GenAI4ELab/papercli-papers huggingface dataset:
DKITNLP at ArchEHR-QA 2025: A Retrieval Augmented LLM Pipeline for Evidence-Based Patient Question Answering
Authors: Provia Kadusabe; Abhishek Kaushik; Fiona Lawless
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: This paper describes our submission for the BioNLP ACL 2025 Shared task on grounded Question Answering (QA) from Electronic Health Records (EHRs). The task aims to automatically generate answers to patients’ health related questions that are grounded in the evidence from their clinical notes. We propose a two stage retrieval pipeline to identify relevant sentences to guide response generation by a Large Language Model (LLM). Specifically, our approach uses a BioBERT based bi-encoder for initial retrieval, followed by a re-ranking step using a fine-tuned cross-encoder to enhance retrieval precision. The final set of selected sentences serve as an input to Mistral 7B model which generates answers through few-shot prompting. Our approach achieves an overall score of 31.6 on the test set, outperforming a substantially larger baseline model LLaMA 3.3 70B (30.7), which demonstrates the effectiveness of retrieval-augmented generation for grounded QA.
AEHRC at BioLaySumm 2025: Leveraging T5 for Lay Summarisation of Radiology Reports
Authors: Wenjun Zhang; Shekhar Chandra; Bevan Koopman; Jason Dowling; Aaron Nicolson
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: Biomedical texts, such as research articles and clinical reports, are often written in highly technical language, making them difficult for patients and the general public to understand. The BioLaySumm 2025 Shared Task addresses this challenge by promoting the development of models that generate lay summarisation of biomedical content. This paper focuses on Subtask 2.1: Radiology Report Generation with Layman’s Terms. In this work, we evaluate two large language model (LLM) architectures, T5-large (700M parameter encoder–decoder model) and LLaMA-3.2-3B (3B parameter decoder-only model). Both models are trained under fully-supervised conditions using the task’s multi-source dataset. Our results show that T5-large consistently outperforms LLaMA-3.2-3B across nine out of ten metrics, including relevance, readability, and clinical accuracy, despite having only a quarter of the parameters. Our T5-based model achieved the top rank in both the open-source and close-source tracks of the subtask 2.1.
MetninOzU at BioLaySumm2025: Text Summarization with Reverse Data Augmentation and Injecting Salient Sentences
Authors: Egecan Evgin; Ilknur Karadeniz; Olcay Taner Yıldız
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: In this paper, we present our approach to the BioLaySumm 2025 Shared Task on lay summarization of biomedical research articles, which was conducted as part of the BioNLP Workshop 2025. The aim of the task is to create lay summaries from scientific articles to improve accessibility for a non-expert audience. To this end, we applied preprocessing techniques to clean and standardize the input texts, and fine-tuned Qwen2.5 and Qwen3-based language models for the summarization task. For abstract-based fine-tuning, we investigated whether we can insert salient sentences from the main article into the summary to enrich the input. We also curated a dataset of child-friendly articles with corresponding gold-standard summaries and used large language models to rewrite them into more complex scientific variants to augment our training data with more examples.
Shared Task at Biolaysumm2025 : Extract then summarize approach Augmented with UMLS based Definition Retrieval for Lay Summary generation.
Authors: Aaradhya Gupta; Parameswari Krishnamurthy
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: The paper presents a modular, two‐track lay‐summary generation system for biomedical research articles, evaluated on the PLOS and eLife subsets of the BioLaySumm2025 shared task. In Task 1, it extracts salient sentences via an LLM–based chunking and summarization pipeline, then applies iterative rewriting to produce an accessible summary. In Task 2, it augments that summary with UMLS‐sourced definitions identified by a BioBERT NER model, yielding improved readability and factual consistency, at the cost of slight reductions in n‐gram overlap metrics like ROUGE and BLEU.
RainCityNLP at BioLaySumm2025: Extract then Summarize at Home
Authors: Jen Wilson; Michael Pollack; Rachel Edwards; Avery Bellamy; Helen Salgi
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: As part of the BioLaySumm shared task at ACL 2025, we developed a summarization tool designed to translate complex biomedical texts into layperson-friendly summaries. Our goal was to enhance accessibility and comprehension for patients and others without specialized medical knowledge. The system employed an extractive-then-abstractive summarization pipeline. For the abstractive component, we experimented with two models: Pegasus-XSum and a Falcons.ai model pre-trained on medical data. Final outputs were evaluated using the official BioLaySumm 2025 metrics. To promote practical accessibility, we completed all experimentation on consumer-grade hardware, demonstrating the feasibility of our approach in low-resource settings.
TLPIQ at BioLaySumm: Hide and Seq, a FLAN-T5 Model for Biomedical Summarization
Authors: Melody Bechler; Carly Crowther; Emily Luedke; Natasha Schimka; Ibrahim Sharaf
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: BioLaySumm 2025 is a shared task that aims to automatically generate lay summaries of scientific papers for a wider audience of readers without domain-specific knowledge, making scientific discoveries in the domain of biology and medicine more accessible to the general public. Our submission to the task is a FLAN-T5 base model fine-tuned on the abstract and conclusion of articles and expert-written lay summaries from the shared task’s provided datasets. We find that our system performs competitively in terms of relevance, exceeds the baseline on factuality, but falls short on readability.
LaySummX at BioLaySumm: Retrieval-Augmented Fine-Tuning for Biomedical Lay Summarization Using Abstracts and Retrieved Full-Text Context
Authors: Fan Lin; Dezhi Yu
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: Generating lay summaries of biomedical research remains a time-intensive task, despite their importance in bridging the gap between scientific findings and non-expert audiences. This study introduces a retrieval-augmented fine-tuning framework for biomedical lay summarization, integrating abstract-driven semantic retrieval with LoRA-tuned LLaMA 3.1 models. Abstracts are used as queries to retrieve relevant text segments from full-text articles, which are then incorporated into prompts for supervised fine-tuning. Evaluations on the PLOS and eLife datasets show that this hybrid approach significantly improves relevance and factuality metrics compared to both base models and those tuned individually, while maintaining competitive readability. Prompt design experiments highlight a trade-off between readability and factual accuracy. Our fine-tuned model demonstrates strong performance in relevance and factuality among open-source systems and rivals closed-source models such as GPT, providing an efficient and effective solution for domain-specific lay summarization.
5cNLP at BioLaySumm2025: Prompts, Retrieval, and Multimodal Fusion
Authors: Juan Antonio Lossio-Ventura; Callum Chan; Arshitha Basavaraj; Hugo Alatrista-Salas; Francisco Pereira; Diana Inkpen
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: In this work, we present our approach to addressing all subtasks of the BioLaySumm 2025 shared task by leveraging prompting and retrieval strategies, as well as multimodal input fusion. Our method integrates: (1) zero-shot and few-shot prompting with large language models (LLMs); (2) semantic similarity-based dynamic few-shot prompting; (3) retrieval-augmented generation (RAG) incorporating biomedical knowledge from the Unified Medical Language System (UMLS); and (4) a multimodal fusion pipeline that combines images and captions using image-text-to-text generation for enriched lay summarization. Our framework enables lightweight adaptation of pretrained LLMs for generating lay summaries from scientific articles and radiology reports. Using modern LLMs, including Llama-3.3-70B-Instruct and GPT-4.1, our 5cNLP team achieved third place in Subtask 1.2 and second place in Subtask 2.1, among all submissions.
MIRAGES at BioLaySumm2025: The Impact of Search Terms and Data Curation for Biomedical Lay Summarization
Authors: Benjamin Pong; J u - H u i Chen; Jonathan Jiang; Abimael Jimenez; Melody Vahadi
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: Biomedical articles are often inaccessible to non-experts due to their technical complexity. To improve readability and factuality of lay summaries, we built on an extract-then-summarize framework by experimenting with novel extractive summarization strategies and employing Low Rank Adaptation (LoRA) fine-tuning of Meta-Llama-3-8B-Instruct on data selected by these strategies. We also explored counterfactual data augmentation and post-processing definition insertion to further enhance factual grounding and accessibility. Our best performing system treats the article’s title and keywords (i.e. search terms) as a single semantic centroid and ranks sentences by their semantic similarity to this centroid. This constrained selection of data serves as input for fine-tuning, achieving marked improvements in readability and factuality of downstream abstractive summaries while maintaining relevance. Our approach highlights the importance of quality data curation for biomedicallay summarization, resulting in 4th best overall performance and 2nd best Readability performance for the BioLaySumm 2025 Shared Task at BioNLP 2025.
SUWMIT at BioLaySumm2025: Instruction-based Summarization with Contrastive Decoding
Authors: Priyam Basu; Jose Cols; Daniel Jarvis; Yongsin Park; Daniel Rodabaugh
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: In the following paper, we present our team’s approach to subtask 1.1 of the BioLaySumm 2025 shared task, which entails the automated generation of lay summaries from biomedical articles. To this end, we experiment with a variety of methods for text preprocessing, extractive summarization, model fine-tuning, and abstractive summarization. Our final results are generated on a fine-tuned Llama 3.1 Instruct (8B) model, notably achieving top scores on two out of four relevance metrics, as well as the highest overall ranking among this year’s participating teams on the plain lay summarization subtask.
BDA-UC3M @ BioLaySumm: Efficient Lay Summarization with Small-Scale SoTA LLMs
Authors: Ilyass Ramzi; Isabel Bedmar
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: This paper presents an efficient system for the BioLaySumm 2025 Shared Task on biomedical lay summarization. The approach leverages compact, state-of-the-art language models (4–7 billion parameters), including Gemma3 4B, Qwen3 4B, and GPT-4.1-mini, optimized for relevance, readability, and factuality. Through dynamic 4-bit quantization, parameter-efficient fine-tuning, advanced extractive preprocessing, and direct preference optimization, the system achieves performance competitive with much larger baselines. Comprehensive experiments on the eLife and PLOS datasets demonstrate that small language models can deliver high-quality, accessible biomedical summaries using modest computational resources. The findings suggest that resource-efficient models can help democratize access to scientific information, supporting broader scientific communication goals.
KHU_LDI at BioLaySumm2025: Fine-tuning and Refinement for Lay Radiology Report Generation
Authors: Nur Alya Dania Binti Moriazi; Mujeen Sung
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: Though access to one’s own radiology reports has improved over the years, the use of complex medical terms makes understanding these reports difficult. To tackle this issue, we explored two approaches: supervised fine-tuning open-source large language models using QLoRA, and refinement, which improves a given generated output using feedback generated by a feedback model. Despite the fine-tuned model outperforming refinement on the test data, refinement showed good results on the validation set, thus showing good potential in the generation of lay radiology reports. Our submission achieved 2nd place in the open track of Subtask 2.1 of the BioLaySumm 2025 shared task.
CUTN_Bio at BioLaySumm: Multi-Task Prompt Tuning with External Knowledge and Readability adaptation for Layman Summarization
Authors: Bhuvaneswari Sivagnanam; Rivo Krishnu C H; Princi Chauhan; Saranya Rajiakodi
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: In this study, we presented a prompt based layman summarization framework for the biomedical articles and radiology reports developed as part of the BioLaySumm 2025 shared task at the BioNLP Workshop, ACL 2025. For Subtask 1.1 (Plain Lay Summarization), we utilized the abstract as input and employed Meta-LLaMA-3-8B-Instruct with a Tree-of-Thought prompting strategy and obtained 13th rank. In Subtask 1.2 (Lay Summarization with External Knowledge), we adopted an extractive plus prompt approach by combining LEAD-K sentence extraction with Meta-LLaMA-3-8B-Instruct. Medical concepts were identified using MedCAT, and their definitions were taken from Wikipedia to enrich the generated summaries. Our system secured the 2nd position in this subtask. For Subtask 2.1 (Radiology Report Translation), we implemented a Retrieval-Augmented Generation (RAG) approach using the Zephyr model to convert professional radiology reports into layman terms, achieved 3rd place in the shared task.
Team XSZ at BioLaySumm2025: Section-Wise Summarization, Retrieval-Augmented LLM, and Reinforcement Learning Fine-Tuning for Lay Summaries
Authors: Pengcheng Xu; Sicheng Shen; Jieli Zhou; Hongyi Xin
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: We propose a unified, multi-stage lay summarization pipeline for BioLaySumm 2025 (Subtask 1.1) that (1) selects and summarizes key article sections via BioBART, (2) retrieves K-shot demonstrations using BGE embeddings for in-context Llama 3 8B prompting, (3) applies LoRA adapters to Llama 3 8B for supervised fine-tuning, (4) merges section summaries with a second BioBART pass, and (5) refines outputs through reinforcement learning (PPO & GRPO) using a composite reward of factuality (AlignScore, SummaC), relevance (ROUGE-L, BERTScore), and readability (LENS, FKGL, DCRS, CLI). On PLOS and eLife validation sets, our complete systemreduces DCRS from 9.23 to 8.56 and reduces CLI from 12.98 to 12.65, ranking 3rd in readability. and outperforms llama3 finetune baseline in AlignScore 0.722 to 0.862, ranking 5th in factuality, demonstrating balanced gains across readability, relevance, and factuality.
VeReaFine: Iterative Verification Reasoning Refinement RAG for Hallucination-Resistant on Open-Ended Clinical QA
Authors: Pakawat Phasook; Rapepong Pitijaroonpong; Jiramet Kinchagawat; Amrest Chinkamol; Tossaporn Saengja; Kiartnarin Udomlapsakul; Jitkapat Sawatphol; Piyalitt Ittichaiwong
Venue: ACL 2025 (Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks))

Abstract: We present VeReaFine, a novel “Verifier-RAG” pipeline designed to eliminate hallucinations in open-ended clinical question answering. VeReaFine interleaves three tightly coupled stages—retrieval, verification, and generation—across up to three iterations. First, a two-stage dense retriever (BM-Retriever-410M → BM-Reranker-2B) fetches and ranks top-k biomedical passages; an 8B-parameter MedReason verifier then filters these for direct relevance and identifies missing evidence. When the verifier deems the context insufficient, it formulates a focused “feedback query” to retrieve additional passages (bounded to prevent infinite loops). Once a minimal ground-truth context is assembled, a 7B-parameter generator (Qwen2.5-7B-Instruct) drafts an answer purely from that vetted context, and the verifier performs a final check—prompting the generator to refine any remaining unsupported claims. By iteratively fetching only missing facts and ensuring every assertion is evidence-backed, VeReaFine achieves monotonic factuality improvements with minimal overhead. On the BioNLP 2025 ClinIQLink “LLM Lie-Detector” shared task, our 7B generator augmented with VeReaFine matches or surpasses a 32B medical model on open-ended reasoning metrics, reducing multi-hop inverse step-identification errors by 26%. These findings demonstrate that moderate-size LLMs, when guided by targeted verification loops, can deliver expert-level reliability in clinical QA.
Identifying Filled Pauses in Speech Across South and West Slavic Languages
Authors: Nikola Ljubešić; Ivan Porupski; Peter Rupnik
Venue: ACL 2025 (Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025))

Abstract: Filled pauses are among the most common paralinguistic features of speech, yet they are mainly omitted from transcripts. We propose a transformer-based approach for detecting filled pauses directly from the speech signal, fine-tuned on Slovenian and evaluated across South and West Slavic languages. Our results show that speech transformers achieve excellent performance in detecting filled pauses when evaluated in the in-language scenario. We further evaluate cross-lingual capabilities of the model on two closely related South Slavic languages (Croatian and Serbian) and two less closely related West Slavic languages (Czech and Polish). Our results reveal strong cross-lingual generalization capabilities of the model, with only minor performance drops. Moreover, error analysis reveals that the model outperforms human annotators in recall and F1 score, while trailing slightly in precision. In addition to evaluating the capabilities of speech transformers for filled pause detection across Slavic languages, we release new multilingual test datasets and make our fine-tuned model publicly available to support further research and applications in spoken language processing.
Few-Shot Prompting, Full-Scale Confusion: Evaluating Large Language Models for Humor Detection in Croatian Tweets
Authors: Petra Bago; Nikola Bakarić
Venue: ACL 2025 (Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025))

Abstract: Humor detection in low-resource languages is hampered by cultural nuance and subjective annotation. We test two large language models, GPT-4 and Gemini 2.5 Flash, on labeling humor in 6,000 Croatian tweets with expert gold labels generated through a rigorous annotation pipeline. LLM–human agreement (κ = 0.28) matches human–human agreement (κ = 0.27), while LLM–LLM agreement is substantially higher (κ = 0.63). Although concordance with expert adjudication is lower, additional metrics imply that the models equal a second human annotator while working far faster and at negligible cost. These findings suggest, even with simple prompting, LLMs can efficiently bootstrap subjective datasets and serve as practical annotation assistants in linguistically under-represented settings.
GigaEmbeddings — Efficient Russian Language Embedding Model
Authors: Egor Kolodin; Anastasia Ianina
Venue: ACL 2025 (Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025))

Abstract: We introduce GigaEmbeddings, a novel framework for training high-performance Russian-focused text embeddings through hierarchical instruction tuning of the decoder-only LLM designed specifically for Russian language (GigaChat-3B). Our three-stage pipeline, comprising large-scale contrastive pre-training in web-scale corpora, fine-tuning with hard negatives, and multitask generalization across retrieval, classification, and clustering tasks, addresses key limitations of existing methods by unifying diverse objectives and leveraging synthetic data generation. Architectural innovations include bidirectional attention for contextual modeling, latent attention pooling for robust sequence aggregation, and strategic pruning of 25% of transformer layers to enhance efficiency without compromising performance. Evaluated on the ruMTEB benchmark spanning 23 multilingual tasks, GigaEmbeddings achieves state-of-the-art results (69.1 avg. score), outperforming strong baselines with a larger number of parameters.
PL-Guard: Benchmarking Language Model Safety for Polish
Authors: Aleksandra Krasnodebska; Karolina Seweryn; Szymon Łukasik; Wojciech Kusa
Venue: ACL 2025 (Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025))

Abstract: We present a benchmark dataset for evaluating language model safety in Polish, addressing the underrepresentation of medium-resource languages in existing safety assessments. Our dataset includes both original and adversarially perturbed examples. We fine-tune and evaluate multiple models—LlamaGuard-3-8B, a HerBERT-based classifier, and PLLuM—and find that the HerBERT-based model outperforms others, especially under adversarial conditions.
Dialects, Topic Models, and Border Effects: The Rusyn Case
Authors: Achim Rabus; Yves Scherrer
Venue: ACL 2025 (Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025))

Abstract: In this contribution, we present, discuss, and apply a data-driven approach for analyzing varieties of the Slavic minority language Carpathian Rusyn spoken in different countries in the Carpathian region. Using topic modeling, a method originally developed for text mining, we show that the Rusyn varieties are subject to border effects, i.e., vertical convergence and horizontal divergence, due to language contacts with their respective umbrella languages Polish, Slovak and Standard Ukrainian. Additionally, we show that the method is suitable for uncovering fieldworker isoglosses, i.e., different transcription principles in an otherwise homogeneous dataset.