GenAI4ELab/papercli-papers huggingface dataset:
Hate Explained: Evaluating NER-Enriched Text in Human and Machine Moderation of Hate Speech
Authors: Andrés Carvallo; Marcelo Mendoza; Miguel Fernández; Maximiliano Ojeda; Lilly Guevara; Diego Varela; Martín Bórquez; Nicolás Buzeta; Felipe Ayala
Venue: ACL 2025 (Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH))

Abstract: Hate speech detection is vital for creating safe online environments, as harmful content can drive social polarization. This study explores the impact of enriching text with intent and group tags on machine performance and human moderation workflows. For machine performance, we enriched text with intent and group tags to train hate speech classifiers. Intent tags were the most effective, achieving state-of-the-art F1-score improvements on the IHC, SBIC, and DH datasets, respectively. Cross-dataset evaluations further demonstrated the superior generalization of intent-tagged models compared to other pre-trained approaches. Then, through a user study (N=100), we evaluated seven moderation settings, including intent tags, group tags, model probabilities, and randomized counterparts. Intent annotations significantly improved the accuracy of the moderators, allowing them to outperform machine classifiers by 12.9%. Moderators also rated intent tags as the most useful explanation tool, with a 41% increase in perceived helpfulness over the control group. Our findings demonstrate that intent-based annotations enhance both machine classification performance and human moderation workflows.
Personas with Attitudes: Controlling LLMs for Diverse Data Annotation
Authors: Leon Fröhling; Gianluca Demartini; Dennis Assenmacher
Venue: ACL 2025 (Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH))

Abstract: We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investigate the impact of injecting diverse persona descriptions into LLM prompts across two studies, exploring whether personas increase annotation diversity and whether the impacts of individual personas on the resulting annotations are consistent and controllable. Our results indicate that persona-prompted LLMs generate more diverse annotations than LLMs prompted without personas, and that the effects of personas on LLM annotations align with subjective differences in human annotations. These effects are both controllable and repeatable, making our approach a valuable tool for enhancing data annotation in subjective NLP tasks such as toxicity detection.
Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt. Generation for Enhanced LLM Content Moderation
Authors: Daniel Schwarz; Dmitriy Bespalov; Zhe Wang; Ninad Kulkarni; Yanjun Qi
Venue: ACL 2025 (Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH))

Abstract: As large language models (LLMs) become increasingly prevalent, ensuring their robustness against adversarial misuse is crucial. This paper introduces the GAP (Graph of Attacks with Pruning) framework, an advanced approach for generating stealthy jailbreak prompts to evaluate and enhance LLM safeguards. GAP addresses limitations in existing tree-based methods by implementing an interconnected graph structure that enables knowledge sharing across attack paths. Our experimental evaluation demonstrates GAP’s superiority over existing techniques, achieving a 20.8% increase in attack success rates while reducing query costs by 62.7%. GAP consistently outperforms state-of-the-art methods across various open and closed LLMs, with attack success rates of 96%. Additionally, we present specialized variants like GAP-Auto for automated seed generation and GAP-VLM for multimodal attacks. GAP-generated prompts prove highly effective in improving content moderation systems, increasing true positive detection rates by 108.5% and accuracy by 183.6% when used for fine-tuning.
A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance
Authors: Matteo Melis; Gabriella Lapesa; Dennis Assenmacher
Venue: ACL 2025 (Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH))

Abstract: Detecting harmful content is a crucial task in the landscape of NLP applications for Social Good, with hate speech being one of its most dangerous forms. But what do we mean by hate speech, how can we define it and how does prompting different definitions of hate speech affect model performance? The contribution of this work is twofold. At the theoretical level, we address the ambiguity surrounding hate speech by collecting and analyzing existing definitions from the literature. We organize these definitions into a taxonomy of 14 conceptual elements—building blocks that capture different aspects of hate speech definitions, such as references to the target of hate. At the experimental level, we employ the collection of definitions in a systematic zero-shot evaluation of three LLMs, on three hate speech datasets representing different types of data (synthetic, human-in-the-loop, and real-world). We find that choosing different definitions, i.e., definitions with a different degree of specificity in terms of encoded elements, impacts model performance, but this effect is not consistent across all architectures.
Red-Teaming for Uncovering Societal Bias in Large Language Models
Authors: Chu Fei Luo; Ahmad Ghawanmeh; Kashyap Coimbatore Murali; Bhimshetty Bharat Kumar; Murli Jadhav; Xiaodan Zhu; Faiza Khan Khattak
Venue: ACL 2025 (Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH))

Abstract: Ensuring the safe deployment of AI systems is critical in industry settings where biased outputs can lead to significant operational, reputational, and regulatory risks. Thorough evaluation before deployment is essential to prevent these hazards. Red-teaming addresses this need by employing adversarial attacks to develop guardrails that detect and reject biased or harmful queries, enabling models to be retrained or steered away from harmful outputs. However, red-teaming techniques are often limited, and malicious actors may discover new vulnerabilities that bypass safety fine-tuning, underscoring the need for ongoing research and innovative approaches. Notably, most red-teaming efforts focus on harmful or unethical instructions rather than addressing social bias, leaving this critical area under-explored despite its significant real-world impact, especially in customer-facing AI systems. We propose two bias-specific red-teaming methods, Emotional Bias Probe (EBP) and BiasKG, to evaluate how standard safety measures for harmful content mitigate bias. For BiasKG, we refactor natural language stereotypes into a knowledge graph. and use adversarial attacking strategies to induce biased responses from several open- and closed-source language models. We find our method increases bias in all models, even those trained with safety guardrails. Our work emphasizes uncovering societal bias in LLMs through rigorous evaluation, addressing adversarial challenges to ensure AI safety in high-stakes industry deployments.
Using LLMs and Preference Optimization for Agreement-Aware HateWiC Classification
Authors: Sebastian Loftus; Adrian Mülthaler; Sanne Hoeken; Sina Zarrieß; Ozge Alacam
Venue: ACL 2025 (Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH))

Abstract: Annotator disagreement poses a significant challenge in subjective tasks like hate speech detection. In this paper, we introduce a novel variant of the HateWiC task that explicitly models annotator agreement by estimating the proportion of annotators who classify the meaning of a term as hateful. To tackle this challenge, we explore the use of Llama 3 models fine-tuned through Direct Preference Optimization (DPO). Our experiments show that while LLMs perform well for majority-based hate classification, they struggle with the more complex agreement-aware task. DPO fine-tuning offers improvements, particularly when applied to instruction-tuned models. Yet, our results emphasize the need for improved modeling of subjectivity in hate classification and this study can serve as foundation for future advancements.
Wikivecs: A Fully Reproducible Vectorization of Multilingual Wikipedia
Authors: Brandon Duderstadt
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Advancing Natural Language Processing for Wikipedia (WikiNLP 2025))

Abstract: Dense vector representations have become foundational to modern natural language processing (NLP), powering diverse workflows from semantic search and retrieval augmented generation to content comparison across languages. Although Wikipedia is one of the most comprehensive and widely used datasets in modern NLP research, it lacks a fully reproducible and permissively licensed dense vectorization.In this paper, we present Wikivecs, a fully reproducible, permissively licensed dataset containing dense vector embeddings for every article in Multilingual Wikipedia. Our pipeline leverages a fully reproducible and permissively licensed multilingual text encoder to embed Wikipedia articles into a unified vector space, making it easy to compare and analyze content across languages.Alongside these vectors, we release a two-dimensional data map derived from the vectors, enabling visualization and exploration of Multilingual Wikipedia’s content landscape.We demonstrate the utility of our dataset by identifying several content gaps between English and Russian Wikipedia.
WETBench: A Benchmark for Detecting Task-Specific Machine-Generated Text on Wikipedia
Authors: Gerrit Quaremba; Elizabeth Black; Denny Vrandecic; Elena Simperl
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Advancing Natural Language Processing for Wikipedia (WikiNLP 2025))

Abstract: Given Wikipedia’s role as a trusted source of high-quality, reliable content, there are growing concerns about the proliferation of low-quality machine-generated text (MGT) produced by large language models (LLMs) on its platform. Reliable detection of MGT is therefore essential, yet existing work primarily evaluates MGT detectors on generic generation tasks, rather than on tasks more commonly performed by Wikipedia editors. This misalignment can lead to poor generalisability when applied to real-world Wikipedia contexts.We introduce WETBench, a multilingual, multi-generator, and task-specific benchmark for MGT detection. We define three editing tasks empirically grounded in Wikipedia editors’ perceived use cases for LLM-assisted editing: Paragraph Writing, Summarisation, and Text Style Transfer, which we implement using two new datasets across three languages. For each writing task, we evaluate three prompts, produce MGT across multiple generators using the best-performing prompt, and benchmark diverse detectors.We find that, across settings, training-based detectors achieve an average accuracy of 78%, while zero-shot detectors average 58%. These results demonstrate that detectors struggle with MGT in realistic generation scenarios and underscore the importance of evaluating such models on diverse, task-specific data to assess their reliability in editor-driven contexts.
Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset
Authors: Rawan Bondok; Mayar Nassar; Salam Khalifa; Kurt Micallef; Nizar Habash
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Advancing Natural Language Processing for Wikipedia (WikiNLP 2025))

Abstract: Proper nouns in Arabic Wikipedia are frequently undiacritized, creating ambiguity in pronunciation and interpretation, especially for transliterated named entities of foreign origin. While transliteration and diacritization have been well-studied separately in Arabic NLP, their intersection remains underexplored. In this paper, we introduce a new manually diacritized dataset of Arabic proper nouns of various origins with their English Wikipedia equivalent glosses, and present the challenges and guidelines we followed to create it. We benchmark GPT-4o on the task of recovering full diacritization given the undiacritized Arabic and English forms, and analyze its performance. Achieving 73% accuracy, our results underscore both the difficulty of the task and the need for improved models and resources. We release our dataset to facilitate further research on Arabic Wikipedia proper noun diacritization.
Fine-Tuning Large Language Models for Relation Extraction within a Retrieval-Augmented Generation Framework
Authors: Sefika Efeoglu; Adrian Paschke
Venue: ACL 2025 (Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025))

Abstract: Information Extraction (IE) plays a pivotal role in transforming unstructured data into structured formats, such as Knowledge Graphs. One of the main tasks within IE is Relation Extraction (RE), which identifies relations between entities in text data. This process enriches the semantic understanding of documents, enabling more precise information retrieval and query answering. Recent works leveraging pre-trained language models have demonstrated significant performance improvements in RE. In the current era of Large Language Models (LLMs), fine-tuning these LLMs can mitigate the limitations of zero-shot RE methods, particularly in overcoming the domain adaptation challenges inherent in RE. This work explores not only the effectiveness of fine-tuned LLMs but also their integration into a Retrieval-Augmented Generation (RAG)-based RE approach to address domain adaptation challenges when general-purpose LLMs serve as generators within the RAG framework. Empirical evaluations on the TACRED, TACRED-Revisited (TACREV), and Re-TACRED datasets reveal substantial performance improvements with fine-tuned LLMs, such as Llama2-7B, Mistral-7B, and Flan-T5 Large and surpass previous methods on these datasets.
Benchmarking Table Extraction: Multimodal LLMs vs Traditional OCR
Authors: Guilherme Nunes; Vitor Rolla; Duarte Pereira; Vasco Alves; Andre Carreiro; Márcia Baptista
Venue: ACL 2025 (Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025))

Abstract: This paper compares two approaches for table extraction from images: deep learning computer vision and Multimodal Large Language Models (MLLMs). Computer vision models for table extraction, such as the Table Transformer model (TATR), have enhanced the extraction of complex table structural layouts by leveraging deep learning for precise structural recognition combined with traditional Optical Character Recognition (OCR). Conversely, MLLMs, which process both text and image inputs, present a novel approach by potentially bypassing the limitations of TATR plus OCR methods altogether. Models such as GPT-4o, Phi-3 Vision, and Granite Vision 3.2 demonstrate the potential of MLLMs to analyze and interpret table images directly, offering enhanced accuracy and robust extraction capabilities. A state-of-the-art metric like Grid Table Similarity (GriTS) evaluated these methodologies, providing nuanced insights into structural and text content effectiveness. Utilizing the PubTables-1M dataset, a comprehensive and widely used benchmark in the field, this study highlights the strengths and limitations of each approach, setting the stage for future innovations in table extraction technologies. Deep learning computer vision techniques still have a slight edge when extracting table structural layout, but in terms of text cell content, MLLMs are far better.
Injecting Structured Knowledge into LLMs via Graph Neural Networks
Authors: Zichao Li; Zong Ke; Puning Zhao
Venue: ACL 2025 (Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025))

Abstract: Large language models (LLMs) have achieved remarkable success in natural language processing (NLP), but they often struggle to capture explicit linguistic structures and world knowledge. To address this limitation, we propose a hybrid model that integrates LLMs with graph neural networks (GNNs) to inject structured knowledge into NLP tasks. Our approach leverages the strengths of both components: LLMs provide rich contextual representations, while GNNs encode explicit structural priors from sources such as dependency trees, Abstract Meaning Representations (AMRs), and knowledge graphs. We evaluate the hybrid model on a diverse set of tasks, including semantic parsing, multi-hop question answering, text summarization, commonsense reasoning, and dependency parsing. Experimental results demonstrate consistent improvements over both standalone baselines and state-of-the-art methods, with relative gains of up to 2.3% in Exact Match scores for multi-hop QA and 1.7% in accuracy for commonsense reasoning. Ablation studies and sensitivity analyses further highlight the importance of balancing contextual and structural information. By bridging the gap between unstructured textual data and structured knowledge, our work advances the state of the art in NLP and paves the way for more interpretable and robust language models.
Regular-pattern-sensitive CRFs for Distant Label Interactions
Authors: Sean Papay; Roman Klinger; Sebastian Padó
Venue: ACL 2025 (Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025))

Abstract: While LLMs have grown popular in sequence labeling, linear-chain conditionalrandom fields (CRFs) remain a popular alternativewith the ability to directly model interactions between labels.However, the Markov assumption limits them to interactions between adjacent labels.Weighted finite-state transducers (FSTs), in contrast, can modeldistant label–label interactions, but exact label inference is intractable in general.In this work, we present regular-pattern-sensitiveCRFs (RPCRFs), a method of enriching standardlinear-chain CRFs with the ability to learnlong-distance label interactions through user-specified patterns.This approach allows users to write regular-expressionlabel patterns concisely specifying which types of interactionsthe model should take into account, allowingthe model to learn from data whether and inwhich contexts these patterns occur. The resultcan be interpreted alternatively as a CRF augmented with additional,non-local potentials,or as a finite-state transducer whose structureis defined by a set of easily-interpretable patterns.Critically, exact training and inferenceare tractable for many pattern sets. We detailhow an RPCRF can be automatically constructed from a set of user-specified patterns,and demonstrate the model’s effectiveness ona sequence of three synthetic sequence modeling datasets.
From Syntax to Semantics: Evaluating the Impact of Linguistic Structures on LLM-Based Information Extraction
Authors: Anushka Swarup; Avanti Bhandarkar; Ronald Wilson; Tianyu Pan; Damon Woodard
Venue: ACL 2025 (Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025))

Abstract: Large Language Models (LLMs) have brought significant breakthroughs across all areas of Natural Language Processing (NLP), including Information Extraction (IE). However, knowledge gaps remain regarding their effectiveness in extracting entity-relation triplets, i.e. Joint Relation Extraction (JRE). JRE has been a key operation in creating knowledge bases that can be used to enhance Retrieval Augmented Generation (RAG) systems. Prior work highlights low-quality triplets generated by LLMs. Thus, this work investigates the impact of incorporating linguistic structures, such as constituency and dependency trees and semantic role labeling, to enhance the quality of the extracted triplets. The findings suggest that incorporating specific structural information enhances the uniqueness and topical relevance of the triplets, particularly in scenarios where multiple relationships are present.
Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language Models
Authors: Bram Willemsen; Gabriel Skantze
Venue: ACL 2025 (Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025))

Abstract: In this paper, we explore the use of a text-only, autoregressive language modeling approach for the extraction of referring expressions from visually grounded dialogue. More specifically, the aim is to investigate the extent to which the linguistic context alone can inform the detection of mentions that have a (visually perceivable) referent in the visual context of the conversation. To this end, we adapt a pretrained large language model (LLM) to perform a relatively course-grained annotation of mention spans in unfolding conversations by demarcating mention span boundaries in text via next-token prediction. Our findings indicate that even when using a moderately sized LLM, relatively small datasets, and parameter-efficient fine-tuning, a text-only approach can be effective, highlighting the relative importance of the linguistic context for this task. Nevertheless, we argue that the task represents an inherently multimodal problem and discuss limitations fundamental to unimodal approaches.
Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis
Authors: Daoyang Li; Haiyan Zhao; Qingcheng Zeng; Mengnan Du
Venue: ACL 2025 (Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025))

Abstract: Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of other world’s languages. In this paper, we extend these probing methods to a multilingual context, investigating how LLMs encode linguistic structures across diverse languages. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages. Our key findings reveal: (1) a consistent performance gap between high-resource and low-resource languages, with high-resource languages achieving significantly higher probing accuracy; (2) divergent layer-wise accuracy trends, where high-resource languages show substantial improvement in deeper layers similar to English; and (3) higher representational similarities among high-resource languages, with low-resource languages demonstrating lower similarities both among themselves and with high-resource languages. These results provide insights into how linguistic structures are represented differently across languages in LLMs and emphasize the need for improved structure modeling for low-resource languages.
Self-Contrastive Loop of Thought Method for Text-to-SQL Based on Large Language Model
Authors: Fengrui Kang; Mingxi Tan; Xianying Huang; Shiju Yang
Venue: ACL 2025 (Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025))

Abstract: Text-to-SQL is a task with excellent prospects and challenges, and it aims to convert natural language queries (NL) into corresponding structured query language (SQL) statements. The main challenge of this task is how to efficiently transform unstructured data and structured data. In recent years, the emergence of large language models (LLMs) has further promoted the development of this field. However, current LLM-based text-to-SQL methods rely on specific few-shot example construction, resulting in poor performance across domains. To solve this problem, we propose a text-to-SQL method of self-contrastive loop of thought structure. This method designs the LLM inference process as a loop structure based on the comparison of positive and negative examples. The model optimizes the generated results through continuous verification and error correction, greatly improving accuracy and reducing dependence on few-shot example construction. The experimental results on SPIDER and BIRD datasets show that this method can generate SQL with higher precision without relying on few-shot example construction.