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Traditional topic modeling treats each document as a single, coherent unit of topic, which can cause topic contamination when documents cover multiple topics. This becomes especially problematic when stakeholders are interested in identifying documents that focus on a specific topic. We introduce segment-based topic allocation, a novel paradigm that redefines topic assignment at the level of segments, coherent textual spans conveying distinct topical content. This granularity improves topic purity, interpretability, and applicability to multi-theme corpora such as reviews or survey responses. To support this paradigm, we construct SemEval-STM, a benchmark derived from aspect-based sentiment datasets, where segments are automatically extracted using large language models (LLMs) and post-processed with human supervision. We further propose the segment intrusion task (SIT), a novel evaluation method extending word intrusion to the span level, enabling human-centric assessment of topical coherence. Empirical results across diverse metrics and models demonstrate that SBTA significantly outperforms traditional document-based methods in clustering and interpretability. Our framework provides a practical and scalable solution for fine-grained topic analysis in heterogeneous text corpora.

ACL 2026

From Documents to Segments: A Contextual Reformulation for Topic Assignment

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. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><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.

ASR errors are typically analysed at the phoneme level, treating phonemes as atomic symbols. In this work, we instead adopt a featural representation of phonemes, grounded in phonological theory, which models speech sounds as structured bundles of distinctive articulatory and acoustic properties. This perspective allows us to analyse recognition errors at a finer granularity and to investigate whether certain phonological features are more vulnerable than others. Across multiple languages, we show that phoneme confusions are strongly structured in phonological feature space: errors are predominantly local and exhibit systematic asymmetries that reveal a small set of weakly modelled features. These findings have direct implications both for the design and diagnosis of ASR systems and for cognitive models of human speech perception, where similar feature-level asymmetries have long been observed.

 What Do Neural Speech Models Know About Phonology? Evidence from Structured Phoneme Confusions

Zero-shot ASR for Arabic remains challenging: while multilingual models perform well on Modern Standard Arabic (MSA), error rates rise sharply on dialectal and accented speech due to linguistic mismatch and scarce labeled data. We study $\textit{context-aware}$ decoding as a lightweight test-time adaptation paradigm that conditions inference on external side information without parameter updates. For promptable encoder–decoder ASR (e.g., Whisper), we incorporate context through (i) decoder prompting with first-pass hypotheses and (ii) encoder/decoder prefixing with retrieved speech-text exemplars, complemented by simple prompt reordering and optional speaker-matched synthetic exemplars to improve robustness in informal and multi-speaker settings. To extend contextual adaptation beyond promptable architectures, we introduce $\textit{proxy-guided $n$-best selection}$ for CTC ASR: given one or more external proxy hypotheses, we select from a model's $n$-best list by minimizing text-level distance to the proxies, enabling contextual inference without direct prompting. Across ten Arabic conditions spanning MSA, accented MSA, and multiple dialects, the best-performing context-aware variants yield average relative WER reductions of 22.29\% on MSA, 20.54\% on accented MSA, and 9.15\% on dialectal Arabic. For CTC ASR on our Common Voice MSA testbed, proxy-guided selection reduces WER by 15.6\% relative and recovers a substantial fraction of oracle $n$-best gains, showing that external-context guidance can also benefit non-promptable ASR.

Zero-Shot Context-Aware ASR for Diverse Arabic Varieties

Model merging has emerged as a promising technique for combining multiple fine-tuned models into a single multitask model without retraining. However, the factors that determine whether merging will succeed or fail remain poorly understood. In this work, we investigate why specific models are merged better than others. To do so, we propose a concrete, measurable definition of mergeability. We investigate several potential causes for high or low mergeability, highlighting the base model knowledge as a dominant factor: Models fine-tuned on instances that the base model knows better are more mergeable than models fine-tuned on instances that the base model struggles with. Based on our mergeability definition, we explore a simple weighted merging technique that better preserves weak knowledge in the base model.

 Will it Merge? On The Causes of Model Mergeability

Large Language Models (LLMs) have shown strong promise for mining Electronic Health Records (EHRs) by reasoning over longitudinal clinical information to capture context-rich patient trajectories. However, leveraging LLMs for structured EHRs (e.g., standardized diagnosis and medication codes) presents two key challenges. First, translating time-stamped EHR sequences into plain text can obscure both temporal structure and code identities, weakening the ability to capture code co-occurrence and longitudinal regularities. Second, unlike cohort-trained predictive models that learn a shared, task-aligned representation space across patients, LLMs are often applied in a case-isolated inference setting where each patient is processed independently without leveraging population-level patterns. To address these challenges, we introduce RePrompT, a time-aware LLM framework that integrates structured EHR encoders through prompt tuning, without modifying underlying architectures. Specifically, RePrompT recurrently incorporates latent states from prior visits to preserve longitudinal information, and injects population-level information through trainable prompt tokens derived from a cohort-trained, task-aligned EHR encoder. Experiments on MIMIC-III and MIMIC-IV demonstrate that RePrompT consistently outperforms both EHR-based and LLM-based baselines across multiple clinical prediction tasks.

RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models

Tiny Recursive Models (TRMs) perform iterative reasoning with an Adaptive Computation Time (ACT)-style loop, but their supervised training targets can be brittle, and their halting behavior can be difficult to tune. We introduce TRM-Planner, a two-stage teacher-cache distillation recipe that shifts compute to an offline teacher-cache stage. A frozen TRM checkpoint is unrolled for multiple refinement steps and stochastic rollouts; for each instance, we cache a small set of teacher entries (tokens, logits, step index, and quality metadata). A student TRM is then trained with the standard TRM objective plus a distillation loss computed from cached entries. Across Sudoku-Extreme and ARC-AGI-1/2, TRM-Planner shows an improvement over our reproduced TRM baseline while leaving student-time inference unchanged. On ARC1/ARC2 with 7M parameters, the two-attempt accuracy (pass@2) increases from 43.1% to 48.1% and 6.7% to 9.2%, respectively.

 TRM-Planner: Offline Target Planning and Distillation for Tiny Recursive Models

Large Language Models (LLMs) exhibit social biases, which can lead to harmful stereotypes and unfair outcomes. We propose Multi-Persona Thinking (MPT), a simple inference-time framework that reduces social bias by encouraging reasoning from multiple perspectives. MPT guides the model to consider contrasting social identities, such as male and female, together with a neutral viewpoint. These viewpoints then interact through an iterative reasoning process to identify and correct biased judgments. This design transforms the potential weakness of persona assignment into a mechanism to mitigate bias. We evaluate MPT on two widely used bias benchmarks with both open-source and closed-source models. Our results show that MPT achieves a lower bias than the existing prompting-based methods while maintaining the core reasoning ability.

 Multi-Persona Thinking for Bias Mitigation in Large Language Models

The combination of Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) has shown significant potential for enhancing the multi-task learning capabilities of Large Language Models. However, existing methods face two primary challenges: (1)Imprecise Routing in the current MoE-LoRA method fails to explicitly match input semantics with expert capabilities, leading to weak expert specialization. (2)Uniform weight fusion strategies struggle to provide adaptive update strengths, overlooking the varying complexity of different tasks. To address these limitations, we propose \textbf{SAMoRA} (\textbf{S}emantic-\textbf{A}ware \textbf{M}ixture \textbf{o}f Lo\textbf{RA} Experts), a novel parameter-efficient fine-tuning framework tailored for task-adaptive learning. Specifically, A \textbf{Semantic-Aware Router} is proposed to explicitly align textual semantics with the most suitable experts for precise routing. A \textbf{Task-Adaptive Scaling} mechanism is designed to regulate expert contributions based on specific task requirements dynamically. In addition, a novel regularization objective is proposed to jointly promote expert specialization and effective scaling. Extensive experiments on multiple multi-task benchmarks demonstrate that SAMoRA significantly outperforms the state-of-the-art methods and holds excellent task generalization capabilities. Code is available at \url{https://github.com/boyan-code/SAMoRA}

 SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning

Finite-State Transducers (FSTs) are effective models for string-to-string rewriting tasks, often providing the efficiency necessary for high-performance applications, but constructing transducers by hand is difficult. In this work, we propose a novel method for automatically constructing unweighted FSTs following the hidden state geometry learned by a recurrent neural network. We evaluate our methods on real-world datasets for morphological inflection, grapheme-to-phoneme prediction, and historical normalization, showing that the constructed FSTs are highly accurate and robust for many datasets, massively outperforming classical transducer learning algorithms by up to 87% accuracy on held-out test sets.

 Neural Induction of Finite-State Transducers

Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfully represent the true ``forgetting scope'' learned by the model. 
We formalize two distinct unlearning granularities, domain-level and instance-level, and propose \BiForget, an automated framework for synthesizing high-quality forget sets.
Unlike prior work relying on \emph{external} generators, \BiForget exploits the target model per se to elicit data that matches its internal knowledge distribution through seed-guided and adversarial prompting. 
Our experiments across diverse benchmarks show that it achieves a superior balance of relevance, diversity, and efficiency. 
Quantitatively, in the Harry Potter domain, it improves relevance by ${\sim}20$ and diversity by ${\sim}$0.05 while \emph{halving} the total data size compared to SOTAs. 
Ultimately, it facilitates more robust forgetting and better utility preservation, providing a more rigorous foundation for evaluating LLM unlearning.

From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning

Causal self-attention provides positional information to Transformer decoders. Prior work has shown that stacks of causal self-attention layers alone induce a positional bias in attention scores toward earlier tokens. However, this differs from the bias toward later tokens typically observed in Transformer decoders, known as recency bias. We address this discrepancy by analyzing the interaction between causal self-attention and other architectural components. We show that stacked causal self-attention layers combined with LayerNorm induce recency bias. Furthermore, we examine the effects of residual connections and the distribution of input token embeddings on this bias. Our results provide new theoretical insights into how positional information interacts with architectural components and suggest directions for improving positional encoding strategies.

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What Do Neural Speech Models Know About Phonology? Evidence from Structured Phoneme Confusions

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