GenAI4ELab/papercli-papers huggingface dataset:
DisCuT and DiscReT: MELODI at DISRPT 2025 Multilingual discourse segmentation, connective tagging and relation classification
Authors: Robin Pujol; Firmin Rousseau; Philippe Muller; Chloé Braud
Venue: EMNLP 2025 (Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025))

Abstract: This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2025 shared task on discourse: segmentation, connective identification, and relation classification. The competition involves corpora in various languages, in several underlying frameworks, and datasets are given with or without sentence segmentation. This year, for the ranked, closed track, the campaign adds as a constraint to train only one model for each task, with an upper bound on the size of the model (no more than 4B parameters).An additional open track authorizes any size of, possibly non public, models that will not be reproduced by the organizers and thus not ranked.We compared several fine-tuning approaches either based on encoder-only transformer-based models, or auto-regressive generative ones. To be able to train one model on the variety of corpora, we explored various ways of combining data – by framework, language or language groups, with different sequential orderings –, and the addition of features to guide the model. For the closed track, our final submitted system is based on XLM-RoBERTa large for relation identification, and on InfoXLM for segmentation and connective identification. Our experiments demonstrate that building a single, multilingual model does not necessarily degrade the performance compared to language-specific systems, with at best 64.06% for relation identification, 90.19% for segmentation and 81.15% for connective identification (on average on the development sets), results that are similar or higher that the ones obtained in previous campaigns.We also found that a generative approach could give even higher results on relation identification, with at best 64.65% on the dev sets.
CLaC at DISRPT 2025: Hierarchical Adapters for Cross-Framework Multi-lingual Discourse Relation Classification
Authors: Nawar Turk; Daniele Comitogianni; Leila Kosseim
Venue: EMNLP 2025 (Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025))

Abstract: We present our submission to Task 3 (Discourse Relation Classification) of the DISRPT 2025 shared task. Task 3 introduces a unified set of 17 discourse relation labels across 39 corpora in 16 languages and six discourse frameworks, posing significant multilingual and cross‐formalism challenges. We first benchmark the task by fine‐tuning multilingual BERT‐based models (mBERT, XLM‐RoBERTa‐Base, and XLM‐RoBERTa‐Large) with two argument‐ordering strategies and progressive unfreezing ratios to establish strong baselines. We then evaluate prompt‐based large language models (namely Claude Opus 4.0) in zero‐shot and few‐shot settings to understand how LLMs respond to the newly proposed unified labels. Finally, we introduce HiDAC, a Hierarchical Dual‐Adapter Contrastive learning model. Results show that while larger transformer models achieve higher accuracy, the improvements are modest, and that unfreezing the top 75% of encoder layers yields performance comparable to full fine‐tuning while training far fewer parameters. Prompt‐based models lag significantly behind fine‐tuned transformers, and HiDAC achieves the highest overall accuracy (67.5%) while remaining more parameter‐efficient than full fine‐tuning.
DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification
Authors: Zhuoxuan Ju; Jingni Wu; Abhishek Purushothama; Amir Zeldes
Venue: EMNLP 2025 (Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025))

Abstract: This paper presents DeDisCo, Georgetown University’s entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results.
HITS at DISRPT 2025: Discourse Segmentation, Connective Detection, and Relation Classification
Authors: Souvik Banerjee; Yi Fan; Michael Strube
Venue: EMNLP 2025 (Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025))

Abstract: This paper describes the submission of the HITS team to the DISRPT 2025 shared task. The shared task includes three sub-tasks: (1) discourse unit segmentation across formalisms, (2) cross-lingual discourse connective identification, and (3) cross-formalism discourse relation classification. This paper presents our strategies for the DISRPT 2025 Shared Task. In Task 1, our approach involves fine-tuning through multilingual joint training on linguistically motivated language groups. We incorporated two key techniques to improve model performance: a weighted loss function to address the task’s significant class imbalance and Fast Gradient Method (FGM) adversarial training to boost the model’s robustness. In task 2, our approach involves building an ensemble of three encoder models whose embeddings are smartly fused together with a multi-head attention layer. We also add Part-Of-Speech tags and dependency relations present in the training file as linguistic features. A CRF layer is added after the classification layer to account for dependencies between adjacent labels. To account for label imbalance, we use focal loss and label smoothing. This ensures our model is robust and flexible enough to handle different languages. In task 3, we use two-stage fine-tuning framework designed to transfer the nuanced reasoning capabilities of a very large “teacher” model to a compact “student” model so that the smaller model can learn complex discourse relationships. The fine-tuning process follows a curriculum learning framework. In such a framework the model learns to perform increasingly harder tasks. In our case, the model first learns to look at the discourse units and then predict the label followed by looking at Chain-Of-Thought reasoning for harder examples. This way it can learn to internalise such reasoning and increase prediction accuracy on the harder samples.
SeCoRel: Multilingual Discourse Analysis in DISRPT 2025
Authors: Sobha Lalitha Devi; Pattabhi Rk Rao; Vijay Sundar Ram
Venue: EMNLP 2025 (Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025))

Abstract: The work presented here describes our participation in DISRPT 2025 shared task in three tasks, Task1: Discourse Unit Segmentation across Formalisms, Task 2: Discourse Connective Identification across Languages and Task 3: Discourse Relation Classification across Formalisms. We have fine-tuned XLM-RoBERTa, a language model to address these three tasks. We have come up with one single multilingual language model for each task. Our system handles data in both the formats .conllu and .tok and different discourse formalisms. We have obtained encouraging results. The performance on test data in the three tasks is similar to the results obtained for the development data.
Digital Tongues: Internet Language, Collective Identity, and Implications for Human-Computer Interaction
Authors: Zi-Xiang Lin
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: Nowadays, internet languages, including emojis, memes, hashtags, and slang, have become vital in constructing online communities’ collective identities. However, all these forms of internet language can sometimes disempower people from other generations or cultures. This position paper presents an argument explaining how online forms of communication create social belonging for specific groups at the expense of users, and especially elderly people, due to interpretation hurdles. The present study aims to evaluate the relationship between the internet language and online collective identity, highlighting how patterns in internet language can inform human- computer interaction (HCI) by revealing how users’ express identity, inclusion, and exclusion online.
Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation
Authors: Maike Behrendt; Stefan Sylvius Wagner; Mira Warne; Jana Leonie Peters; Marc Ziegele; Stefan Harmeling
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: Online spaces provide individuals with the opportunity to engage in discussions on important topics and make collective decisions, regardless of their geographic location or time zone. However, without adequate support and careful design, such discussions often suffer from a lack of structure and civility in the exchange of opinions. Artificial intelligence (AI) offers a promising avenue for helping both participants and organizers in managing large-scale online participation processes. This paper introduces an extension of adhocracy+, a large-scale open-source participation platform. Our extension features two AI-supported debate modules designed to improve discussion quality and foster participant interaction.In a large-scale user study we examined the effects and usability of both modules. We report our findings in this paper. The extended platform is available at https://github.com/mabehrendt/discuss2.0.
User-Centric Design Paradigms for Trust and Control in Human-LLM-Interactions: A Survey
Authors: Milena Belosevic
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: As LLMs become widespread, trust in their behavior becomes increasingly important. For NLP research, it is crucial to ensure that not only AI designers and developers, but also end users, are enabled to control the properties of trustworthy LLMs, such as transparency, privacy, or accuracy. However, involving end users in this process remains a practical challenge. Based on a design-centered survey of methods developed in recent papers from HCI and NLP venues, this paper proposes seven design paradigms that can be integrated in NLP research to enhance end-user control over the trustworthiness of LLMs. We discuss design gaps and challenges of applying these paradigms in NLP and propose future research directions.
TripleCheck: Transparent Post-Hoc Verification of Biomedical Claims in AI-Generated Answers
Authors: Ana Valeria González; Sidsel Boldsen; Roland Hangelbroek
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: Retrieval Augmented Generation (RAG) has advanced Question Answering (QA) by connecting Large Language Models (LLMs) to external knowledge. However, these systems can still produce answers that are unsupported, lack clear traceability, or misattribute information — a critical issue in the biomedical domain where accuracy, trust and control are essential. We introduce TripleCheck, a post-hoc framework that breaks down an LLM’s answer into factual triples and checks each against both the retrieved context and a biomedical knowledge graph. By highlighting which statements are supported, traceable, or correctly attributed, TripleCheck enables users to spot gaps, unsupported claims, and misattributions, prompting more careful follow up. We present the TripleCheck framework, evaluate it on the SciFact benchmark, analyze its limitations, and share preliminary expert feedback. Results show that TripleCheck provides nuanced insight, potentially supporting greater trust and safer AI adoption in biomedical applications.
Rethinking Search: A Study of University Students’ Perspectives on Using LLMs and Traditional Search Engines in Academic Problem Solving
Authors: Md. Faiyaz Abdullah Sayeedi; Md. Sadman Haque; Zobaer Ibn Razzaque; Robiul Awoul Robin; Sabila Nawshin
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: With the increasing integration of Artificial Intelligence (AI) in academic problem solving, university students frequently alternate between traditional search engines like Google and large language models (LLMs) for information retrieval. This study explores students’ perceptions of both tools, emphasizing usability, efficiency, and their integration into academic workflows. Employing a mixed-methods approach, we surveyed 109 students from diverse disciplines and conducted in-depth interviews with 12 participants. Quantitative analyses, including ANOVA and chi-square tests, were used to assess differences in efficiency, satisfaction, and tool preference. Qualitative insights revealed that students commonly switch between GPT and Google: using Google for credible, multi-source information and GPT for summarization, explanation, and drafting. While neither tool proved sufficient on its own, there was a strong demand for a hybrid solution. In response, we developed a prototype, a chatbot embedded within the search interface, that combines GPT’s conversational capabilities with Google’s reliability to enhance academic research and reduce cognitive load.
First Impressions from Comparing Form-Based and Conversational Interfaces for Public Service Access in India
Authors: Chaitra C R; Pranathi Voora; Bhaskar Ruthvik Bikkina; Bharghavaram Boddapati; Vivan Jain; Prajna Upadhyay; Dipanjan Chakraborty
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: Accessing government welfare schemes in India remains difficult for emergent users—individuals with limited literacy, digital familiarity, or language support. This paper compares two mobile platforms that deliver the same scheme-related information but differ in interaction modality: myScheme, a government-built, form-based Android application, and Prabodhini, a voice-based conversational prototype powered by generative AI and Retrieval-Augmented Generation (RAG). Through a task-based comparative study with 15 low-income participants, we examine usability, task completion time, and user preference. Drawing on theories such as the Gulf of Execution and Zipf’s Law of Least Effort, we show that Prabodhini’s conversational design and support for natural language input better align with emergent users’ mental models and practices. Our findings highlight the value of multimodal, voice-first NLP systems for improving trust, access, and inclusion in public digital services. We discuss implications for designing accessible language technologies for marginalised populations.
Out of the Box, into the Clinic? Evaluating State-of-the-Art ASR for Clinical Applications for Older Adults
Authors: Bram Van Dijk; Tiberon Kuiper; Sirin Aoulad si Ahmed; Armel Lefebvre; Jake Johnson; Jan Duin; Simon Mooijaart; Marco Spruit
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: Voice-controlled interfaces can support older adults in clinical contexts – with chatbots being a prime example – but reliable Automatic Speech Recognition (ASR) for underrepresented groups remains a bottleneck. This study evaluates state-of-the-art ASR models on language use of older Dutch adults, who interacted with the Welzijn.AI chatbot designed for geriatric contexts. We benchmark generic multilingual ASR models, and models fine-tuned for Dutch spoken by older adults, while also considering processing speed. Our results show that generic multilingual models outperform fine-tuned models, which suggests recent ASR models can generalise well out of the box to real-world datasets. Moreover, our results indicate that truncating generic models is helpful in balancing the accuracy-speed trade-off. Nonetheless, we also find inputs which cause a high word error rate and place them in context.
MobileA3gent: Training Mobile GUI Agents Using Decentralized Self-Sourced Data from Diverse Users
Authors: WenHao Wang; Mengying Yuan; Zijie Yu; Guangyi Liu; Rui Ye; Tian Jin; Siheng Chen; Yanfeng Wang
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: The advancement of mobile GUI agents has opened new opportunities for automating tasks on mobile devices. Training these agents requires large-scale high-quality data, which is prohibitively expensive when relying on human labor. Given the vast population of global mobile phone users, if automated data collection from them becomes feasible, the resulting data volume and the subsequently trained mobile agents could reach unprecedented levels. Nevertheless, two major challenges arise: (1) extracting user instructions without human intervention and (2) utilizing distributed user data while preserving privacy.To tackle these challenges, we propose MobileA3gent, a collaborative framework that trains mobile GUI Agents using decentralized self-sourced data from diverse users. The framework comprises two components, each targeting a specific challenge: (1) Auto-Annotation, which enables the automatic collection of high-quality datasets during users’ routine phone usage with minimal cost. (2) FedVLM-A, which enhances federated VLM training under non-IID distributions by incorporating adapted global aggregation based on both episode-level and step-level variability. Extensive experiments prove that MobileA3gent achieves superior performance over traditional approaches at only 1% of the cost, highlighting its potential for real-world applications. Our code is publicly available at: https://anonymous.4open.science/r/MobileA3gent-Anonymous.
Towards an Automated Framework to Audit Youth Safety on TikTok
Authors: Linda Xue; Francesco Corso; Nicolo Fontana; Geng Liu; Stefano Ceri; Francesco Pierri
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: This paper investigates the effectiveness of TikTok’s enforcement mechanisms for limiting the exposure of harmful content to youth accounts. We collect over 7000 videos, classify them as harmful vs not-harmful, and then simulate interactions using age-specific sockpuppet accounts through both passive and active engagement strategies. We also evaluate the performance of large language (LLMs) and vision-language models (VLMs) in detecting harmful content, identifying key challenges in precision and scalability. Preliminary results show minimal differences in content exposure between adult and youth accounts, raising concerns about the platform’s age-based moderation. These findings suggest that the platform needs to strengthen youth safety measures and improve transparency in content moderation.
Predictive Modeling of Human Developers’ Evaluative Judgment of Generated Code as a Decision Process
Authors: Sergey Kovalchuk; Yanyu Li; Dmitriy Fedrushkov
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: The paper presents early results in the development of an approach to predictive modeling of human developer perceiving of code generated in question-answering scenarios with Large Language Model (LLM) applications. The study is focused on building a model for the description and prediction of human implicit behavior during evaluative judgment of generated code through evaluation of its consistency, correctness, and usefulness as subjective perceiving characteristics. We used Markov Decision Process (MDP) as a basic framework to describe the human developer and his/her perceiving. We consider two approaches (regression-based and classification-based) to identify MDP parameters so it can be used as an “artificial” developer for human-centered code evaluation. An experimental evaluation of the proposed approach was performed with survey data previously collected for several code generation LLMs in a question-answering scenario. The results show overall good performance of the proposed model in acceptance rate prediction (accuracy 0.82) and give promising perspectives for further development and application.
Collaborative Co-Design Practices for Supporting Synthetic Data Generation in Large Language Models: A Pilot Study
Authors: Heloisa Candello; Raya Horesh; Aminat Adebiyi; Muneeza Azmat; Rogério Abreu de Paula; Lamogha Chiazor
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: Large language models (LLMs) are increasingly embedded in development pipelines and the daily workflows of AI practitioners. However, their effectiveness depends on access to high-quality datasets that are sufficiently large, diverse, and contextually relevant. Existing datasets often fall short of these requirements, prompting the use of synthetic data (SD) generation. A critical step in this process is the creation of human seed examples, which guide the generation of SD tailored to specific tasks. We propose a participatory methodology for seed example generation, involving multidisciplinary teams in structured workshops to co-create examples aligned with Responsible AI principles. In a pilot study with a Responsible AI team, we facilitated hands-on activities to produce seed examples and evaluated the resulting data across three dimensions: diversity, sensibility, and relevance. Our findings suggest that participatory approaches can enhance the representativeness and contextual fidelity of synthetic datasets. We provide a reproducible framework to support NLP practitioners in generating high-quality seed data for LLM development and deployment
How Well Can AI Models Generate Human Eye Movements During Reading?
Authors: Ivan Stebakov; Ilya Pershin
Venue: EMNLP 2025 (Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP))

Abstract: Eye movement analysis has become an essential tool for studying cognitive processes in reading, serving both psycholinguistic research and natural language processing applications aimed at enhancing language model performance. However, the scarcity of eye-tracking data and its limited generalizability constrain data-driven approaches. Synthetic scanpath generation offers a potential solution to these limitations. While recent advances in scanpath generation show promise, current literature lacks systematic evaluation frameworks that comprehensively assess models’ ability to reproduce natural reading gaze patterns. Existing studies often focus on isolated metrics rather than holistic evaluation of cognitive plausibility. This study presents a systematic evaluation of contemporary scanpath generation models, assessing their capacity to replicate natural reading behavior through comprehensive scanpath analysis. We demonstrate that while synthetic scanpath models successfully reproduce basic gaze patterns, significant limitations persist in capturing part-of-speech dependent gaze features and reading behaviors. Our cross-dataset comparison reveals performance degradation in three key areas: generalization across text genres, processing of long sentences, and reproduction of psycholinguistic effects. These findings underscore the need for more robust evaluation protocols and model architectures that better account for psycholinguistic complexity. Through detailed analysis of fixation sequences, durations, and reading patterns, we identify concrete pathways for developing more cognitively plausible scanpath generation models.