GenAI4ELab/papercli-papers huggingface dataset:
Thapar Titan/s : Fine-Tuning Pretrained Language Models with Contextual Augmentation for Mistake Identification in Tutor–Student Dialogues
Authors: Harsh Dadwal; Sparsh Rastogi; Jatin Bedi
Venue: ACL 2025 (Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025))

Abstract: This paper presents Thapar Titan/s’ submission to the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors. The shared task consists of five subtasks; our team ranked 18th in Mistake Identification, 15th in Mistake Location, and 18th in Actionability. However, in this paper, we focus exclusively on presenting results for Task 1: Mistake Identification, which evaluates a system’s ability to detect student mistakes.Our approach employs contextual data augmentation using a RoBERTa based masked language model to mitigate class imbalance, supplemented by oversampling and weighted loss training. Subsequently, we fine-tune three separate classifiers: RoBERTa, BERT, and DeBERTa for three-way classification aligned with task-specific annotation schemas. This modular and scalable pipeline enables a comprehensive evaluation of tutor feedback quality in educational dialogues.
Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain
Authors: Shintaro Ozaki; Yuta Kato; Siyuan Feng; Masayo Tomita; Kazuki Hayashi; Wataru Hashimoto; Ryoma Obara; Masafumi Oyamada; Katsuhiko Hayashi; Hidetaka Kamigaito; Taro Watanabe
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries. This approach is widely applied in several fields by taking its advantage of injecting the most up-to-date information, and researchers are focusing on understanding and improving this aspect to unlock the full potential of RAG in such high-stakes applications.However, despite the potential of RAG to address these needs, the mechanisms behind the confidence levels of its outputs remain underexplored.Our study focuses on the impact of RAG, specifically examining whether RAG increases the confidence of LLM outputs in the medical domain.We conduct this analysis across various configurations and models.We evaluate confidence by treating the model’s predicted probability as its output and calculating several evaluation metrics which include calibration error method, entropy, best probability, and accuracy.Experimental results across multiple datasets confirmed that certain models possess the capability to judge for themselves whether an inserted document relates to the correct answer. These results suggest that evaluating models based on their output probabilities determine whether they function as generators in the RAG framework.Our approach allows to evaluate whether the models handle retrieved documents.
Effect of Multilingual and Domain-adapted Continual Pre-training on Few-shot Promptability
Authors: Ken Yano; Makoto Miwa
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Continual Pre-training (CPT) can help pre-trained large language models (LLMs) effectively adapt to new or under-trained domains or low-resource languages without re-training from scratch.Nevertheless, during CPT, the model’s few-shot transfer ability is known to be affected for emergent tasks.We verified this by comparing the performance between the few-shot and fine-tuning settings on the same tasks.We used Llama3-ELAINE-medLLM, which was continually pre-trained on Llama3-8B, targeted for the biomedical domain, and adapted for multilingual languages (English, Japanese, and Chinese).We compared the performance of Llama3-ELAINE-medLLM and Llama3-8B in three emergent tasks: two related domain tasks, entity recognition (NER) and machine translation (MT), and one out-of-domain task, summarization (SUM). Our experimental results show that degradation in few-shot transfer ability does not necessarily indicate the model’s underlying potential on the same task after fine-tuning.
MedSummRAG: Domain-Specific Retrieval for Medical Summarization
Authors: Guanting Luo; Yuki Arase
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Medical text summarization faces significant challenges due to the complexity and domain-specific nature of the language. Although large language models have achieved significant success in general domains, their effectiveness in the medical domain remains limited. This limitation stems from their insufficient understanding of domain-specific terminology and difficulty in interpreting complex medical relationships, which often results in suboptimal summarization quality. To address these challenges, we propose MedSummRAG, a novel retrieval-augmented generation (RAG) framework that integrates external knowledge to enhance summarization. Our approach employs a fine-tuned dense retriever, trained with contrastive learning, to retrieve relevant documents for medical summarization. The retrieved documents are then integrated with the input text to generate high-quality summaries. Experimental results show that MedSummRAG achieves significant improvements in ROUGE scores on both zero/few-shot and fine-tuned language models, outperforming baseline methods. These findings underscore the importance of RAG and domain adaptation of the retriever for medical text summarization. The source code of this paper can be obtained from: https://github.com/guantingluo98/MedSummRAG
Enhancing Stress Detection on Social Media Through Multi-Modal Fusion of Text and Synthesized Visuals
Authors: Efstathia Soufleri; Sophia Ananiadou
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Social media platforms generate an enormous volume of multi-modal data, yet stress detection research has predominantly relied on text-based analysis. In this work, we propose a novel framework that integrates textual content with synthesized visual cues to enhance stress detection. Using the generative model DALL·E, we synthesize images from social media posts, which are then fused with text through the multi-modal capabilities of a pre-trained CLIP model. Our approach is evaluated on the Dreaddit dataset, where a classifier trained on frozen CLIP features achieves 94.90% accuracy, and full fine-tuning further improves performance to 98.41%. These results underscore the integration of synthesized visuals with textual data not only enhances stress detection but also offers a robust method over traditional text-only methods, paving the way for innovative approaches in mental health monitoring and social media analytics.
Fine-tuning LLMs to Extract Epilepsy Seizure Frequency Data from Health Records
Authors: Ben Holgate; Joe Davies; Shichao Fang; Joel Winston; James Teo; Mark Richardson
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: We developed a new methodology of extracting the frequency of a patient’s epilepsy seizures from unstructured, free-text outpatient clinic letters by: first, devising a singular unit of measurement for seizure frequency; and second, fine-tuning a generative Large Language Model (LLM) on our bespoke annotated dataset. We measured frequency by the number of seizures per month: one seizure or more requires an integer; and less than one a decimal. This approach enables us to track whether a patient”s seizures are improving or not over time. We found fine-tuning improves the F1 score of our best-performing LLM, Ministral-8B-Instruct-2410, by around three times compared to an untrained model. We also found Ministral demonstrated an impressive ability for mathematical reasoning.
AdaBioBERT: Adaptive Token Sequence Learning for Biomedical Named Entity Recognition
Authors: Sumit Kumar; Tanmay Basu
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Accurate identification and labeling of biomedical entities, such as diseases, genes, chemical and species, within scientific texts are crucial for understanding complex relationships. We propose Adaptive BERT or AdaBioBERT, a robust named entity recognition (NER) model that builds upon BioBERT (Biomedical Bidirectional Encoded Representation from Transformers) based on an adaptive loss function to learn different types of biomedical token sequence. This adaptive loss function combines the standard Cross Entropy (CE) loss and Conditional Random Field (CRF) loss to optimize both token level accuracy and sequence-level coherence. AdaBioBERT captures rich semantic nuances by leveraging pre-trained contextual embeddings from BioBERT. On the other hand, the CRF loss of AdaBioBERT ensures proper identification of complex multi-token biomedical entities in a sequence and the CE loss can capture the simple unigram entities in a sequence. The empirical analysis on multiple standard biomedical coprora demonstrates that AdaBioBERT performs better than the state of the arts for most of the datasets in terms of macro and micro averaged F1 score.’
Transformer-Based Medical Statement Classification in Doctor-Patient Dialogues
Authors: Farnod Bahrololloomi; Johannes Luderschmidt; Biying Fu
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: The classification of medical statements in German doctor-patient interactions presents significant challenges for automated medical information extraction, particularly due to complex domain-specific terminology and the limited availability of specialized training data. To address this, we introduce a manually annotated dataset specifically designed for distinguishing medical from non-medical statements. This dataset incorporates the nuances of German medical terminology and provides a valuable foundation for further research in this domain. We systematically evaluate Transformer-based models and multimodal embedding techniques, comparing them against traditional embedding-based machine learning (ML) approaches and domain-specific models such as medBERT.de. Our empirical results show that Transformer-based architectures, such as the Sentence-BERT model combined with a support vector machine (SVM), achieve the highest accuracy of 79.58% and a weighted F1-Score of 78.81%, demonstrating an average performance improvement of up to 10% over domain-specific counterparts. Additionally, we highlight the potential of lightweight ML-models for resource-efficient deployment on mobile devices, enabling real-time medical information processing in practical settings. These findings emphasize the importance of embedding selection for optimizing classification performance in the medical domain and establish a robust foundation for the development of advanced, domain-adapted German language models.
PreClinIE: An Annotated Corpus for Information Extraction in Preclinical Studies
Authors: Simona Doneva; Hanna Hubarava; Pia Härvelid; Wolfgang Zürrer; Julia Bugajska; Bernard Hild; David Brüschweiler; Gerold Schneider; Tilia Ellendorff; Benjamin Ineichen
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Animal research, sometimes referred to as preclinical research, plays a vital role in bridging the gap between basic science and clinical applications. However, the rapid increase in publications and the complexity of reported findings make it increasingly difficult for researchers to extract and assess relevant information. While automation through natural language processing (NLP) holds great potential for addressing this challenge, progress is hindered by the absence of high-quality, comprehensive annotated resources specific to preclinical studies. To fill this gap, we introduce PreClinIE, a fully open manually annotated dataset. The corpus consists of abstracts and methods sections from 725 publications, annotated for study rigor indicators (e.g., random allocation) and other study characteristics (e.g., species). We describe the data collection and annotation process, outlining the challenges of working with preclinical literature. By providing this resource, we aim to accelerate the development of NLP tools that enhance literature mining in preclinical research.
Benchmarking zero-shot biomedical relation triplet extraction across language model architectures
Authors: Frederik Gade; Ole Lund; Marie Lisandra Mendoza
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Many language models (LMs) in the literature claim excellent zero-shot and/or few-shot capabilities for named entity recognition (NER) and relation extraction (RE) tasks and assert their ability to generalize beyond their training datasets. However, these claims have yet to be tested across different model architectures. This paper presents a performance evaluation of zero-shot relation triplet extraction (NER followed by RE of the entities) for both small and large LMs, utilizing 13,867 texts from 61 biomedical corpora and encompassing 151 unique entity types. This comprehensive evaluation offers valuable insights into the practical applicability and performance of LMs within the intricate domain of biomedical relation triplet extraction, highlighting their effectiveness in managing a diverse range of relations and entity types. Gemini 1.5 Pro, the largest LM included in the study, was the top-performing zero-shot model, achieving an average partial match micro F1 of 0.492 for NER, followed closely by SciLitLLM 1.5 14B with a score of 0.475. Fine-tuned models generally outperformed others on the corpora they were trained on, even in a few-shot setting, but struggled to generalize across all datasets with similar entity types. No models achieved an F1 score above 0.5 for the RTE task on any dataset, and their scores fluctuated based on the specific class of entity and the dataset involved. This observation highlights that there is still large room for improvement on the zero-shot utility of LMs in biomedical RTE applications.
RadQA-DPO: A Radiology Question Answering System with Encoder-Decoder Models Enhanced by Direct Preference Optimization
Authors: Md Sultan Al Nahian; Ramakanth Kavuluru
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Extractive question answering over clinical text is a crucial need to help deal with the deluge of clinical text generated in hospitals. While encoder models (e.g., BERT) have been popular for this reading comprehension–style question answering task, recently encoder-decoder models (e.g., T5) are on the rise. There is also the emergence of preference optimization techniques to align decoder-only LLMs with human preferences. In this paper, we combine encoder-decoder models with the direct preference optimization (DPO) method for the RadQA radiology question answering task. Our approach achieves a 12–15 F1 point improvement over previous state-of-the-art models. To the best of our knowledge, this effort is the first to show that DPO method also works for reading comprehension via novel heuristics to generate preference data without human inputs.
Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstracts
Authors: Elizabeth Schaefer; Kirk Roberts
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: This paper presents a pipeline for mitigating gender bias in large language models (LLMs) used in medical literature by neutralizing gendered occupational pronouns. A set of 379,000 PubMed abstracts from 1965-1980 was processed to identify and modify pronouns tied to professions. We developed a BERT-based model, Modern Occupational Bias Elimination with Refined Training, or MOBERT, trained on these neutralized abstracts, and compared it with 1965BERT, trained on the original dataset. MOBERT achieved a 70% inclusive replacement rate, while 1965BERT reached only 4%. A further analysis of MOBERT revealed that pronoun replacement accuracy correlated with the frequency of occupational terms in the training data. We propose expanding the dataset and refining the pipeline to improve performance and ensure more equitable language modeling in medical applications.
Error Detection in Medical Note through Multi Agent Debate
Authors: Abdine Maiga; Anoop Shah; Emine Yilmaz
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Large Language Models (LLMs) have approached human-level performance in text generation and summarization, yet their application in clinical settings remains constrained by potential inaccuracies that could lead to serious consequences. This work addresses the critical safety weaknesses in medical documentation systems by focusing on detecting subtle errors that require specialized medical expertise. We introduce a novel multi-agent debating framework that achieves 78.8% accuracy on medical error detection, significantly outperforming both single-agent approaches and previous multi-agent systems. Our framework leverages specialized LLM agents with asymmetric access to complementary medical knowledge sources (Mayo Clinic and WebMD), engaging them in structured debate to identify inaccuracies in clinical notes. A judge agent evaluates these arguments based solely on their medical reasoning quality, with agent-specific performance metrics incorporated as feedback for developing situation-specific trust models.
Accelerating Cross-Encoders in Biomedical Entity Linking
Authors: Javier Sanz-Cruzado; Jake Lever
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Biomedical entity linking models disambiguate mentions in text by matching them with unique biomedical concepts. This problem is commonly addressed using a two-stage pipeline comprising an inexpensive candidate generator, which filters a subset of suitable entities for a mention, and a costly but precise reranker that provides the final matching between the mention and the concept. With the goal of applying two-stage entity linking at scale, we explore the construction of effective cross-encoder reranker models, capable of scoring multiple mention-entity pairs simultaneously. Through experiments on four entity linking datasets, we show that our cross-encoder models provide between 2.7 to 36.97 times faster training speeds and 3.42 to 26.47 times faster inference speeds than a base cross-encoder model capable of scoring only one entity, while achieving similar accuracy (differences between -3.42% to 2.76% Acc@1).
Advancing Biomedical Claim Verification by Using Large Language Models with Better Structured Prompting Strategies
Authors: Siting Liang; Daniel Sonntag
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: In this work, we propose a structured four-step prompting strategy that explicitly guides large language models (LLMs) through (1) claim comprehension, (2) evidence analysis, (3) intermediate conclusion, and (4) entailment decision-making to improve the accuracy of biomedical claim verification. This strategy leverages compositional and human-like reasoning to enhance logical consistency and factual grounding to reduce reliance on memorizing few-Shot exemplars and help LLMs generalize reasoning patterns across different biomedical claim verification tasks. Through extensive evaluation on biomedical NLI benchmarks, we analyze the individual contributions of each reasoning step. Our findings demonstrate that comprehension, evidence analysis, and intermediate conclusion each play distinct yet complementary roles. Systematic prompting and carefully designed step-wise instructions not only unlock the latent cognitive abilities of LLMs but also enhance interpretability by making it easier to trace errors and understand the model’s reasoning process. Our research aims to improve the reliability of AI-driven biomedical claim verification.
A Retrieval-Based Approach to Medical Procedure Matching in Romanian
Authors: Andrei Niculae; Adrian Cosma; Emilian Radoi
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Accurately mapping medical procedure names from healthcare providers to standardized terminology used by insurance companies is a crucial yet complex task. Inconsistencies in naming conventions lead to missclasified procedures, causing administrative inefficiencies and insurance claim problems in private healthcare settings. Many companies still use human resources for manual mapping, while there is a clear opportunity for automation. This paper proposes a retrieval-based architecture leveraging sentence embeddings for medical name matching in the Romanian healthcare system. This challenge is significantly more difficult in underrepresented languages such as Romanian, where existing pretrained language models lack domain-specific adaptation to medical text. We evaluate multiple embedding models, including Romanian, multilingual, and medical-domain-specific representations, to identify the most effective solution for this task. Our findings contribute to the broader field of medical NLP for low-resource languages such as Romanian.
Improving Barrett’s Oesophagus Surveillance Scheduling with Large Language Models: A Structured Extraction Approach
Authors: Xinyue Zhang; Agathe Zecevic; Sebastian Zeki; Angus Roberts
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing)

Abstract: Gastroenterology (GI) cancer surveillance scheduling relies on extracting structured data from unstructured clinical texts, such as endoscopy and pathology reports. Traditional Natural Language Processing (NLP) models have been employed for this task, but recent advancements in Large Language Models (LLMs) present a new opportunity for automation without requiring extensive labeled datasets. In this study, we propose an LLM-based entity extraction and rule-based decision support framework for Barrett’s Oesophagus (BO) surveillance timing prediction. Our approach processes endoscopy and pathology reports to extract clinically relevant information and structures it into a standardised format, which is then used to determine appropriate surveillance intervals. We evaluate multiple state-of-the-art LLMs on real-world clinical datasets from two hospitals, assessing their performance in accuracy and running time cost. The results demonstrate that LLMs, particularly Phi-4 and (DeepSeek distilled) Qwen-2.5, can effectively automate the extraction of BO surveillance-related information with high accuracy, while Phi-4 is also efficient during inference. We also compared the trade-offs between LLMs and fine-tuned non-LLMs. Our findings indicate that LLM extraction based methods can support clinical decision-making by providing justifications from report extractions, reducing manual workload, and improving guideline adherence in BO surveillance scheduling.