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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.

ACL 2026

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

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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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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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.

Thesis Proposal: The Missing Why? Building Generative AI That Understands Purpose, Audience, and Context

We argue that LLM-based coding agents frequently fail to solve problems that lie within the model's capacity and the bottleneck is often the conditioning context rather than the model itself. We formalize this for the full class of Turing-computable problems with verifiable specifications and introduce a framework that recasts coding as optimization over a latent space of intentions—natural-language descriptions of high-level solution strategies—and searches this vector space with feedback guidance to steer coding agent toward correct solutions of the problems. The method operates as a plug-in layer that can wrap any coding agent without modifying its architecture or weights. On SWE-Bench Verified, our method raises the resolution rate of a weak, quantized 24B open-weight model to parity with frontier models +25× its size.

Beyond Discrete Search: Divergent Thinking as Optimization in Latent Intention Space

Negotiation involves complex emotional and strategic dynamics that pose challenges for AI agents in negotiation dialogues. This paper proposes a zero-shot soft-labeling method using an large language model-based embedding model and verifies its performance on negotiation dialogues. Furthermore, it examines the performance of predictive model training on rule-based annotated hard and soft labels obtained by the proposed method for the task of predicting whether agreement will be reached from partial dialogues, namely, final disagreement anticipation in negotiation mid-dialogues (FDANMD). Soft labeling obtained by the proposed method showed a maximum HIT@3 score of 0.87 against rule-based annotated hard labels, whereas failure cases also demonstrated the limitations of rule-based annotation. Furthermore, using ROC AUC, evaluations of FDANMD across three datasets (CB, DN, and JI) with negotiation progress rates of 0.25, 0.5, and 1.0 revealed that soft labeling is particularly effective at low negotiation progress rates and also offers superior performance on individual datasets and unseen datasets for models trained on multiple datasets. These results motivate the use of soft labeling to incorporate the complexity of negotiation dialogues into intermediate representations and support the generalizability of zero-shot soft labeling and generalizable predictors across a wide range of negotiations beyond known domains.

LLM-Based Zero-Shot Soft Labeling for Anticipating Disagreement in Negotiation Dialogues

We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the *neglect-zero effect*. This effect refers to the human tendency to ignore *zero-models*, which are configurations that render a proposition vacuously true by virtue of an empty set. We focus on two types of inference driven by the neglect-zero effect, and examine how LLMs process the inference by comparing their behavior with that in inference not involving the neglect-zero effect. For this purpose, we employ a paradigm using *structural priming*, where recent exposure to a preceding sentence (the *prime*) facilitates the processing of a subsequent sentence (the *target*) due to their structural similarity. We prepare primes so that they force LLMs to consider the zero-model, and analyze whether they also consider zero-models in the target. The results indicate the possibility that the neglect-zero effect does not arise within the LLMs analyzed in this study.

Analysis of the Neglect-Zero Effect in Large Language Models

Constrained decoding systems are often built on context-free parsers intended for programming languages. These parsers either degrade into $O(n^3)$ time-complexity or fail entirely if the grammar is not carefully engineered to keep properties such as determinism and non-ambiguity. There is thus a need to design parsers that efficiently handle non-determinism and ambiguity, while simultaneously being incremental so that they can be coupled with the token-based predictions of large language models. Inspired by prior work, we derive an incremental CYK prefix recognizer, which still has $O(n^3)$ complexity but allows for acceleration with only a fraction of GPU resources (stream multiprocessors). Our parser shows robust efficiency in complex context-free grammars while other parsers crash or degrade. At the same time, we remain empirically competitive in restrictive grammar classes such as LALR.

An Incremental CYK Recognizer for GPU-accelerated General Context-free Prefix Validation

The text-to-table task aims to generate structured data in tabular formats from unstructured text. While the integration of large language models (LLMs) has significantly enhanced the comprehensiveness and flexibility of generation, challenges regarding inconsistent output quality persist, such as the inclusion of redundant information and numerical inaccuracies. We propose TableMBR, a robust table generation method that maintains structural consistency through minimum Bayes risk (MBR) decoding. Experimental results showed that TableMBR outperforms the baseline, achieving relative improvements of up to 15% in F1 score on Rotowire and 23% in accuracy on LiveSum.

TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency

Large language models (LLMs) are increasingly used as evaluative tools across languages, yet bias research remains overwhelmingly Anglocentric, with most studies conducted in English using Latin-script names. It remains unclear whether bias patterns generalize across linguistic contexts. We investigate this question and introduce the stereotype perceptual map, a framework for analyzing how ethnic groups are positioned along evaluative dimensions. Using 900,000 model responses over 45,000 names spanning 9 ethnicities, we evaluate model behavior across prompt languages (English, Chinese, Thai), writing scripts (Latin, Chinese, Thai), evaluative domains (competence, warmth), and models (GPT, DeepSeek). We find that ethnic bias hierarchies are jointly shaped by local linguistic context and model origin and differ substantially between Western-centric and Sinocentric models. DeepSeek exhibits highly stable rankings across conditions in math competence judgments, consistently placing Chinese at the top, followed by Russian, and White, Hispanic, and Black names at the bottom. GPT, by contrast, shows strong script-dependent reordering: Latin-script conditions form one stable cluster, while native-script conditions form another, with substantially lower cross-cluster correlations. We term this script-gated bias: transliterating the same names into a non-Latin script can activate a different evaluative frame and produce rankings that are sometimes inversely correlated with Latin-script results. Warmth evaluations are less stable than competence across both models. Our findings demonstrate that multilingual bias cannot be characterized through single-language, single-script audits. For multilingual users, code-switching between languages can toggle between different bias regimes. Fairness evaluations for multilingual LLMs must therefore account for deployment language, writing system, and model origin to capture the full range of potentially harmful bias these systems exhibit.

Through the Looking Glass of Multilingual AI: Contrasting Language- and Name Script-Dependent Ethnic Hierarchies in GPT and DeepSeek

We propose a comprehensive research agenda to detect, measure, and mitigate racial bias in Natural Language Processing (NLP) systems deployed in criminal justice contexts. Our preliminary work demonstrates that racial descriptors systematically alter embedding similarity scores and retrieval rankings across six models, with bias being race-specific and models showing rank displacements of 1.82 to 7.44 positions, on average. This empirically indicates that even small shifts in similarity scores can displace relevant records outside top-10 results, leading to systematic under-retrieval of records involving certain demographic groups. Building on these findings, this thesis proposes four research questions: (1) developing and evaluating debiasing techniques including counterfactual data augmentation, adversarial training, and fairness-constrained fine-tuning; (2) validating synthetic findings on authentic law enforcement data through IRB-approved partnerships; (3) investigating intersectional bias patterns across race, gender, and age; and (4) we extend beyond embedding-level analysis to examine how bias propagates across modern multi-stage retrieval pipelines from embeddings to cross-encoders to LLMs. Expected contributions include empirical comparisons of debiasing methods, bias benchmarks for criminal justice NLP, deployment guidelines for fairness-aware retrieval systems, and the first comprehensive analysis of multi-stage bias propagation in retrieval pipelines.

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Next from ACL 2026

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

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Early Integration of Harm Reduction in Medical Education: A Pilot Curriculum for First-Year Medical Students

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