United States

In-person cognitive assessments are structured clinical conversations that provide important cues about patients&#39; cognition but are difficult to analyze at scale. Existing datasets do not capture both speaker roles and Dialogue-Acts in this setting, limiting progress in modeling conversational structure. We introduce a dataset of anonymized cognitive assessment transcripts annotated with speaker roles and Dialogue-Act labels. We evaluate large language models on two tasks: dialogue-act classification and patient utterance generation. Our results show that while few-shot prompting improves performance, models still struggle to capture fine-grained pragmatic intent and produce clinically consistent responses. These findings highlight a gap between fluent text generation and true dialogue understanding in clinical settings.

ACL 2026

Understanding Clinical Cognitive Dialogues Using Large Language Models

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

In-person cognitive assessments are structured clinical conversations that provide important cues about patients' cognition but are difficult to analyze at scale. Existing datasets do not capture both speaker roles and Dialogue-Acts in this setting, limiting progress in modeling conversational structure. We introduce a dataset of anonymized cognitive assessment transcripts annotated with speaker roles and Dialogue-Act labels. We evaluate large language models on two tasks: dialogue-act classification and patient utterance generation. Our results show that while few-shot prompting improves performance, models still struggle to capture fine-grained pragmatic intent and produce clinically consistent responses. These findings highlight a gap between fluent text generation and true dialogue understanding in clinical settings.

poster

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.

Deploying machine learning models in real-world domain-specific scenarios is challenged by the scarcity of expert annotations and by data drift, where the statistical properties of incoming data continuously evolve. Active Learning (AL) iteratively improves compact models with expert annotations but suffers from recurring cold-start degradation, while LLMs provide strong off-the-shelf performance yet cannot leverage newly accumulated labels, raising the question: how can we better leverage LLMs to assist the active learning process? Through an empirical study on five legal and biomedical datasets, we reveal a complementary temporal dynamic: LLMs excel during early and post-drift stages, while AL-assisted compact models eventually surpass them as annotations accumulate. Motivated by this finding, we propose an ensemble system that combines an LLM, an AL-assisted compact model, and an automatic switch module that routes predictions to the better-performing model in real time. Evaluated under simulated data drift on two mental health datasets, our system achieves 96--98% switch accuracy and consistently outperforms either model used alone.

LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks

Large language model (LLM) agents that invoke external tools must make sequences of interdependent decisions, yet existing uncertainty quantification (UQ) methods treat each step in isolation, ignoring how confidence evolves and compounds across a full task trajectory. We propose a framework for trajectory-level confidence analysis in the tool-use agent setting. The thesis pursues three aims: (1) estimating action-level confidence by adapting step-wise UQ to the heterogeneous think-act-observe cycles of tool-using agents; (2) aggregating the diverse action space into semantically coherent action types to enable meaningful trajectory-level analysis; and (3) discovering temporal patterns in the resulting confidence trajectories that reliably predict task success or failure. We ground the work in standard tool-use benchmarks and expect the framework to expose early warning signals for agent failure and offer interpretable diagnostic tools for understanding when and why LLM agents lose confidence, with improved calibration of multi-step agentic pipelines as a secondary benefit.

Thesis Proposal: When Does an Agent Know It Is Lost? Confidence Trajectory Analysis for Tool-Using LLMs

Crowdsourced annotators and Large Language Models (LLMs) offer complementary, cost-effective ways to obtain labeled data, yet ensuring high label quality remains challenging. We observe that task features influence the accuracy of humans and LLMs, while real-world constraints, such as per-annotator assignment limits, further complicate allocation. Prior work typically addresses either task features or constraints, but not both. We present an integrated framework that (i) estimates per-task accuracy from task features using a \textit{learning from crowds} model and (ii) incorporate these estimations into a linear programming that assigns tasks under practical constraints. Experimental results demonstrate that the proposed method achieves the comparable accuracy to baseline methods while satisfying the given constraints.

Task Assignment meets Annotator Modeling: Human-LLM Collaborative Annotation with Constraints

We propose Dynamic Meta-Metrics (DMM), a framework for machine translation evaluation that learns source-sentence conditioned combinations of existing metrics. Rather than relying on a single static ensemble or language-specific weighting, DMM adapts the metric combination based on properties of the source segment. We study hard conditioning, which fits an interpretable combiner per cluster, and an exploratory soft-conditioned extension whose weights vary continuously with source-cluster responsibilities. We evaluate DMM on the WMT Metrics Shared Task data across multiple language pairs using pairwise agreement measures at the system and segment levels. Across settings, MLP-based combinations outperform linear and Gaussian process-based ensembles, and introducing soft conditioning yields gains over linear models.

Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation

Speech-based screening for mild cognitive impairment offers a highly accessible way to detect early cognitive decline. While most existing work focuses on English, cross-linguistic research is emerging to examine how cognitive decline manifests across languages. Studies on the Interspeech'24 TAUKADIAL dataset, comprising English and Chinese speech recordings, consistently report higher classification performance on Chinese, yet the cause of this cross-lingual discrepancy remains unexplored. We examine this gap using Gemini 2.5 Pro, a multimodal large language model, using zero-shot and in-context-learning (ICL) paradigms. We hypothesize that this disparity is rooted in language typology: in tonal languages like Chinese, pitch encodes lexical meaning in every syllable (tone), whereas in non-tonal languages like English, pitch carries no lexical function. To test this, we pitch-flattened audio from TAUKADIAL and compared how classification performance changed across both languages. We found that Chinese classification degraded significantly under both zero-shot and ICL conditions (-4.78 and -5.92 UAR, respectively), while English performance increased (+0.11 and +2.98 UAR), implicating tonal pitch as the cross-lingual advantage. These findings suggest language typology should inform the design of audio-based cognitive screening tools, with raw audio preferred for tonal languages and text for non-tonal languages, a distinction critical for developing equitable cross-linguistic screening.

Tonal Salience in Cognitive Decline: In-Context MCI Detection with Multimodal LLMs

Tool-using LLM agents are typically compared by accuracy alone, despite deployments being constrained by inference cost. We present a budgeted evaluation of common strategies for improving ReAct-style tool agents (multi-sample aggregation, iterative self-correction, and post-hoc answer revision) using Pareto analysis of cumulative accuracy versus token budget on three benchmarks (HotPotQA, FEVER, GSM8K) with Gemini-2.5-Flash. All experiments use three random seeds ($N{=}500$ per seed); budgeted curves are computed post hoc from per-instance token logs. In our offline evaluation, Reflexion attains the highest accuracy, but its reported token costs are optimistic lower bounds because retries are stopped using ground-truth feedback; deployment costs would be higher. Sampling-based approaches often spend 3--5$\times$ more tokens for comparatively small gains on tool-heavy tasks. GSM8K, a pure-math benchmark with minimal tool interaction, shows dramatically larger gains from all methods, suggesting that the efficiency penalty of repeated tool trajectories is a key factor in tool-heavy settings. We provide a compute-aware evaluation protocol (frontier analysis and marginal-cost metrics) and practical guidance for choosing agent designs under different budget regimes.

What Moves the Pareto Frontier in Tool-Using Agents? A Compute-Aware Study of ReAct Variants

Medical education resources are dense for common diseases but often sparse for under-covered conditions, atypical presentations, and fine-grained concept distinctions. This creates curriculum gaps that are difficult to repair manually, especially in long-tail domains where structured teaching materials are limited. We introduce Curriculum-Gap Completion (CGC), a new task for Large Language Model (LLM)-based medical education in which a model reconstructs missing educational units from a partially specified curriculum graph. Given topic nodes, pedagogical relations, and structured teaching slots, the model predicts omitted concepts, restores missing instructional links, and completes automatically verifiable teaching content. We instantiate this setting in a long-tail medical case study (hyperhidrosis) and evaluate five LLMs under three methods: direct prompting, retrieval-augmented prompting, and our proposed Structure-Aware Curriculum-Gap Completion (SACGC) framework. Across models, SACGC achieves the strongest overall performance, with the largest gains on structurally demanding masking settings. Ablation results show that explicit graph structure is the most important component, while schema constraints provide additional benefit. These findings suggest that LLMs are better suited for reconstructing an under-specified educational structure than for unrestricted medical tutoring, and they motivate CGC as a new natural language processing (NLP) problem for healthcare education.

Filling the Long Tail: Structure-Aware Curriculum-Gap Completion for Medical Education with LLMs

Modern LLMs have demonstrated advanced reasoning skills, including the ability to solve Olympiad-level mathematics problems. While solving more and more difficult problems is a hallmark of LLM progress, less attention has been placed on how "difficulty" is operationalized in the context of LLM problem solving tasks. This is particularly relevant in educational contexts where teachers or students may ask LLMs for "easy" or "hard" questions. In this paper, we explore various quantitative measurements from LLM-generated solutions and evaluate their inter-correlations, as well as their correlation to human-annotated difficulty scores. We find moderate correlations between metrics using log probabilities and output lengths, including some that are more strongly correlated to difficulty than LLM accuracy. We also train ModernBERT to predict difficulty scores, leading to reasonable accuracy within a given benchmark, but decreased performance when generalizing to other math benchmarks. Finally, to explore connections between difficulty scores and human performance, we collect problems, human solutions, and human performance data from the Putnam competition. We find poor alignment between LLM metrics and human-assigned difficulty scores, despite strong correlations between those scores and human performance on the problems.

How Hard is Math? Using Quantitative Metrics to Measure LLM Alignment to Human Intuitions of Difficulty

Frequent revisions of complex regulatory documents in large organizations often introduce inconsistencies and contradictions that are difficult for lawyers and auditors to detect manually. Existing tools rely on character‑level diffs and therefore miss paraphrases and semantic shifts. We introduce LegDiff, a novel benchmark for evaluating span‑aware semantic comparison of legal texts, and use it to investigate the ability of large language models to detect semantic changes beyond token‑ and character‑level matching. LegDiff comprises manually annotated pairs of legal paragraphs drawn from different documents. In addition, we present a pipeline to generate synthetic training data that aligns with the manual annotations and closely resembles real‑world data, and a visualization tool for clearly displaying detected differences and inconsistencies. The dataset, code, and a visualization tool are publicly available to facilitate reproducibility and further research (https://anonymous.4open.science/r/LDC-5BF6/README.md).

Fine-Grained Semantic Comparison of Legal Documents using LLMs

Modern generative AI systems can produce fluent text, clear diagrams, and polished presentations—but they still fail in high-stakes scientific settings. The core issue is not fluency, but the lack of understanding of why content is generated, for whom, and how it should adapt as goals evolve. In this proposal, I argue that effective scientific communication requires modeling user intent, audience variation, and multimodal structure—not just surface quality. To address these gaps, I develop systems across the communication pipeline: ADAPTIVE IE for intent-driven information extraction, GPA and Persona-Aware Slide Generation for audience-aware content adaptation, SciDoc2Diagrammer-MAF for faithful diagram generation, and SMART-Editor for structurally coherent layout refinement. Together, these systems demonstrate that aligning with intent, audience, and structure is essential for meaningful communication. However, existing approaches still lack a principled way to evaluate and generalize usefulness. To address this, I propose three directions: (1) a goal-driven framework to measure the educational utility of document-to-video generation, (2) studying multimodal personalization to improve both creation and comprehension, and (3) building a unified SuperPersonalization benchmark to learn transferable user preferences across tasks. This work shifts the focus from generating content that looks correct to content that actually communicates.

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LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks

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