pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the topics, sentiments and hate speech in the Spanish information environment
Authors: ALEJANDRO BUITRAGO LOPEZ, Javier Pastor-Galindo, José Antonio Ruipérez-Valiente
Date: 2024-09-19 00:00:00

In the digital era, the internet and social media have transformed communication but have also facilitated the spread of hate speech and disinformation, leading to radicalization, polarization, and toxicity. This is especially concerning for media outlets due to their significant role in shaping public discourse. This study examines the topics, sentiments, and hate prevalence in 337,807 response messages (website comments and tweets) to news from five Spanish media outlets (La Vanguardia, ABC, El Pa\'is, El Mundo, and 20 Minutos) in January 2021. These public reactions were originally labeled as distinct types of hate by experts following an original procedure, and they are now classified into three sentiment values (negative, neutral, or positive) and main topics. The BERTopic unsupervised framework was used to extract 81 topics, manually named with the help of Large Language Models (LLMs) and grouped into nine primary categories. Results show social issues (22.22%), expressions and slang (20.35%), and political issues (11.80%) as the most discussed. Content is mainly negative (62.7%) and neutral (28.57%), with low positivity (8.73%). Toxic narratives relate to conversation expressions, gender, feminism, and COVID-19. Despite low levels of hate speech (3.98%), the study confirms high toxicity in online responses to social and political topics.
Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories
Authors: Hikaru Asano, Ryo Yonetani, Taiki Sekii, Hiroki Ouchi
Date: 2024-09-19 00:00:00
Tasks: Management

This paper presents Text2Traj2Text, a novel learning-by-synthesis framework for captioning possible contexts behind shopper's trajectory data in retail stores. Our work will impact various retail applications that need better customer understanding, such as targeted advertising and inventory management. The key idea is leveraging large language models to synthesize a diverse and realistic collection of contextual captions as well as the corresponding movement trajectories on a store map. Despite learned from fully synthesized data, the captioning model can generalize well to trajectories/captions created by real human subjects. Our systematic evaluation confirmed the effectiveness of the proposed framework over competitive approaches in terms of ROUGE and BERT Score metrics.
Exploring Large Language Models for Product Attribute Value Identification
Authors: Kassem Sabeh, Mouna Kacimi, Johann Gamper, Robert Litschko, Barbara Plank
Date: 2024-09-19 00:00:00
Tasks: Attribute, In-Context Learning

Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information, enabling features like product search, recommendation, and comparison. Existing methods primarily rely on fine-tuning pre-trained language models, such as BART and T5, which require extensive task-specific training data and struggle to generalize to new attributes. This paper explores large language models (LLMs), such as LLaMA and Mistral, as data-efficient and robust alternatives for PAVI. We propose various strategies: comparing one-step and two-step prompt-based approaches in zero-shot settings and utilizing parametric and non-parametric knowledge through in-context learning examples. We also introduce a dense demonstration retriever based on a pre-trained T5 model and perform instruction fine-tuning to explicitly train LLMs on task-specific instructions. Extensive experiments on two product benchmarks show that our two-step approach significantly improves performance in zero-shot settings, and instruction fine-tuning further boosts performance when using training data, demonstrating the practical benefits of using LLMs for PAVI.
LLM-Measure: Generating Valid, Consistent, and Reproducible Text-Based Measures for Social Science Research
Authors: Yi Yang, Hanyu Duan, Jiaxin Liu, Kar Yan Tam
Date: 2024-09-19 00:00:00
Tasks: valid

The increasing use of text as data in social science research necessitates the development of valid, consistent, reproducible, and efficient methods for generating text-based concept measures. This paper presents a novel method that leverages the internal hidden states of large language models (LLMs) to generate these concept measures. Specifically, the proposed method learns a concept vector that captures how the LLM internally represents the target concept, then estimates the concept value for text data by projecting the text's LLM hidden states onto the concept vector. Three replication studies demonstrate the method's effectiveness in producing highly valid, consistent, and reproducible text-based measures across various social science research contexts, highlighting its potential as a valuable tool for the research community.
Edu-Values: Towards Evaluating the Chinese Education Values of Large Language Models
Authors: Peiyi Zhang, Yazhou Zhang, Bo wang, Lu Rong, Jing Qin
Date: 2024-09-19 00:00:00
Tasks: Ethics, Multiple-choice, Question Answering

With the recent evolution of large language models (LLMs), concerns about aligning such models with human values have grown. Previous research has primarily focused on assessing LLMs' performance in terms of the Helpful, Honest, Harmless (3H) basic principles, while often overlooking their alignment with educational values in the Chinese context. To fill this gap, we present Edu-Values, the first Chinese education values evaluation benchmark designed to measure LLMs' alignment ability across seven dimensions: professional ideology, cultural literacy, educational knowledge and skills, education laws and regulations, teachers' professional ethics, basic competencies, and subject knowledge. We meticulously design and compile 1,418 questions, including multiple-choice, multi-modal question answering, subjective analysis, adversarial prompts, and questions on traditional Chinese culture. We conduct both human evaluation and automatic evaluation over 11 state-of-the-art (SoTA) LLMs, and highlight three main findings: (1) due to differences in educational culture, Chinese LLMs significantly outperform English LLMs, with Qwen 2 ranking the first with a score of 81.37; (2) LLMs perform well in subject knowledge and teaching skills but struggle with teachers' professional ethics and basic competencies; (3) LLMs excel at multiple-choice questions but perform poorly on subjective analysis and multi-modal tasks. This demonstrates the effectiveness and potential of the proposed benchmark. Our dataset is available at https://github.com/zhangpeii/Edu-Values.git.
Bilingual Evaluation of Language Models on General Knowledge in University Entrance Exams with Minimal Contamination
Authors: Eva Sánchez Salido, Roser Morante, Julio Gonzalo, Guillermo Marco, Jorge Carrillo-de-Albornoz, Laura Plaza, Enrique Amigó, Andrés Fernández, Alejandro Benito-Santos, Adrián Ghajari Espinosa, Victor Fresno
Date: 2024-09-19 00:00:00
Tasks: General Knowledge, MMLU, Multiple-choice

In this article we present UNED-ACCESS 2024, a bilingual dataset that consists of 1003 multiple-choice questions of university entrance level exams in Spanish and English. Questions are originally formulated in Spanish and translated manually into English, and have not ever been publicly released. A selection of current open-source and proprietary models are evaluated in a uniform zero-shot experimental setting both on the UNED-ACCESS 2024 dataset and on an equivalent subset of MMLU questions. Results show that (i) reasoning questions are challenging for models, (ii) smaller models perform worse than larger models and degrade faster in Spanish than in English and (iii) the performance gap between languages is negligible for the best models and grows up to 37% for smaller models. Model ranking on UNED-ACCESS 2024 is almost identical in English and Spanish, and has also a high correlation (0.98 Pearson) with ranking on MMLU, suggesting that a small dataset is sufficiently diverse and representative to measure performance by discipline.
Language Models Learn to Mislead Humans via RLHF
Authors: Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng
Date: 2024-09-19 00:00:00
Tasks: Question Answering

Language models (LMs) can produce errors that are hard to detect for humans, especially when the task is complex. RLHF, the most popular post-training method, may exacerbate this problem: to achieve higher rewards, LMs might get better at convincing humans that they are right even when they are wrong. We study this phenomenon under a standard RLHF pipeline, calling it "U-SOPHISTRY" since it is Unintended by model developers. Specifically, we ask time-constrained (e.g., 3-10 minutes) human subjects to evaluate the correctness of model outputs and calculate humans' accuracy against gold labels. On a question-answering task (QuALITY) and programming task (APPS), RLHF makes LMs better at convincing our subjects but not at completing the task correctly. RLHF also makes the model harder to evaluate: our subjects' false positive rate increases by 24.1% on QuALITY and 18.3% on APPS. Finally, we show that probing, a state-of-the-art approach for detecting Intended Sophistry (e.g. backdoored LMs), does not generalize to U-SOPHISTRY. Our results highlight an important failure mode of RLHF and call for more research in assisting humans to align them.
Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models
Authors: Muhammad Raees, Samina Fazilat
Date: 2024-09-19 00:00:00
Tasks: Sentiment Analysis

Sentiment analysis possesses the potential of diverse applicability on digital platforms. Sentiment analysis extracts the polarity to understand the intensity and subjectivity in the text. This work uses a lexicon-based method to perform sentiment analysis and shows an evaluation of classification models trained over textual data. The lexicon-based methods identify the intensity of emotion and subjectivity at word levels. The categorization identifies the informative words inside a text and specifies the quantitative ranking of the polarity of words. This work is based on a multi-class problem of text being labeled as positive, negative, or neutral. Twitter sentiment dataset containing 1.6 million unprocessed tweets is used with lexicon-based methods like Text Blob and Vader Sentiment to introduce the neutrality measure on text. The analysis of lexicons shows how the word count and the intensity classify the text. A comparative analysis of machine learning models, Naiive Bayes, Support Vector Machines, Multinomial Logistic Regression, Random Forest, and Extreme Gradient (XG) Boost performed across multiple performance metrics. The best estimations are achieved through Random Forest with an accuracy score of 81%. Additionally, sentiment analysis is applied for a personality judgment case against a Twitter profile based on online activity.
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning
Authors: Peichao Lai, Zhengfeng Zhang, Wentao Zhang, Fangcheng Fu, Bin Cui
Date: 2024-09-19 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Diversity, Knowledge Graphs, Semantic Textual Similarity, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, STS

Recently, using large language models (LLMs) for data augmentation has led to considerable improvements in unsupervised sentence embedding models. However, existing methods encounter two primary challenges: limited data diversity and high data noise. Current approaches often neglect fine-grained knowledge, such as entities and quantities, leading to insufficient diversity. Additionally, unsupervised data frequently lacks discriminative information, and the generated synthetic samples may introduce noise. In this paper, we propose a pipeline-based data augmentation method via LLMs and introduce the Gaussian-decayed gradient-assisted Contrastive Sentence Embedding (GCSE) model to enhance unsupervised sentence embeddings. To tackle the issue of low data diversity, our pipeline utilizes knowledge graphs (KGs) to extract entities and quantities, enabling LLMs to generate more diverse, knowledge-enriched samples. To address high data noise, the GCSE model uses a Gaussian-decayed function to limit the impact of false hard negative samples, enhancing the model's discriminative capability. Experimental results show that our approach achieves state-of-the-art performance in semantic textual similarity (STS) tasks, using fewer data samples and smaller LLMs, demonstrating its efficiency and robustness across various models.
LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning
Authors: Jin Jiang, Yuchen Yan, Yang Liu, Yonggang Jin, Shuai Peng, Mengdi Zhang, Xunliang Cai, Yixin Cao, Liangcai Gao, Zhi Tang
Date: 2024-09-19 00:00:00
Tasks: GSM8K, Logical Reasoning, RTE

In this paper, we present a novel approach, called LogicPro, to enhance Large Language Models (LLMs) complex Logical reasoning through Program Examples. We do this effectively by simply utilizing widely available algorithmic problems and their code solutions. First, we constructed diverse test samples input based on algorithmic questions and code solutions. Then, we designed different complex reasoning questions based on algorithmic problems and test samples. Finally, combining the intermediate variable outputs of the code solutions and the complex reasoning questions, we derived the reasoning process and the final answer. With this approach, we can construct a dataset that is sufficiently difficult (all models are ineffective), diverse (synthesized from 2,360 different algorithmic questions), and scalable (building different test samples and collecting more algorithmic questions). In addition, we obtain a high-quality reasoning process guided by the values of intermediate variables. As a result, our approach achieves significant improvements in multiple models for the BBH$^{27}$, GSM8K, HellSwag, Logicqa, Reclor, and RTE datasets, outperforming a wide range of existing reasoning datasets.
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
Authors: Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, Manaal Faruqui
Date: 2024-09-19 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augmented generation (RAG) capabilities. These systems require LLMs to understand user queries, retrieve relevant information, and synthesize coherent and accurate responses. Given the increasing real-world deployment of such systems, comprehensive evaluation becomes crucial. To this end, we propose FRAMES (Factuality, Retrieval, And reasoning MEasurement Set), a high-quality evaluation dataset designed to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers. While previous work has provided datasets and benchmarks to evaluate these abilities in isolation, FRAMES offers a unified framework that provides a clearer picture of LLM performance in end-to-end RAG scenarios. Our dataset comprises challenging multi-hop questions that require the integration of information from multiple sources. We present baseline results demonstrating that even state-of-the-art LLMs struggle with this task, achieving 0.40 accuracy with no retrieval. The accuracy is significantly improved with our proposed multi-step retrieval pipeline, achieving an accuracy of 0.66 (>50% improvement). We hope our work will help bridge evaluation gaps and assist in developing more robust and capable RAG systems.
CLAIR-A: Leveraging Large Language Models to Judge Audio Captions
Authors: Tsung-Han Wu, Joseph E. Gonzalez, Trevor Darrell, David M. Chan
Date: 2024-09-19 00:00:00
Tasks: Audio captioning, Language Modeling, Language Modelling, Scene Understanding

The Automated Audio Captioning (AAC) task asks models to generate natural language descriptions of an audio input. Evaluating these machine-generated audio captions is a complex task that requires considering diverse factors, among them, auditory scene understanding, sound-object inference, temporal coherence, and the environmental context of the scene. While current methods focus on specific aspects, they often fail to provide an overall score that aligns well with human judgment. In this work, we propose CLAIR-A, a simple and flexible method that leverages the zero-shot capabilities of large language models (LLMs) to evaluate candidate audio captions by directly asking LLMs for a semantic distance score. In our evaluations, CLAIR-A better predicts human judgements of quality compared to traditional metrics, with a 5.8% relative accuracy improvement compared to the domain-specific FENSE metric and up to 11% over the best general-purpose measure on the Clotho-Eval dataset. Moreover, CLAIR-A offers more transparency by allowing the language model to explain the reasoning behind its scores, with these explanations rated up to 30% better by human evaluators than those provided by baseline methods. CLAIR-A is made publicly available at https://github.com/DavidMChan/clair-a.
Robust Audiovisual Speech Recognition Models with Mixture-of-Experts
Authors: Yihan Wu, Yifan Peng, Yichen Lu, Xuankai Chang, Ruihua Song, Shinji Watanabe
Date: 2024-09-19 00:00:00
Tasks: Mixture-of-Experts, Robust Speech Recognition, speech-recognition, Speech Recognition

Visual signals can enhance audiovisual speech recognition accuracy by providing additional contextual information. Given the complexity of visual signals, an audiovisual speech recognition model requires robust generalization capabilities across diverse video scenarios, presenting a significant challenge. In this paper, we introduce EVA, leveraging the mixture-of-Experts for audioVisual ASR to perform robust speech recognition for ``in-the-wild'' videos. Specifically, we first encode visual information into visual tokens sequence and map them into speech space by a lightweight projection. Then, we build EVA upon a robust pretrained speech recognition model, ensuring its generalization ability. Moreover, to incorporate visual information effectively, we inject visual information into the ASR model through a mixture-of-experts module. Experiments show our model achieves state-of-the-art results on three benchmarks, which demonstrates the generalization ability of EVA across diverse video domains.
A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
Authors: Hakan T. Otal, Stephen V. Faraone, M. Abdullah Canbaz
Date: 2024-09-19 00:00:00
Tasks: Chatbot, graph construction, RAG, Retrieval

Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors. To explore how we can gain deeper insights on this topic, we performed a network analysis on a comprehensive knowledge graph (KG) of ADHD, constructed by integrating scientific literature and clinical data with the help of cutting-edge large language models. The analysis, including k-core techniques, identified critical nodes and relationships that are central to understanding the disorder. Building on these findings, we curated a knowledge graph that is usable in a context-aware chatbot (Graph-RAG) with Large Language Models (LLMs), enabling accurate and informed interactions. Our knowledge graph not only advances the understanding of ADHD but also provides a powerful tool for research and clinical applications.
Mitigating Unsafe Feedback with Learning Constraints
Authors: Domenic Rosati, Giles Edkins, Harsh Raj, David Atanasov, Subhabrata Majumdar, Janarthanan Rajendran, Frank Rudzicz, Hassan Sajjad
Date: 2024-09-19 00:00:00
Tasks: Safety Alignment, Text Generation

While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety-guards can easily be removed when fine-tuned on unsafe and harmful datasets.While this setting has been treated extensively, another popular training paradigm, learning from unsafe feedback with reinforcement learning, has previously been unexplored. This is concerning due to the widespread deployment of feedback collection systems. We address this gap by providing an analysis of learning settings where feedback is adversarial and noisy, i.e. that unsafe samples are preferred over safe ones despite model developers goal to maintain safety. We find that safety-aligned LLMs easily explore unsafe action spaces through generating harmful text and optimize for adversarial reward indicating that current safety guards are not enough to prevent learning from unsafe feedback. In order to protect against this vulnerability, we adapt a number of both "implict" and "explicit" harmful fine-tuning defences to evaluate whether they are effective as learning constraints in an RL setting finding that no method is generally effective pointing to the need for more research in defences given the widespread adoption of methods designed to learn from feedback. We end the paper with the observation that some defences work by performing "harmless reward hacking" for which we provide a theoretical explanation drawn from the theory of Constrained Markov Decision Processes and provide some direction for future defence development.
A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
Authors: Falguni Roy, Xiaofeng Ding, K. -K. R. Choo, Pan Zhou
Date: 2024-09-19 00:00:00
Tasks: Fairness, Privacy Preserving, Recommendation Systems

Recommender systems are essential for personalizing digital experiences on e-commerce sites, streaming services, and social media platforms. While these systems are necessary for modern digital interactions, they face fairness, bias, threats, and privacy challenges. Bias in recommender systems can result in unfair treatment of specific users and item groups, and fairness concerns demand that recommendations be equitable for all users and items. These systems are also vulnerable to various threats that compromise reliability and security. Furthermore, privacy issues arise from the extensive use of personal data, making it crucial to have robust protection mechanisms to safeguard user information. This study explores fairness, bias, threats, and privacy in recommender systems. It examines how algorithmic decisions can unintentionally reinforce biases or marginalize specific user and item groups, emphasizing the need for fair recommendation strategies. The study also looks at the range of threats in the form of attacks that can undermine system integrity and discusses advanced privacy-preserving techniques. By addressing these critical areas, the study highlights current limitations and suggests future research directions to improve recommender systems' robustness, fairness, and privacy. Ultimately, this research aims to help develop more trustworthy and ethical recommender systems that better serve diverse user populations.