GenAI4ELab/papercli-papers huggingface dataset:
QA Analysis in Medical and Legal Domains: A Survey of Data Augmentation in Low-Resource Settings
Authors: Benedictus Kent Rachmat; Thomas Gerald; Zheng Zhang; Cyril Grouin
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), but their success remains largely confined to high-resource, general-purpose domains. In contrast, applying LLMs to low-resource domains poses significant challenges due to limited training data, domain drift, and strict terminology constraints. This survey provides an overview of the current landscape in domain-specific, low-resource QA with LLMs. We begin by analyzing the coverage and representativeness of specialized-domain QA datasets against large-scale reference datasets what we refer to as ParentQA. Building on this analysis, we survey data-centric strategies to enhance input diversity, including data augmentation techniques. We further discuss evaluation metrics for specialized tasks and consider ethical concerns. By mapping current methodologies and outlining open research questions, this survey aims to guide future efforts in adapting LLMs for robust and responsible use in resource-constrained, domain-specific environments. To facilitate reproducibility, we make our code available at https://github.com/kentrachmat/survey-da.
Time-LlaMA: Adapting Large Language Models for Time Series Modeling via Dynamic Low-rank Adaptation
Authors: Juyuan Zhang; Jiechao Gao; Wenwen Ouyang; Wei Zhu; Hui Yi Leong
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Time series modeling holds significant importance in many industrial applications and has been extensively studied. A series of recent studies have demonstrated that large language models (LLMs) possess robust pattern recognition and semantic understanding capabilities over time series data. However, the current literature have yet striked a high-quality balance between (a) effectively aligning the time series and natural language modalities and (b) keeping the inference efficiency for industrial deployment. To address the above issues, we now propose the Time-LlaMA framework. Time-LlaMA first converts the time series input into token embeddings through a linear tokenization mechanism. Second, the time series token embeddings are aligned with the text prompts. Third, to further adapt the large languag model (LLM) backbone for time series modeling, we have developed a dynamic low-rank adaptation technique (DynaLoRA). DynaLoRA dynamically chooses the most suitable LoRA modules at each layer of the Transformer backbone for each time series input, enhancing the model’s predictive capabilities. Our experimental results on an extensive collection of challenging open and proprietary time series tasks confirm that our proposed method achieves the state-of-the-art (SOTA) performance and have potentials for wide industrial usages.
RusConText Benchmark: A Russian Language Evaluation Benchmark for Understanding Context
Authors: Andrey Chirkin; Svetlana Kuznetsova; Maria Volina; Anna Dengina
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: This paper represents an implementation of an approach rather similar to that of (Zhu et al., 2024), adapted for the Russian-language data. We introduce the RusConText Benchmark for evaluating short-context understanding in Russian, comprising four distinct yet interrelated tasks: coreference resolution, discourse understanding, idiom interpretation and ellipsis resolution. Each task targets a specific aspect of linguistic processing, challenging a large language model to recover omitted information, resolve referential dependencies, interpret idioms and discourse. The RusConText Benchmark is an additional resource beyond standard benchmarks, designed to assess model performance from a specific perspective. In addition, we present the results of scoring 4 models on our benchmark.
GenDLN: Evolutionary Algorithm-Based Stacked LLM Framework for Joint Prompt Optimization
Authors: Pia Chouayfati; Niklas Herbster; Ábel Domonkos Sáfrán; Matthias Grabmair
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: With Large Language Model (LLM)-based applications becoming more common due to strong performance across many tasks, prompt optimization has emerged as a way to extract better solutions from frozen, often commercial LLMs that are not specifically adapted to a task. LLM-assisted prompt optimization methods provide a promising alternative to manual/human prompt engineering, where LLM “reasoning” can be used to make them optimizing agents. However, the cost of using LLMs for prompt optimization via commercial APIs remains high, especially for heuristic methods like evolutionary algorithms (EAs), which need many iterations to converge, and thus, tokens, API calls, and rate-limited network overhead. We propose GenDLN, an open-source, efficient genetic algorithm-based prompt pair optimization framework that leverages commercial API free tiers. Our approach allows teams with limited resources (NGOs, non-profits, academics, ...) to efficiently use commercial LLMs for EA-based prompt optimization. We conduct experiments on CLAUDETTE for legal terms of service classification and MRPC for paraphrase detection, performing in line with selected prompt optimization baselines, at no cost.
Sign Language Video Segmentation Using Temporal Boundary Identification
Authors: Kavu Maithri Rao; Yasser Hamidullah; Eleftherios Avramidis
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Sign language segmentation focuses on identifying temporal boundaries within sign language videos. As compared to previous segmentation techniques that have depended on frame-level and phrase-level segmentation, our study emphasizes on subtitle-level segmentation, using synchronized subtitle data to facilitate temporal boundary recognition. Based on Beginning-Inside-Outside (BIO) tagging for subtitle unit delineation, we train a sequence-to-sequence (Seq2Seq) model with and without attention for subtitle boundary identification. Training on optical flow data and aligned subtitles from BOBSL and YouTube-ASL, we show that the Seq2Seq model with attention outperforms baseline models, achieving improved percentage of segments, F1 and IoU score. An additional contribution is the development of an method for subtitle temporal resolution, aiming to facilitate manual annotation.
LIP-NER: Literal Patterns Benefit LLM-Based NER
Authors: Ruiqi Li; Li Chen
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Large Language Models (LLMs) can enhance the performance of Named Entity Recognition (NER) tasks by leveraging external knowledge through in-context learning. When it comes to entity-type-related external knowledge, existing methods mainly provide LLMs with semantic information such as the definition and annotation guidelines of an entity type, leaving the effect of orthographic or morphological information on LLM-based NER unexplored. Besides, it is non-trivial to obtain literal patterns written in natural language to serve LLMs. In this work, we propose LiP-NER, an LLM-based NER framework that utilizes Literal Patterns, the entity-type-related knowledge that directly describes the orthographic and morphological features of entities. We also propose an LLM-based method to automatically acquire literal patterns, which requires only several sample entities rather than any annotation example, thus further reducing human labor. Our extensive experiments suggest that literal patterns can enhance the performance of LLMs in NER tasks. In further analysis, we found that entity types with relatively standardized naming conventions but limited world knowledge in LLMs, as well as entity types with broad and ambiguous names or definitions yet low internal variation among entities, benefit most from our approach. We found that the most effective written literal patterns are (1) detailed in classification, (2) focused on majority cases rather than minorities, and (3) explicit about obvious literal features.
Testing English News Articles for Lexical Homogenization Due to Widespread Use of Large Language Models
Authors: Sarah Fitterer; Dominik Gangl; Jannes Ulbrich
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: It is widely assumed that Large Language Models (LLMs) are shaping language, with multiple studies noting the growing presence of LLM-generated content and suggesting homogenizing effects. However, it remains unclear if these effects are already evident in recent writing. This study addresses that gap by comparing two datasets of English online news articles – one from 2018, prior to LLM popularization, and one from 2024, after widespread LLM adoption. We define lexical homogenization as a decrease in lexical diversity, measured by the MATTR, Maas, and MTLD metrics, and introduce the LLM-Style-Word Ratio (SWR) to measure LLM influence. We found higher MTLD and SWR scores, yet negligible changes in Maas and MATTR scores in 2024 corpus. We conclude that while there is an apparent influence of LLMs on written online English, homogenization effects do not show in the measurements. We therefore propose to apply different metrics to measure lexical homogenization in future studies on the influence of LLM usage on language change.
Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation
Authors: Rohit Kumar; Chandan Nolbaria
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Static and outdated datasets hinder the accuracy of Financial Sentiment Analysis (FSA) in capturing rapidly evolving market sentiment. We tackle this by proposing a novel data augmentation technique using Retrieval Augmented Generation (RAG). Our method leverages a generative LLM to infuse established benchmarks with up-to-date contextual information from contemporary financial news. This RAG-based augmentation significantly modernizes the data’s alignment with current financial language. Furthermore, a robust BERT-BiGRU judge model verifies that the sentiment of the original annotations is faithfully preserved, ensuring the generation of high-quality, temporally relevant, and sentiment-consistent data suitable for advancing FSA model development.
Inverse Reinforcement Learning Meets Large Language Model Alignment
Authors: Mihaela van der Schaar; Hao Sun
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts))

Abstract: In the era of Large Language Models (LLMs), alignment has emerged as a fundamental yet challenging problem in the pursuit of more reliable, controllable, and capable machine intelligence. The recent success of reasoning models and conversational AI systems has underscored the critical role of reinforcement learning (RL) in enhancing these systems, driving increased research interest at the intersection of RL and LLM alignment.This tutorial will provide a comprehensive review of recent advances in LLM alignment through the lens of inverse reinforcement learning (IRL), emphasizing the distinctions between RL techniques employed in LLM alignment and those in conventional RL tasks. In particular, we highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift. The tutorial will begin with fundamental concepts in RL to provide a foundation for the audience unfamiliar with the field. We then examine recent advances in this research agenda, discussing key challenges and opportunities in conducting IRL for LLM alignment. Beyond methodological considerations, we explore practical aspects, including datasets, benchmarks, evaluation metrics, infrastructure, and computationally efficient training and inference techniques.Finally, we draw insights from the literature on sparse-reward RL to identify open questions and potential research directions. By synthesizing findings from diverse studies, we aim to provide a structured and critical overview of the field, highlight unresolved challenges, and outline promising future directions for improving LLM alignment through RL and IRL techniques.
Eye Tracking and NLP
Authors: David Reich; Omer Shubi; Lena Jäger; Yevgeni Berzak
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts))

Abstract: Our tutorial introduces a growing research area that combines eye tracking during reading with NLP. The tutorial outlines how eye movements in reading can be leveraged for NLP, and, vice versa, how NLP methods can advance psycholinguistic modeling of eye movements in reading. We cover four main themes: (i) fundamentals of eye movements in reading, (ii) experimental methodologies and available data, (iii) integrating eye movement data in NLP models, and (iv) using LLMs for modeling eye movements in reading. The tutorial is tailored to NLP researchers and practitioners, and provides the essential background for conducting research on joint modeling of eye movements and text.
Uncertainty Quantification for Large Language Models
Authors: Artem Shelmanov; Maxim Panov; Roman Vashurin; Artem Vazhentsev; Ekaterina Fadeeva; Timothy Baldwin
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts))

Abstract: Large language models (LLMs) are widely used in NLP applications, but their tendency to produce hallucinations poses significant challenges to the reliability and safety, ultimately undermining user trust. This tutorial offers the first systematic introduction to uncertainty quantification (UQ) for LLMs in text generation tasks – a conceptual and methodological framework that provides tools for communicating the reliability of a model answer. This additional output could be leveraged for a range of downstream tasks, including hallucination detection and selective generation. We begin with the theoretical foundations of uncertainty, highlighting why techniques developed for classification might fall short in text generation. Building on this grounding, we survey state-of-the-art white-box and black-box UQ methods, from simple entropy-based scores to supervised probes over hidden states and attention weights, and show how they enable selective generation and hallucination detection. Additionally, we discuss the calibration of uncertainty scores for better interpretability. A key feature of the tutorial is practical examples using LM-Polygraph, an open-source framework that unifies more than a dozen recent UQ and calibration algorithms and provides a large-scale benchmark, allowing participants to implement UQ in their applications, as well as reproduce and extend experimental results with only a few lines of code. By the end of the session, researchers and practitioners will be equipped to (i) evaluate and compare existing UQ techniques, (ii) develop new methods, and (iii) implement UQ in their code for deploying safer, more trustworthy LLM-based systems.
Human-AI Collaboration: How AIs Augment Human Teammates
Authors: Sherry Wu; Diyi Yang; Joseph Chang; Marti A. Hearst; Kyle Lo
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts))

Abstract: The continuous, rapid development of general-purpose models like LLMs suggests the theoretical possibility of AI performing any human task. Yet, despite the potential and promise, these models are far from perfect, excelling at certain tasks while struggling with others. The tension between what is possible and a model’s limitations raises the general research question that has attracted attention from various disciplines: What is the best way to use AI to maximize its benefits? In this tutorial, we will review recent developments related to human-AI teaming and collaboration. To the best of our knowledge, our tutorial will be the first to provide a more integrated view from NLP, HCI, Computational Social Science, and Learning Science, etc., and highlight how different communities have identified the goals and societal impacts of such collaborations, both positive and negative. We will further discuss how to operationalize these Human-AI collaboration goals, and reflect on how state-of-the-art AI models should be evaluated and scaffolded to make them most useful in collaborative contexts.
Navigating Ethical Challenges in NLP: Hands-on strategies for students and researchers
Authors: Luciana Benotti; Fanny Ducel; Karën Fort; Guido Ivetta; Zhijing Jin; Min-Yen Kan; Seunghun J. Lee; Minzhi Li; Margot Mieskes; Adriana Pagano
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts))

Abstract: With NLP research being rapidly productionized into real-world applications, it is important to be aware of and think through the consequences of our work. Such ethical considerations are important in both authoring and reviewing (e.g. privacy, consent, fairness, among others). This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues and review common considerations that recur in NLP research. The methodology is interactive and participatory, including discussion of case studies and group work. Participants will gain practical experience on when to flag a paper for ethics review and how to write an ethical consideration section to be shared with the broader community. Most importantly, the participants will be co-creating the tutorial outcomes and extending tutorial materials to share as public outcomes.
NLP for Counterspeech against Hate and Misinformation (CSHAM)
Authors: Daniel Russo; Helena Bonaldi; Yi-Ling Chung; Gavin Abercrombie; Marco Guerini
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts))

Abstract: This tutorial aims to bring together research from different fields such as computer science and the social sciences and policy to show how counterspeech is currently used to tackle abuse and misinformation by individuals, activists and organisations, how Natural Language Processing (NLP) and Generation (NLG) can be applied to automate its production, and the implications of using large language models for this task. It will also address, but not be limited to, the questions of how to evaluate and measure the impacts of counterspeech, the importance of expert knowledge from civil society in the development of counterspeech datasets and taxonomies, and how to ensure fairness and mitigate the biases present in language models when generating counterspeech. The tutorial will bring diverse multidisciplinary perspectives to safety research by including case studies from industry and public policy to share insights on the impact of counterspeech and social correction and the implications of applying NLP to critical real-world problems. It will also go deeper into the challenging task of tackling hate and misinformation together, which represents an open research question yet to be addressed in NLP but gaining attention as a stand alone topic.
Synthetic Data in the Era of Large Language Models
Authors: Vijay Viswanathan; Xiang Yue; Alisa Liu; Yizhong Wang; Graham Neubig
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts))

Abstract: Progress in natural language processing has historically been driven by better data, and researchers today are increasingly using ‘synthetic data’ - data generated with the assistance of large language models - to make dataset construction faster and cheaper. However, most synthetic data generation approaches are executed in an ad hoc manner and ‘reinvent the wheel’ rather than build on prior foundations. This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. Our tutorial will be divided into four main sections. First, we will describe algorithms for producing high-quality synthetic data. Second, we will describe how synthetic data can be used to advance the general-purpose development and study of language models. Third, we will demonstrate how to customize synthetic data generation to support scenario-specific applications. Finally, we will discuss open questions about the production and use of synthetic data that must be answered to overcome some of their current limitations. Our goal is that by unifying recent advances in this emerging research direction, we can build foundations upon which the community can improve the rigor, understanding, and effectiveness of synthetic data moving forward.
Guardrails and Security for LLMs: Safe, Secure and Controllable Steering of LLM Applications
Authors: Traian Rebedea; Leon Derczynski; Shaona Ghosh; Makesh Narsimhan Sreedhar; Faeze Brahman; Liwei Jiang; Bo Li; Yulia Tsvetkov; Christopher Parisien; Yejin Choi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts))

Abstract: Pretrained generative models, especially large language models, provide novel ways for users to interact with computers. While generative NLP research and applications had previously aimed at very domain-specific or task-specific solutions, current LLMs and applications (e.g. dialogue systems, agents) are versatile across many tasks and domains. Despite being trained to be helpful and aligned with human preferences (e.g., harmlessness), enforcing robust guardrails on LLMs remains a challenge. And, even when protected against rudimentary attacks, just like other complex software, LLMs can be vulnerable to attacks using sophisticated adversarial inputs. This tutorial provides a comprehensive overview of key guardrail mechanisms developed for LLMs, along with evaluation methodologies and a detailed security assessment protocol - including auto red-teaming of LLM-powered applications. Our aim is to move beyond the discussion of single prompt attacks and evaluation frameworks towards addressing how guardrailing can be done in complex dialogue systems that employ LLMs.
ACL 2025 Industry Track: Overview
Authors: Georg Rehm; Yunyao Li
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: For the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), it was decided once again to organise a dedicated Industry Track. Similar to the main research track of the conference, the industry track attracted an unprecedented number of 421 paper submissions. In total, 453 reviewers and 21 area chairs participated in the evaluation of these papers. After a thorough, double-blind peer-review evaluation with three reviews for each submission followed by reviewer discussions and additional deliberations, 108 papers were selected for presentation at the ACL 2025 Industry Track. Large language models were front and center of almost all submissions with trustworthiness, domain-adaptation, retrieval-augmented generation, and agentic architectures – across domains such as medical, legal, and finance – being popular topics.