the aforementioned tasks.
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMs
Authors: Pham Vu Tuan Dat, Long Doan, Huynh Thi Thanh Binh
Date: 2024-12-19 00:00:00
Tasks: Combinatorial Optimization, Diversity, Heuristic Search

Automatic Heuristic Design (AHD) is an active research area due to its utility in solving complex search and NP-hard combinatorial optimization problems in the real world. The recent advancements in Large Language Models (LLMs) introduce new possibilities by coupling LLMs with evolutionary computation to automatically generate heuristics, known as LLM-based Evolutionary Program Search (LLM-EPS). While previous LLM-EPS studies obtained great performance on various tasks, there is still a gap in understanding the properties of heuristic search spaces and achieving a balance between exploration and exploitation, which is a critical factor in large heuristic search spaces. In this study, we address this gap by proposing two diversity measurement metrics and perform an analysis on previous LLM-EPS approaches, including FunSearch, EoH, and ReEvo. Results on black-box AHD problems reveal that while EoH demonstrates higher diversity than FunSearch and ReEvo, its objective score is unstable. Conversely, ReEvo's reflection mechanism yields good objective scores but fails to optimize diversity effectively. With this finding in mind, we introduce HSEvo, an adaptive LLM-EPS framework that maintains a balance between diversity and convergence with a harmony search algorithm. Through experimentation, we find that HSEvo achieved high diversity indices and good objective scores while remaining cost-effective. These results underscore the importance of balancing exploration and exploitation and understanding heuristic search spaces in designing frameworks in LLM-EPS.
Movie2Story: A framework for understanding videos and telling stories in the form of novel text
Authors: Kangning Li, Zheyang Jia, Anyu Ying
Date: 2024-12-19 00:00:00
Tasks: Dataset Generation, Fairness, Form, Language Modeling, Language Modelling, Large Language Model, Story Generation, Text Generation

In recent years, large-scale models have achieved significant advancements, accompanied by the emergence of numerous high-quality benchmarks for evaluating various aspects of their comprehension abilities. However, most existing benchmarks primarily focus on spatial understanding in static image tasks. While some benchmarks extend evaluations to temporal tasks, they fall short in assessing text generation under complex contexts involving long videos and rich auxiliary information. To address this limitation, we propose a novel benchmark: the Multi-modal Story Generation Benchmark (MSBench), designed to evaluate text generation capabilities in scenarios enriched with auxiliary information. Our work introduces an innovative automatic dataset generation method to ensure the availability of accurate auxiliary information. On one hand, we leverage existing datasets and apply automated processes to generate new evaluation datasets, significantly reducing manual efforts. On the other hand, we refine auxiliary data through systematic filtering and utilize state-of-the-art models to ensure the fairness and accuracy of the ground-truth datasets. Our experiments reveal that current Multi-modal Large Language Models (MLLMs) perform suboptimally under the proposed evaluation metrics, highlighting significant gaps in their capabilities. To address these challenges, we propose a novel model architecture and methodology to better handle the overall process, demonstrating improvements on our benchmark.
Generalizing Constraint Models in Constraint Acquisition
Authors: Dimos Tsouros, Senne Berden, Steven Prestwich, Tias Guns
Date: 2024-12-19 00:00:00

Constraint Acquisition (CA) aims to widen the use of constraint programming by assisting users in the modeling process. However, most CA methods suffer from a significant drawback: they learn a single set of individual constraints for a specific problem instance, but cannot generalize these constraints to the parameterized constraint specifications of the problem. In this paper, we address this limitation by proposing GenCon, a novel approach to learn parameterized constraint models capable of modeling varying instances of the same problem. To achieve this generalization, we make use of statistical learning techniques at the level of individual constraints. Specifically, we propose to train a classifier to predict, for any possible constraint and parameterization, whether the constraint belongs to the problem. We then show how, for some classes of classifiers, we can extract decision rules to construct interpretable constraint specifications. This enables the generation of ground constraints for any parameter instantiation. Additionally, we present a generate-and-test approach that can be used with any classifier, to generate the ground constraints on the fly. Our empirical results demonstrate that our approach achieves high accuracy and is robust to noise in the input instances.
Cirbo: A New Tool for Boolean Circuit Analysis and Synthesis
Authors: Daniil Averkov, Tatiana Belova, Gregory Emdin, Mikhail Goncharov, Viktoriia Krivogornitsyna, Alexander S. Kulikov, Fedor Kurmazov, Daniil Levtsov, Georgie Levtsov, Vsevolod Vaskin, Aleksey Vorobiev
Date: 2024-12-19 00:00:00

We present an open-source tool for manipulating Boolean circuits. It implements efficient algorithms, both existing and novel, for a rich variety of frequently used circuit tasks such as satisfiability, synthesis, and minimization. We tested the tool on a wide range of practically relevant circuits (computing, in particular, symmetric and arithmetic functions) that have been optimized intensively by the community for the last three years. The tool helped us to win the IWLS 2024 Programming Contest. In 2023, it was Google DeepMind who took the first place in the competition. We were able to reduce the size of the best circuits from 2023 by 12\% on average, whereas for some individual circuits, our size reduction was as large as 83\%.
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
Authors: Junyu Luo, Xiao Luo, Kaize Ding, Jingyang Yuan, Zhiping Xiao, Ming Zhang
Date: 2024-12-19 00:00:00
Tasks: Denoising

Supervised fine-tuning (SFT) plays a crucial role in adapting large language models (LLMs) to specific domains or tasks. However, as demonstrated by empirical experiments, the collected data inevitably contains noise in practical applications, which poses significant challenges to model performance on downstream tasks. Therefore, there is an urgent need for a noise-robust SFT framework to enhance model capabilities in downstream tasks. To address this challenge, we introduce a robust SFT framework (RobustFT) that performs noise detection and relabeling on downstream task data. For noise identification, our approach employs a multi-expert collaborative system with inference-enhanced models to achieve superior noise detection. In the denoising phase, we utilize a context-enhanced strategy, which incorporates the most relevant and confident knowledge followed by careful assessment to generate reliable annotations. Additionally, we introduce an effective data selection mechanism based on response entropy, ensuring only high-quality samples are retained for fine-tuning. Extensive experiments conducted on multiple LLMs across five datasets demonstrate RobustFT's exceptional performance in noisy scenarios.