Austria

Masculine discourse words are discourse terms that are both socially normative and statistically associated with male speakers. We propose a twofold framework for (i) the large-scale discovery and analysis of gendered discourse words in spoken content via our Gendered Discourse Correlation Framework; and (ii) the measurement of the gender bias associated with these words in LLMs via our Discourse Word-Embedding Association Test. We focus our study on podcasts, a popular and growing form of social media, analyzing 15,117 podcast episodes. We analyze correlations between gender and discourse words -- discovered via LDA and BERTopic. We then find that gendered discourse-based masculine defaults exist in the domains of business, technology/politics, and video games, indicating that these gendered discourse words are socially influential. Next, we study the representation of these words from a state-of-the-art LLM embedding model from OpenAI, and find that the masculine discourse words have a more stable and robust representation than the feminine discourse words, which may result in better system performance on downstream tasks for men. Hence, men are rewarded for their discourse patterns with better system performance -- and this embedding disparity constitutes a representational harm and a masculine default.

ACL 2025

Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

workshop paper

Courtrooms are places where lives are determined and fates are sealed, yet they are not impervious to manipulation. Strategic use of manipulation in legal jargon can sway the opinions of judges and affect the decisions. Despite the growing advancements in NLP, its application in detecting and analyzing manipulation within the legal domain remains largely unexplored. Our work addresses this gap by introducing LegalCon, a dataset of 1,063 annotated courtroom conversations labeled for manipulation detection, identification of primary manipulators, and classification of manipulative techniques, with a focus on long conversations. Furthermore, we propose CLAIM, a two-stage, Intent-driven Multi-agent framework designed to enhance manipulation analysis by enabling context-aware and informed decision-making. Our results highlight the potential of incorporating agentic frameworks to improve fairness and transparency in judicial processes. We hope that this contributes to the broader application of NLP in legal discourse analysis and the development of robust tools to support fairness in legal decision-making. Our code and data are available at \href{https://github.com/Disha1001/CLAIM}{CLAIM}.

Unmasking the Strategists: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues

In this study, we address the challenge of consistently following emotional support strategies in long conversations by large language models (LLMs). We introduce the Strategy-Relevant Attention (SRA) metric, a model-agnostic measure designed to evaluate the effectiveness of LLMs in adhering to strategic prompts in emotional support contexts. By analyzing conversations within the Emotional Support Conversations dataset (ESConv) using LLaMA models, we demonstrate that SRA is significantly correlated with a model's ability to sustain the outlined strategy throughout the interactions. Our findings reveal that the application of SRA-informed prompts leads to enhanced strategic adherence, resulting in conversations that more reliably exhibit the desired emotional support strategies over longer conversations. Furthermore, we contribute a comprehensive, multi-branch synthetic conversation dataset for ESConv, featuring a variety of strategy continuations informed by our optimized prompting method. The code and data are publicly available on our Github.

Steering Conversational Large Language Models for Long Emotional Support Conversations

Designing effective LLMs for social influence (SI) tasks demands controlling linguistic output such that it adapts to context (such as user attributes, history etc.) while upholding ethical guardrails. Standard Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA struggle to manage the trade-off between adaptive linguistic expression and safety and they optimize based on overall objectives without differentiating the functional roles of internal model components. Therefore, we introduce Probing-Guided PEFT (PG-PEFT), a novel fine-tuning strategy which utilizes interpretability probes to identify LLM components associated with context-driven linguistic variations versus those linked to safety violations (e.g., toxicity, bias). This functional map then guides LoRA updates, enabling more targeted control over the model's linguistic output. We evaluate PG-PEFT on SI tasks (persuasion, negotiation) and linguistic adaptability with safety benchmarks against standard PEFT.

Extended Abstract: Probing-Guided Parameter-Efficient Fine-Tuning for Balancing Linguistic Adaptation and Safety in LLM-based Social Influence Systems

Predicting outcomes in multi-turn dialogues is challenging due to the implicit nature of decision-making and the evolving dynamics between participants. In this work, we explore whether LLM-generated intentions and summaries can facilitate outcome prediction in task-oriented dialogues. These intentions and summaries are designed to capture information at both the utterance (local) and the dialogue (global) level respectively. We evaluate both open-sourced and proprietary LLMs in an in-context-learning setup, to predict the final sale price of an ensuing negotiation at different stages of the conversation. Preliminary results suggest that models augmented with these local and global information can better capture negotiation strategies and concession patterns, thereby improving outcome prediction.



FOLIAGE: Forecasting Outcomes by leveraging Local Intentions and Global Evidence in conversations

This paper introduces Interactive Tables (iTBLS), a dataset of interactive conversations that focuses on natural-language manipulation of tabular information sourced from academic pre-prints on ArXiv. The iTBLS dataset consists of three types of tabular tasks -- interpretation, modification, and generation. Interpretation focuses on tabular understanding, modification focuses on manipulating tabular information, and generation focuses on the addition of new natural-language evidence. In addition, the paper presents a novel framework that reformulates tabular operations as question-answering, where an appropriate question is formulated based on the nature of interaction and the question is answered using the user request as evidence. The developed approach results in an improvement on all tasks on a sequence-to-sequence modeling baseline on iTBLS. In addition, the question-answering-based reformulation is applied to datasets from prior work for the text-to-table task where textual paragraphs are summarized into tables. The novel approach results in up to 13% improvement in Exact-Match accuracy and up to 16% improvement in BERTScores compared to the prior state-of-the-art.

iTBLS: A Dataset of Interactive Conversations Over Tabular Information

Recent advances in open-domain question answering over tables have widely adopted large language models (LLMs) under the Retriever-Reader architecture. Prior works have effectively leveraged LLMs to tackle the complex reasoning demands of the Reader component, such as text-to-text, text-to-SQL, and multi-hop reasoning. In contrast, the Retriever component has primarily focused on optimizing the query representation—training retrievers to retrieve relevant tables based on questions, or to select keywords from questions for matching table segments. However, little attention has been given to enhancing how tables themselves are represented in embedding space to better align with questions. To address this, we propose QGpT (Question Generation from Partial Tables), a simple yet effective method that uses an LLM to generate synthetic questions based on small portions of a table. These questions are generated to simulate how a user might query the content of the table currently under consideration. The generated questions are then jointly embedded with the partial table segments used for generation, enhancing semantic alignment with user queries. Without the need to embed entire tables, our method significantly improves retrieval performance across multiple benchmarks for both dense and late-interaction retrievers.

Improving Table Retrieval with Question Generation from Partial Tables

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), yet achieving high performance for low-resource languages remains challenging due to limited annotated data and linguistic complexity. Ukrainian exemplifies these issues with its rich morphology and scarce NLP resources. Recent advances in Large Language Models (LLMs) demonstrate their ability to generalize across diverse languages and domains, offering promising solutions without extensive annotations. This research explores adapting state-of-the-art LLMs to Ukrainian through prompt engineering, including chain-of-thought (CoT) strategies, and model refinement via Supervised Fine-Tuning (SFT). Our best model achieves 0.89 F1 on the NER-UK 2.0 benchmark, matching the performance of advanced encoder-only baselines. These findings highlight practical pathways for improving NER in low-resource contexts, promoting more accessible and scalable language technologies.

Improving Named Entity Recognition for Low-Resource Languages Using Large Language Models: A Ukrainian Case Study

We developed a methodology and a framework for automatically evaluating and filtering large-scale parallel corpora for neural machine translation (NMT). We applied six modern Quality Estimation (QE) models to score 55 million English-Ukrainian sentence pairs and conducted human evaluation on a stratified sample of 9,755 pairs. Using the obtained data, we ran a thorough statistical analysis to assess the performance of selected QE models and build linear, quadratic and beta regression models on the ensemble to estimate human quality judgments from automatic metrics. Our best ensemble model explained approximately 60\% of the variance in expert ratings. We also found a non-linear relationship between automatic metrics and human quality perception, indicating that automatic metrics can be used to predict the human score. Our findings will facilitate further research in parallel corpus filtering and quality estimation and ultimately contribute to higher-quality NMT systems. We are releasing our framework, the evaluated corpus with quality scores, and the human evaluation dataset to support further research in this area.

A Framework for Large-Scale Parallel Corpus Evaluation: Ensemble Quality Estimation Models Versus Human Assessment

In this paper, we introduce OmniGEC, a collection of multilingual silver-standard datasets for the task of Grammatical Error Correction (GEC), covering eleven languages: Czech, English, Estonian, German, Greek, Icelandic, Italian, Latvian, Slovene, Swedish, and Ukrainian. These datasets facilitate the development of multilingual GEC solutions and help bridge the data gap in adapting English GEC solutions to multilingual GEC. The texts in the datasets originate from three sources: Wikipedia edits for the eleven target languages, subreddits from Reddit in the eleven target languages, and the Ukrainian-only UberText 2.0 social media corpus. While Wikipedia edits were derived from human-made corrections, the Reddit and UberText 2.0 data were automatically corrected with the GPT-4o-mini model. The quality of the corrections in the datasets was evaluated both automatically and manually. Finally, we fine-tune two open-source large language models — Aya-Expanse (8B) and Gemma-3 (12B) — on the multilingual OmniGEC corpora and achieve state-of-the-art (SOTA) results for paragraph-level multilingual GEC. The dataset collection and the best-performing models are available on Hugging Face.

Introducing OmniGEC: A Silver Multilingual Dataset for Grammatical Error Correction

This paper addresses the challenges of sentiment analysis in Ukrainian social media, where users frequently engage in code-switching with Russian and other languages. We introduce COSMUS (COde-Switched MUltilingual Sentiment for Ukrainian Social media), a 12,224-texts corpus collected from Telegram channels, product‑review sites and open datasets, annotated into positive, negative, neutral and mixed sentiment classes as well as language labels (Ukrainian, Russian, code-switched). We benchmark three modeling paradigms: (i) few‑shot prompting of GPT‑4o and DeepSeek V2-chat, (ii) multilingual mBERT, and (iii) the Ukrainian‑centric UkrRoberta. We also analyze calibration and LIME scores of the latter two solutions to verify its performance on various language labels. To mitigate data sparsity we test two augmentation strategies: back‑translation consistently hurts performance, whereas a Large Language Model (LLM) word‑substitution scheme yields up to +2.2% accuracy. 
Our work delivers the first publicly available dataset and comprehensive benchmark for sentiment classification in Ukrainian code‑switching media. Results demonstrate that language‑specific pre‑training combined with targeted augmentation yields the most accurate and trustworthy predictions in this challenging low‑resource setting.

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Unmasking the Strategists: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues

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