United States

While humans can easily produce various types of answers, such as definitions, examples or paraphrases, Large Language Models (LLMs) struggle to provide correct answers to medical questions that require diverse answer formats. In this paper, we introduce TrackList, a fine-grained linguistic and statistical analysis pipeline to investigate the impact of the pre-training data on LLMs answers to diverse linguistic queries. We also propose RefoMed-EN, a medical dataset consisting of 6,170 human-annotated medical terms alongside their corresponding definitions, denominations, exemplifications, explanations, or paraphrases. We investigated whether the high or low frequency of a concept (head or tail knowledge) impacts the language model&#39;s performance for answering medical questions. We evaluated the quality of the LLM&#39;s output using syntactic and semantic similarity metrics, statistical correlations and embeddings. Results showed that the LLM&#39;s answer quality for definition-type questions is the highest, while for the exemplification-type being the lowest. Additionally, we showed that for definition-type medical questions (&quot;What is multiple sclerosis?&quot;), LLMs are prone to paraphrase more for popular medical concepts, and less on more specialized medical knowledge.

ACL 2026

TrackList: Tracing Back Query Linguistic Diversity for Head and Tail Medical Knowledge in Open Large Language Models

query linguistic diversity

open large language models

model interpretation

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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The University of Texas at Austin 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

While humans can easily produce various types of answers, such as definitions, examples or paraphrases, Large Language Models (LLMs) struggle to provide correct answers to medical questions that require diverse answer formats. In this paper, we introduce TrackList, a fine-grained linguistic and statistical analysis pipeline to investigate the impact of the pre-training data on LLMs answers to diverse linguistic queries. We also propose RefoMed-EN, a medical dataset consisting of 6,170 human-annotated medical terms alongside their corresponding definitions, denominations, exemplifications, explanations, or paraphrases. We investigated whether the high or low frequency of a concept (head or tail knowledge) impacts the language model's performance for answering medical questions. We evaluated the quality of the LLM's output using syntactic and semantic similarity metrics, statistical correlations and embeddings. Results showed that the LLM's answer quality for definition-type questions is the highest, while for the exemplification-type being the lowest. Additionally, we showed that for definition-type medical questions ("What is multiple sclerosis?"), LLMs are prone to paraphrase more for popular medical concepts, and less on more specialized medical knowledge.

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Discharge instructions are patient-facing, safety-critical documents that guide medication use, follow-up care, and recovery after hospitalization. Because they must synthesize information across the clinical record and often include post-discharge guidance not stated verbatim in the EHR, they are a difficult target for clinical text generation. In this work, we study discharge instructions in MIMIC-IV through a grounding-first lens. Using two LLMs, we decompose each discharge instruction into medically relevant statements and verify them against the Electronic Health Record (EHR). We find that discharge instructions for Surgical admissions are much longer, averaging roughly 24--25 statements per admission versus 11--12 in Non-Surgical cases, while supported content remains similar in absolute count. The additional Surgical content is dominated by statements that are not directly stated in the record or require clinically plausible extrapolation. Through this analysis, we advocate for better grounding and completeness evaluations at a fine-grained level, establishing a foundational step toward safer and more reliable discharge-instruction generation.

Discharge Instructions are not One Task: Grounding Differences Between Surgical and Non-Surgical Admissions

Prion diseases are rare, rapidly progressive, and fatal neurodegenerative disorders that remain difficult to diagnose, particularly in their early stages because of nonspecific clinical presentations. However, to our knowledge, there is no publicly available prion-disease-focused dataset designed to capture a broad range of clinically relevant entities from the biomedical literature. We introduce PrionNER, a manually annotated named entity recognition dataset for prion disease clinical information in PubMed abstracts. The current release comprises 317 abstracts, 2,943 sentences, and 6,955 text-bound entity annotations spanning 15 coarse-grained and 31 fine-grained clinically oriented entity types covering diseases, symptoms, diagnostics, findings, anatomy, treatments, and temporal and statistical evidence. Inter-annotator agreement reaches 81.78 exact-match F1, indicating strong annotation consistency. We benchmark supervised BERT baselines, W2NER, and zero-shot extractors on PrionNER. W2NER is the strongest supervised model, and Gemma-4-31B is the strongest zero-shot model, but the benchmark remains challenging, especially for structurally complex mentions and fine-grained clinically adjacent label distinctions. PrionNER provides a clinically grounded benchmark for prion-disease information extraction and supports research on rare-disease biomedical NLP under low-resource, fine-grained, and non-flat extraction conditions. The dataset, annotation guidelines, and evaluation scripts are available at https://github.com/daotuanan/PrionNER/

PrionNER: A Named Entity Recognition Dataset for Prion Disease Biomedical Literature

Individuals with particular mental health disorders may find it difficult to learn about their own condition. Therefore, efforts have been made to create materials that explain complex medical information in simpler words, which are also beneficial for caregivers and others. However, text simplification is commonly done in English and only sporadically in other languages. In this study, we explore potential ways for language-agnostic medical text simplification for the mental health domain. Our approach is to simplify the ICD-11 articles on primary psychotic disorders in English, German and French, using small LMs and various metrics for evaluating different aspects of the texts: lexical complexity and readability. Our results show that acceptable texts were produced only in English, and that a joint analysis of Measure of Textual Lexical Diversity (MTLD) and Flesch Reading Ease (FRE) provides the most insight, capturing both the best outcomes and signaling different types of issue. The study is preliminary and requires further investigation.

Evaluation of Multilingual Text Simplification for the Mental Health Domain: Exploring Small Language Models

Patient portal messages often embed clinical questions inside long, emotionally nuanced narratives, requiring clinicians to infer the underlying information need. We study the task of rewriting verbose patient-authored narratives into concise, clinician-interpreted questions framed as if querying an electronic health record (EHR) system. We evaluate a lightweight LLM-based rewrite pipeline that constrains outputs to 10-15 words and uses rule-based validation with regeneration. We test the approach on 140 distinct patient questions drawn from the ArchEHR-QA dataset and shared task. Each system output is double-annotated by two annotators for quality (Good/Ok/Bad) and error types (Generic, Malformed, Tangential, Hallucination). Results show that while models follow output constraints, they often produce overly generic or tangential questions, and occasional hallucinations introduce unsupported clinical details. Across both clinician-question and patient-narrative comparison settings, automatic metrics show substantial overlap across human quality labels; in pairwise meta-evaluation, BERTScore is the strongest proxy for human preferences. We release our code and annotations to support future work.

Reading Between the Lines: Toward Translating Verbose Patient-authored Messages into Clinician-Formulated Questions

Clinical documentation is essential for patient care, billing, and medical research, but it is subject to entrenched bias. While manual chart reviews can identify such bias, they are labor-intensive and expert-dependent. We introduce and evaluate StigMAD, a Multi-Agent Debate framework leveraging open-source Large Language Models (LLMs) to detect stigmatizing language in clinical documentation. We investigate reasoning (multi-agent debate), self-reflection, and self-consistency within this framework. Extensive experiments on clinical notes and patient summaries demonstrate that our framework provides significant advantages over rule-based and supervised baselines. A domain-specific LLM (MedGemma) achieved its highest performance using the StigMAD reasoning framework, while a general-purpose LLM (Llama) showed superior results with the self-consistency framework. These findings suggest that open-source LLMs, steered by structured prompting and reflective reasoning, can effectively support the scalable auditing of stigmatizing language, marking a critical step toward more equitable clinical NLP systems.

Investigating Stigmatizing Language in Clinical Documentation with Open-Source Large Language Models

Recent high-complexity agentic systems such as DeepRare perform strongly on rare disease diagnosis benchmarks, but it remains unclear when gains come from structured knowledge access and when they come from parametric
 LLM knowledge. We compare phenotypebased retrieval, LLM reranking, and unrestricted LLM diagnosis across seven benchmarks covering 10,382 cases. We find a clear performance crossover driven by retrieval coverage—the fraction of cases whose true diagnosis is within the retriever's top-50: on highcoverage datasets, ontology-based retrieval dominates; on low-coverage datasets, openended LLM diagnosis takes the lead. Building on this, adding an LLM reranker over retrieved candidates further improves accuracy across our patient-case benchmarks, closing most of the remaining gap to agentic systems (within 2 pp on MME and LIRICAL). We trace the crossover to two structural failure modes
 of ontology-based retrieval—annotation sparsity and phenotypic homogeneity—and show that aggregate scores across mixed benchmarks can hide these qualitatively different diagnostic settings. These findings motivate per-dataset evaluation and hybrid diagnostic systems that combine retrieval, reranking, and parametric LLM generation based on case characteristics.

When Does Retrieval Beat Direct LLM Diagnosis in Rare Disease? An Empirical Study of Ontology Coverage

Extracting structured cancer registry information from pathology and medical reports is challenging due to heterogeneous reporting styles and implicit clinical reasoning. We propose a modular multi-agent framework that decomposes registry abstraction into semantic chunking, retrieval, field-specific extraction, validation, evaluation, and aggregation stages.
 
 The dataset includes 818 annotated cancer cases from Sultan Qaboos University Hospital. Evaluation in this study focuses on breast (n=454) and colorectal (n=174) reports across grade, morphology, TNM staging, and laterality extraction tasks. The framework is compared against prompt-based LLaMA 3.3 baselines using accuracy and weighted/macro F1-score metrics.
 
 The proposed framework improved performance in context-dependent tasks, particularly grade extraction, where weighted F1-score increased from 0.71 to 0.78 for breast cancer and from 0.56 to 0.67 for colorectal cancer. Improvements were also observed for colorectal laterality extraction. For other extraction tasks, particularly highly structured tasks such as TNM staging and morphology extraction, the multi-agent framework achieved performance comparable to direct prompting.
 
 Although the baseline achieved slightly higher average weighted F1-scores overall, the proposed framework provides improved modularity, traceability, and pipeline-level interpretability through explicit intermediate reasoning stages, supporting error analysis and future clinician-guided refinement.

A Multi-Agent Open-Source LLM for Structured Cancer Registry Information Extraction from Pathology and Medical Reports

We investigate how tokenization granularity affects the representation of medical terminology in language models. Prior work links tokenization granularity to downstream performance under contextualized settings for specifically pretrained and fine-tuned models. We instead ask whether this relationship already emerges at the level of isolated term representations across existing pretrained models. We introduce an intrinsic definition retrieval task using UMLS term-definition pairs, with comparison to WordNet. We show that despite substantially heavier fragmentation of medical terminology, the models remain relatively robust in maintaining semantic alignment between medical terms and their definitions. At the same time, tokenization granularity still correlates with retrieval performance, indicating that effects previously observed in downstream biomedical tasks are already reflected at the level of isolated term representations. Encoder models benefit primarily from whole-token preservation, while for decoder LLMs, tokenization effects emerge mainly at deeper retrieval ranks.

Tokenization Granularity and Medical Term Representations in Language Models

Clinical dialogue-to-note generation is challenging because clinically salient evidence is noisy, distributed across turns, and often revised later in the encounter. Direct transcript-only prompting and coarse intermediate scaffolds can therefore suffer from omissions, section leakage, unsupported fill-in, and brittle final-state tracking.
 
 We propose Clinical Atomic Propositions (CAPs), a dialogue-aware intermediate representation for faithful clinical note generation. CAPs extract source-grounded clinical assertions while preserving modifiers such as verification status, temporality, speaker/source, and action type. We also study an optional event consolidation layer that groups CAPs into problem-oriented care bundles before note rendering.
 
 We evaluate five methods on a 197-case ACI-Bench cohort: a transcript-only baseline, prompt-based reimplementations of Cluster2Sent and MEDSUM-ENT, CAP, and CAP+Event. The main task uses a sectioned-note template, with SOAP-template rendering and transcript-free rendering reported as ablations. We use MEDSUM-ENT-style GPT-R/P/F1 metrics and a proposition-grounded semCAP-R/P/F1 audit to measure concept-level and source-grounded faithfulness, complemented by case-level win/tie/loss analysis and clinician deep review.
 
 Results show that CAP improves preservation of transcript-grounded clinical propositions while remaining competitive on concept-level GPT metrics. CAP+Event is not uniformly better than CAP, but qualitative and boundary analyses show when problem-oriented consolidation can improve organization and when compression can introduce omissions. We release code, prompts, intermediate representations, generated notes, and evaluation artifacts at a public repository.

CAP: A Source-Grounded Proposition Scaffold for Faithful Clinical Dialogue-to-Note Generation

Clinical reasoning over electronic health records (EHRs) involves heterogeneous operations, including text interpretation, numerical computation, temporal filtering, and guideline-based aggregation. However, many existing LLM-based approaches still cast these heterogeneous operations as a single end-to-end generation process, obscuring their different reliability requirements and making intermediate failures difficult to inspect. We therefore propose a framework based on operation-mechanism alignment that represents clinical reasoning as a directed acyclic graph of typed operations, where each node is assigned to the execution mechanism best suited to its reliability requirements. The framework also preserves structured evidence provenance for intermediate results. Across six clinician-annotated binary decision tasks, the framework outperforms direct prompting, single-step retrieval-augmented prompting, and chain-of-thought baselines, supporting operation-mechanism alignment as a practical design principle for reliable clinical reasoning over EHRs.

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