United States

Clinical sources and patient-authored reviews often describe antidepressant side effects in different ways, but these differences are rarely measured directly. We present ClinPeer-AE, a linked dataset for comparing side-effect evidence from PubMed, ClinicalTrials.gov, WebMD, and Drugs.com while preserving source identity. Across five widely prescribed antidepressants, we find low overlap between clinical and peer sources, large differences in relative emphasis, and evidence that many peer-only effects also appear in U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) reports. These findings suggest that patient reviews provide useful context about recurring medication experiences and offer a complementary view of how side effects are described outside formal clinical settings.

ACL 2026

From Trials to Reviews: A Cross-Source Dataset for Antidepressant Side Effects

extraction quality

patient experience

faers

condition-stratified analysis

coverage overlap

salience divergence

evidence provenance

adverse-effect extraction

role classification

drugs.com

webmd

patient-authored narratives

source-aware comparison

linked dataset

antidepressants

patient reviews

clinical evidence

drug safety

clinicaltrials.gov

medical nlp

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

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. <br>
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Retrieval-augmented generation (RAG) holds promise for clinical question answering over electronic health records (EHRs), but existing systems treat retrieval as an opaque subroutine, limiting auditability and reliability in patient care workflows. 
 
 We introduce a deterministic multi-stage retrieval pipeline for longitudinal EHR question answering that decomposes retrieval into four distinct, ablated stages where each stage is instrumented with diagnostic metrics, making the flow of clinical evidence measurable and auditable at every step. Evaluated on a broad LLM-annotated cohort and an expert-annotated cardiovascular benchmark developed alongside clinicians from real ICU records, the full pipeline achieves 22-23% relative recall gain over a strong dense retrieval baseline across both cohorts, with consistent improvements in downstream answer quality. 
 
 The pipeline's deterministic and transparent design addresses a critical gap in clinical NLP: retrieval systems that clinicians and researchers can not only rely on, but inspect, audit, and build upon for real-world deployment.

A Deterministic Multi-Stage Retrieval Pipeline for Longitudinal EHR Question Answering

Transformer-based models such as PLM-CA achieve strong performance for automatic ICD coding, but their attention weights do not provide faithful explanations of their predictions. 
 This is a major limitation for electronic medical records, where users often need concise and trustworthy evidence for each assigned code. 
 To address this issue, we jointly train a sentence extractor and an ICD code classifier such that predictions are based only on the extracted sentences. 
 As a result, the extracted sentences serve as faithful rationales for each predicted code and substantially reduce the effort required to inspect long medical records. 
 Experiments on MIMIC-III show that our method approaches the performance of a transformer baseline that processes the full record while using only a small fraction of the document.

Interpretable ICD Code Classification with Faithful Sentence Extraction

Highly technical medical terms are difficult for patients to understand during fast-paced hospital consultations, leading them to rely on Large Language Models (LLMs) for simplified explanations. However, LLMs can produce inaccurate or false information. Since expert evaluation is costly and time-consuming, LLM-as-a-Judge (LaaJ) approach is increasingly adopted to assess the quality of LLM-generated text. In this paper, we investigate the reliability and robustness of LaaJ for specialized medical knowledge by evaluating six LLMs for their judgment capabilities on three dimensions: correctness, readability, and completeness. We utilized three judgment setups: Vanilla, Epistemic, and Bias to probe robustness, and assess them against human expert annotations to measure alignment. To address the lack of specialized medical benchmarks, we introduce BrainCancerDB, an English dataset of 219 brain cancer terms with 23,652 annotations. Our findings indicate that while LLM-Judges and humans display similar trends in ranking simplified explanations, LLM-Judges tend to be more lenient on correctness, which may have serious implications in medical setting. Additionally, we observe that hallucinations in LaaJ setups can be mitigated by epistemic markers.

Evaluating LLM-as-a-Judge for Medical Term Simplification

Large Language Models such as GPT-4o and GPT-5 achieve strong zero-shot performance on biomedical claim verification, but cost and opacity limit scalable use. We fine-tune three small LLMs; Phi-3-mini (3.8B), Qwen2.5-3B, and Mistral-7B; via QLoRA on SciFact and HealthVer, providing the first study of QLoRA models against GPT-4o and fine-tuned BioLinkBERT encoders. Mistral-7B QLoRA achieves higher F1 than both GPT-4o and GPT-5 (up to 12% gain) at 44.5x lower cost using just 1,008 training examples, representing a compelling cost-quality trade-off. We conduct extensive in-domain and cross-domain evaluation: models trained on SciFact tested on HealthVer and vice versa, at matched sizes to isolate dataset structure from data quantity. We identify a previously unreported structural artifact in SciFact that inflates in-domain scores, and show through bidirectional out-of-domain evaluation that training on structurally sound data enables robust cross-domain transfer. We plan to release all code and adapter checkpoints.

Small LLMs for Biomedical Claim Verification: Cost-Effective Fine-Tuning, Structural Dataset Shortcuts, and Cross-Domain Generalization

Reliable biomedical and clinical retrieval requires more than strong ranking performance: it requires a practical way to find systematic model failures and curate the training evidence needed to correct them.
 Late-interaction models such as ColBERT provide a first solution thanks to the interpretable token-level interaction scores they expose between document and query tokens.
 Yet this interpretability is shallow: it explains a particular document--query pairwise score, but does not reveal whether the model has learned a clinical concept in a stable, reusable, and context-sensitive way across diverse expressions.
 As a result, these scores provide limited support for diagnosing misunderstandings, identifying irreasonably distant biomedical concepts, or deciding what additional data or feedback is needed to address this.
 In this short position paper, we propose Diagnosable ColBERT, a framework that aligns ColBERT token embeddings to a reference latent space grounded in clinical knowledge and expert-provided conceptual similarity constraints.
 This alignment turns document encodings into inspectable evidence of what the model appears to understand, enabling more direct error diagnosis and more principled data curation without relying on large batteries of diagnostic queries.

Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference

Clinical Concept Normalization is essential for clinical research applications involving trial protocols, such as patient-trial matching. Existing approaches focus heavily on specific domains and need large, annotated datasets. To address these challenges, we propose CENT, a context engineering framework that combines semantic matching for candidate retrieval and Large Language Model (LLM) prompting for disambiguation. We applied CENT on a publicly available dataset of procedures normalized to Current Procedural Terminology (CPT) concepts and evaluated the framework using both binary and hierarchical metrics that take into account hierarchical characteristics of predicted codes. CENT achieves superior performance on clinical procedures normalization in both binary and hierarchical metrics compared to semantic matching or LLM-only approaches, without requiring fine-tuning. Advanced prompt strategies, including Chain-of-Thought and Tree-of-Thoughts, achieve high performance at practical cost. We further applied CENT to predict codes in two clinical protocol-derived datasets to validate its performance on noisy procedure texts. CENT is scalable and adaptable for normalization across diverse clinical vocabularies in real-world clinical applications.

CENT: Context Engineering Framework for Normalization of Clinical Trial Procedures

Retrieval-augmented generation (RAG) reduces hallucination in large language models by grounding outputs in retrieved evidence, but it does not guarantee that the resulting citations support the associated claims.
 We present VERICITE, a framework for evaluating citation faithfulness in retrieval-augmented medical QA.
 Our system retrieves PubMed abstracts via the NCBI E-Utilities API, prompts LLMs to generate answers with inline citations, and verifies each citation at the sentence level using a DeBERTa-v3-large NLI model.
 We evaluate four LLMs on 500 BioASQ questions at retrieval depths of 3 and 5, with extended experiments up to k = 15 and an oracle setting with gold standard documents.
 Only 27–41% of citation pairs are supported at the sentence level at retrieval depths of 3 and 5, with support rates declining further at larger k.
 Under the oracle condition, answer quality improves, but citation faithfulness does not substantially improve, suggesting that generation-side citation behavior contributes substantially to unfaithful citations.

VERICITE: Evaluating Sentence-Level Citation Faithfulness in Retrieval-Augmented Medical Question Answering

Biomedical abstracts play a critical role in downstream NLP applications, such as information retrieval, biocuration, and biomedical knowledge discovery. However, a non-trivial number of biomedical articles do not have abstracts, diminishing the utility of these articles for downstream tasks. We propose DPR-BAG (Divide, Prompt, and Refine for Biomedical Abstract Generation), a training-free, zero-shot framework that generates coherent and factually grounded abstracts for biomedical articles with full text but no abstract. DPR-BAG decomposes full-text documents into structured rhetorical facets following the Background-Objective-Methods-Results-Conclusions (BOMRC) schema, performs parallel LLM-based summarization for each facet, and applies a final refinement stage to restore global discourse coherence. On PMC-MAD, a distribution-aligned dataset of 46,309 biomedical articles, DPR-BAG improves abstractive novelty over strong extractive and fine-tuned baselines, while maintaining factual consistency. Our ablation study reveals a counterintuitive finding: increasing prompt complexity or explicitly injecting entity-level guidance can degrade factual alignment, highlighting the importance of controlled prompting strategies. These findings underscore the potential of training-free, structure-aware frameworks for scalable biomedical abstract generation in low-resource settings. Our data and code are available at https://huggingface.co/datasets/pmc-mad/PMC-MAD and https://github.com/ScienceNLP-Lab/MultiTagger-v2/tree/main/DPR-BAG.

Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation

Despite advances in information extraction driven by deep learning and large language models, performance gaps remain in highly specialized biomedical fields, where domain-specific complexity poses challenges for generalist models.
 In this work, we focus on the domain of autoimmunity where the main entities of interest are autoimmune diseases, autoantibodies (i.e. molecules that may mark or cause these diseases), their molecular targets, their location in the body, and the associated clinical signs. 
 Herein, we present AAbAAC (AutoAntibodies and Autoimmunity Annotated Corpus), a corpus of 115 abstracts selected from PubMed that we manually annotated for those entities and their relationships. 
 First, AAbAAC was used to evaluate several methods on the task of named entity recognition (NER), and second, to fine-tune NER models. 
 Our study demonstrates the utility of AAbAAC for information extraction in the domain of autoimmunity, showing expected improvement in NER performance after fine-tuning. This illustrates the value of small-scale annotation efforts for specialized domains and contributes to the computational study of autoimmunity. The AAbAAC corpus is available at: https://github.com/f-maury/AAbAAC .

AAbAAC: An Annotated Corpus for Autoimmunity Information Extraction

Biomedical named entity recognition (NER) and entity linking (EL) strongly depend on annotated corpora, but the utility of these resources for benchmarking is often assumed rather than characterized. We present a corpus-centric framework for diagnosing benchmark-relevant properties directly from corpus annotations, concept links, train-test splits, document metadata, and terminology mappings. The framework organizes standardized statistics into five families: (1) scale, density and label distribution, (2) lexical and conceptual structure, (3) train-test overlap, (4) metadata composition, and (5) terminology coverage where applicable. Applying the framework to nine corpora spanning diseases, chemicals, and cell types, we find that corpus properties can differ substantially, even when they address the same apparent task. We find differences in the evaluation signal they provide, the generalization demands they impose, the degree of train–test reuse they permit, and the regions of biomedical literature and concept space they represent. These differences suggest that commonly reported corpus statistics can be insufficient to characterize what biomedical NER and EL benchmarks evaluate. We argue that corpus-centric diagnostics provide a practical framework for analyzing corpora beyond surface descriptors such as corpus size and entity type, for identifying potential transfer risks, and for interpreting the scope of benchmarking conclusions. We release the framework as open-source code with an interactive dashboard to support reproducing our analyses and characterizing additional corpora.

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A Deterministic Multi-Stage Retrieval Pipeline for Longitudinal EHR Question Answering

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Next from ACL 2026

A Deterministic Multi-Stage Retrieval Pipeline for Longitudinal EHR Question Answering

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Planning to Avoid Side Effects

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