United States

Reinforcement learning (RL) is increasingly used in the healthcare domain, particularly for the development of personalized adaptive health interventions. However, RL methods are often applied to this domain using small state spaces to mitigate data scarcity. In this paper, we aim to use Large Language Models (LLMs) to incorporate text-based user preferences and constraints, to update the RL policy. The LLM acts as a filter in the action selection. To evaluate our method, we develop a novel simulation environment that generates text-based user preferences and incorporates corresponding constraints that impact behavioral dynamics. We show that our method can take into account the text-based user preferences, while improving the RL policy, thus improving personalization in adaptive intervention.

NAACL 2025

Using LLMs to improve RL policies in personalized health adaptive interventions

adaptive intervention

large language models

reinforcement learning

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Narrative detection is an important task across diverse research domains where storytelling serves as a key mechanism for explaining human beliefs and behavior. However, the task faces three significant challenges: (1) inter-narrative heterogeneity, or the variation in narrative communication across social contexts; (2) intra-narrative heterogeneity, or the dynamic variation of narrative features within a single text over time; and (3) the lack of theoretical consensus regarding the concept of narrative. This paper introduces the NarraDetect dataset, a comprehensive resource comprising over 13,000 passages from 18 distinct narrative and non-narrative genres. Through a manually annotated subset of ~400 passages, we also introduce a novel theoretical framework for annotating for a scalar concept of "narrativity.'' Our findings indicate that while supervised models outperform large language models (LLMs) on this dataset, LLMs exhibit stronger generalization and alignment with the scalar concept of narrativity.

NarraDetect: An annotated dataset for the task of narrative detection

Integrating external knowledge into large language models has demonstrated potential for performance improvement across a wide range of tasks. This approach is particularly appealing in domain-specific applications, such as in the biomedical field. However, the strategies for effectively presenting external knowledge to these models remain underexplored. This study investigates the impact of different knowledge presentation methods and their influence on model performance. Our results show that inserting knowledge between demonstrations helps the models perform better, and improve smaller LLMs (7B) to perform on par with larger LLMs (175B). Our further investigation indicates that the performance improvement, however, comes more from the effect of additional tokens and positioning than from the relevance of the knowledge.

Leveraging External Knowledge Bases: Analyzing Presentation Methods and Their Impact on Model Performance

The remarkable results shown by medical question-answering systems lead to their adoption in real-life applications. The systems, however, may misinform the users, even when drawing on scientific evidence to ground the results. The quality of the answers may be verified by the users if they analyze the evidence provided by the systems. User interfaces play an important role in engaging the users. While studies of the user interfaces for biomedical literature search and clinical decision support are abundant, little is known about users' interactions with medical question answering systems and the impact of these systems on health-related decisions. In a study
of several different user interface layouts, we found that only a small number of participants
followed the links to verify automatically generated answers, independently of the interface design. The users who followed the links made better health-related decisions.

Will Gen Z users look for evidence to verify QA System-generated answers?

Summarizing clinical trial data poses a significant challenge due to the structured, voluminous, and domain-specific nature of clinical tables. While large language models (LLMs) such as ChatGPT, Llama, and DeepSeek demonstrate potential in table-to-text generation, they struggle with raw clinical tables that exceed context length, leading to incomplete, inconsistent, or imprecise summaries. These challenges stem from the structured nature of clinical tables, complex study designs, and the necessity for precise medical terminology. To address these limitations, we propose an end-to-end pipeline that enhances the summarization process by integrating fact selection, ensuring that only the most relevant data points are extracted for summary generation. Our approach also incorporates a feedback-driven refinement mechanism, allowing for iterative improvements based on domain-specific requirements and external expert input. By systematically filtering critical information and refining outputs, our method enhances the accuracy, completeness, and clinical reliability of generated summaries while reducing irrelevant or misleading content. This pipeline significantly improves the usability of LLM-generated summaries for medical professionals, regulators, and researchers, facilitating more efficient interpretation of clinical trial results. Our findings suggest that targeted preprocessing and iterative refinement strategies within the proposed piepline can mitigate LLM limitations, offering a scalable solution for summarizing complex clinical trial tables.

LLM Based Efficient CSR Summarization using Structured Fact Extraction and Feedback

decision support are abundant, little is known about users' interactions with medical question answering systems and the impact of these systems on health-related decisions. In a study of several different user interface layouts, we found that only a small number of participants followed the links to verify automatically generated answers, independently of the interface design. The users who followed the links made better health-related decisions.

MNLP at PerAnsSumm: A Classifier-Refiner Architecture for Improving the Classification of Consumer Health User Responses

The PerAnsSumm Shared Task at CL4Health@NAACL 2025 focused on Perspective-Aware Summarization of Healthcare Q/A forums, requiring participants to extract and summarize spans based on predefined perspective categories. Our approach leveraged LLM-based zero-shot prompting enhanced by semantically-similar In-Context Learning (ICL) examples. Using Qwen-Turbo with 20 exemplar samples retrieved through NV-Embed-v2 embeddings, we achieved a mean score of 0.58 on Task A (span identification) and Task B (summarization) mean scores of 0.36 in Relevance and 0.28 in Factuality, finishing 12th on the final leaderboard. Notably, our system achieved higher precision in strict matching (0.20) than the top-performing system, demonstrating the effectiveness of our post-processing techniques. In this paper, we detail our ICL approach for adapting Large Language Models to Perspective-Aware Medical Summarization, analyze the improvements across development iterations, and finally discuss both the limitations of the current evaluation framework and future challenges in modeling this task. We release our code for reproducibility.

Lightweight LLM Adaptation for Medical Summarisation: Roux-lette at PerAnsSumm Shared Task

The PerAnsSumm 2024 shared task at the CL4Health workshop focuses on generating structured, perspective-specific summaries to enhance the accessibility of health-related information. Given a Healthcare community QA dataset containing a question, context, and multiple user-answers, the task involves identifying relevant perspective categories, extracting spans from these perspectives, and generating concise summaries for the extracted spans. We fine-tuned open-source models such as Llama-3.2 3B, Llama-3.1 8B, and Gemma-2 9B, while also experimenting with proprietary models including GPT-4o, o1, Gemini-1.5 Pro, and Gemini-2 Flash Experimental using few-shot prompting. Our best-performing approach leveraged an ensemble strategy, combining span outputs from o1 (CoT) and Gemini-2 Flash Experimental. For overlapping perspectives, we prioritized Gemini. The final spans were summarized using Gemini, preserving the higher classification accuracy of o1 while leveraging Gemini's superior span extraction and summarization capabilities. This hybrid method secured fourth place on the final leaderboard among 100 participants and 206 submissions.

AICOE at PerAnsSumm 2025: An Ensemble of Large Language Models for Perspective-Aware Healthcare Answer Summarization

Healthcare community question-answering (CQA) forums have become popular for users seeking medical advice, offering answers that range from personal experiences to factual information. Traditionally, CQA summarization relies on the best-voted answer as a reference summary. However, this approach overlooks the diverse perspectives across multiple responses. Structuring summaries by perspective could better meet users' informational needs. The PerAnsSumm shared task addresses this by identifying and classifying perspective-specific spans (Task_A) and generating perspective-specific summaries from question-answer threads (Task_B). In this paper, we present our work on the PerAnsSumm shared task 2025 at the CL4Health Workshop, NAACL 2025. Our system leverages the RoBERTa-large model for identifying perspective-specific spans and the BART-large model for summarization. We achieved a Macro-F1 score of 0.9 (90%) and a Weighted-F1 score of 0.92 (92%) for classification. For span matching, our strict matching F1 score was 0.21 (21%), while proportional matching reached 0.68 (68%), resulting in an average Task A score of 0.6 (60%). For Task B, we achieved a ROUGE-1 score of 0.4 (40%), ROUGE-2 of 0.18 (18%), and ROUGE-L of 0.36 (36%). Additionally, we obtained a BERTScore of 0.84 (84%), METEOR of 0.37 (37%), and BLEU of 0.13 (13%), resulting in an average Task B score of 0.38 (38%). Combining both tasks, our system achieved an overall average score of 49% and ranked 6th on the official leaderboard for the shared task.

LTRC-IIITH at PerAnsSumm 2025: SpanSense - Perspective-specific span identification and Summarization

This paper presents the approach of the DataHacks team in the PerAnsSumm Shared Task at CL4Health 2025, which focuses on perspective-aware summarization of healthcare community question-answering (CQA) forums. Unlike traditional CQA summarization, which relies on the best-voted answer, this task captures diverse perspectives, including ‘cause,' ‘suggestion,' ‘experience,' ‘question,' and ‘information.' The task is divided into two subtasks: (1) identifying and classifying perspective-specific spans, and (2) generating perspective-specific summaries. We addressed these tasks using Large Language Models (LLM), fine-tuning it with different low-rank adaptation (LoRA) configurations to balance performance and computational efficiency under resource constraints. In addition, we experimented with various prompt strategies and analyzed their impact on performance. Our approach achieved a combined average score of 0.42, demonstrating the effectiveness of fine-tuned LLMs with adaptive LoRA configurations for perspective-aware summarization.

DataHacks at PerAnsSumm 2025: LoRA-Driven Prompt Engineering for Perspective Aware Span Identification and Summarization

In this paper, we describe our submission for the shared task on Perspective-aware Healthcare Answer Summarization. Our system consists of two quantized models of the LlaMA family, applied across fine-tuning and few-shot settings. Additionally, we adopt the SumCoT prompting technique to improve the factual correctness of the generated summaries. We show that SumCoT yields more factually accurate summaries, even though this improvement comes at the expense of lower performance on lexical overlap and semantic similarity metrics such as ROUGE and BERTScore. Our work highlights an important trade-off when evaluating summarization models.

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NarraDetect: An annotated dataset for the task of narrative detection

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