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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1920ACL Anthology · ACL 2026

SafeMT: Multi-turn Safety for Multimodal Language Models

Han Zhu, Juntao Dai Peking University, Jiaming Ji, Haoran Li, Chengkun Cai University of Edinburgh, University of Edinburgh, Pengcheng Wen, Chi-Min Chan, Boyuan Chen 等 11 位作者

原文摘要

With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interactions, pose a greater risk than single prompts; however, existing benchmarks do not adequately consider this situation. To encourage the community to focus on the safety issues of these models in multi-turn dialogues, we introduce SafeMT, a benchmark that features dialogues of varying lengths generated from harmful queries accompanied by images. This benchmark consists of 10,000 samples in total, encompassing 17 different scenarios and four jailbreak methods. Additionally, we propose Safety Index (SI) to evaluate the general safety of MLLMs during conversations. We assess the safety of 17 models using this benchmark and discover that the risk of successful attacks on these models increases as the number of turns in harmful dialogues rises. This observation indicates that the safety mechanisms of these models are inadequate for recognizing the hazard in dialogue interactions. We propose a dialogue safety moderator capable of detecting malicious intent concealed within conversations and providing MLLMs with relevant safety policies. Experimental results from several open-source models indicate that this moderator is more effective in reducing multi-turn Attack Success Rate (ASR) compared to existed guard models.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1921ACL Anthology · ACL 2026

From \log \pi to \pi: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight

Xiaoliang Fu Fudan University, Jiaye Lin, Yangyi Fang Tsinghua University, Chaowen Hu Zhejiang University, Cong Qin Peking University, Zekai Shao, Binbin Zheng University of Science and Technology of China, Lu Pan Meituan 等 9 位作者

原文摘要

Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics remain fragile. Standard algorithms like GRPO enforce stability via “hard clipping”, which inadvertently stifles exploration by discarding gradients of tokens outside the trust region. While recent “soft clipping” methods attempt to recover these gradients, they suffer from a critical challenge: relying on log-probability gradient (\nabla_\theta\log \pi_\theta) yields divergent weights as probabilities vanish, destabilizing LLM training. We rethink this convention by establishing probability gradient (\nabla_\theta \pi_\theta) as the superior optimization primitive. Accordingly, we propose Decoupled Gradient Policy Optimization (DGPO), which employs a decoupled decay mechanism based on importance sampling ratios. By applying asymmetric, continuous decay to boundary tokens, DGPO resolves the conflict between stability and sustained exploration. Extensive experiments across DeepSeek-R1-Distill-Qwen series models (1.5B/7B/14B) demonstrate that DGPO consistently outperforms strong baselines on various mathematical benchmarks, offering a robust and scalable solution for RLVR. Our code and implementation are available at: https://github.com/FlyTune/DGPO-RL.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1922ACL Anthology · ACL 2026

Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages

Mitodru Niyogi CNRS, Eric Gaussier University Grenoble Alpes, Arnab Bhattacharya IIT Kanpur

原文摘要

Multilingual large language models (LLMs) are expensive to pretrain and often suffer from imbalances across languages and datasets, English-centric bias, tokenizer oversegmentation for morphologically rich low-resource languages, and the curse of multilinguality. We introduce PARAMANU, a family of Indian language-only autoregressive language models trained from scratch on open-source language-specific data for the five most spoken Indian languages: Bangla (Bengali), Hindi, Marathi, Tamil, and Telugu. All models are designed for affordability and are trained on a single GPU with a budget under 1,000, allowing under-resourced researchers to build competitive language models. To address low-resource challenges, we develop morphology-aligned, low-fertility tokenizers, and propose an interpolation-based method for token position indices in RoPE based scaling to train longer sequences efficiently. We also create instruction-tuning datasets in Bangla that are then translated to the other four languages. Despite their small size (108M-367M parameters), Paramanu achieves a strong performance-efficiency tradeoff and outperforms most larger multilingual models up to 8B across all five languages. The models and datasets are available at: https://huggingface.co/collections/mitodru/paramanu.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1923ACL Anthology · ACL 2026

Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models

Tobias Grantner Dynatrace, Emanuel Sallinger TU Wien (Vienna University of Technology) and University of Oxford, Martin Flechl Dynatrace

原文摘要

Transformer-based embedding models suffer from quadratic computational and linear memory complexity, limiting their utility for long sequences. We propose recurrent architectures as an efficient alternative, introducing a vertically chunked inference strategy that enables fast embedding generation with memory usage that becomes constant in the input length once it exceeds the vertical chunk size. By fine-tuning Mamba2 models, we demonstrate their viability as general-purpose text embedders, achieving competitive performance across a range of benchmarks while maintaining a substantially smaller memory footprint compared to transformer-based counterparts. We empirically validate the applicability of our inference strategy to Mamba2, RWKV, and xLSTM models, confirming consistent runtime-memory trade-offs across architectures and establishing recurrent models as a compelling alternative to transformers for efficient embedding generation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1924ACL Anthology · ACL 2026

Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models

Binghai Wang, Yantao Liu Qwen Team, Alibaba Group, Yuxuan Liu Qwen Team, Alibaba Group, Tianyi Tang Alibaba Group, Shenzhi Wang Alibaba Group, Chang Gao Alibaba Group, Chujie Zheng Alibaba Group, Yichang Zhang Alibaba Group 等 16 位作者

原文摘要

Generative Reward Models (GenRMs) and LLM-as-a-Judge exhibit deceptive alignment by producing correct judgments for incorrect reasons, as they are trained and evaluated to prioritize Outcome Accuracy, which undermines their ability to generalize during RLHF. We introduce Rationale Consistency, a fine-grained metric that quantifies the alignment between the model’s reasoning process and human judgment. Our evaluation of frontier models reveals that rationale consistency effectively discriminates among state-of-the-art models and detects deceptive alignment, while outcome accuracy falls short in both respects. To mitigate this gap, we introduce a hybrid signal that combines rationale consistency with outcome accuracy for GenRM training. Our training method achieves state-of-the-art performance on RM-Bench (87.1%) and JudgeBench (82%), surpassing outcome-only baselines by an average of 5%. Using RM during RLHF, our method effectively improves performance as demonstrated on Arena Hard v2, notably yielding a 7% improvement in creative writing tasks. Further analysis confirms that our method escapes the deceptive alignment trap, effectively reversing the decline in rationale consistency observed in outcome-only training.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1925ACL Anthology · ACL 2026

CIS-BWE: Chaos-Informed Speech Bandwidth Extension

Tarikul Islam Tamiti, Tonmoy Das, Nursadul Mamun Chittagong University of Engineering and Technology, Anomadarshi Barua George Mason University

原文摘要

We design CIS-BWE, a novel adversarial Bandwidth Extension (BWE) framework that introduces two chaos-informed discriminators - Multi-Resolution Lyapunov Discriminator (MRLD) and Multi-Scale Detrended Fractal Analysis Discriminator (MSDFA) - for capturing the deterministic chaos from speech. MRLD exploits Lyapunov exponents to capture nonlinear chaotic fluctuations. MSDFA exploits detrended fluctuation analysis to quantify fractal-like, long-range temporal chaotic correlations. To the best of our knowledge, MRLD and MSDFA are included here for the first time with a complex-valued adversarial network to explore the chaotic study of speech reconstruction. We also introduce a novel complex-valued and dual-stream generator, which uses our newly proposed ConformerNeXt as a core block with Lattice interactions, acting as a gating mechanism by enabling controlled mixing of information across streams. We extensively optimize our design across five resolutions and use depth-wise separable convolutions to make our model lightweight yet powerful. Our CIS-BWE is tested with a de facto English and French dataset for clean and noisy speech for generalization. It achieves better performance across a total of nine subjective and objective evaluation metrics with a 40x reduction in discriminator size and overall 0.5x fewer parameters, establishing a new baseline in the BWE task.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1926ACL Anthology · ACL 2026

The Pitfalls of KV Cache Compression

Alex Chen, Renato Geh University of California, Aditya Grover University of California, Los Angeles, Guy Van Den Broeck, Daniel Mingyi Israel

原文摘要

KV cache compression promises increased throughput and efficiency with negligible loss in performance. While the gains in throughput are indisputable and recent literature has indeed shown minimal degradation on particular benchmarks, in general the consequences of compression in realistic scenarios such as multi-instruction prompting have been insufficiently studied. In this paper, we identify several pitfalls that practitioners should be aware of when deploying KV cache compressed LLMs. We evaluate five KV cache compression methods (StreamingLLM, SnapKV, TOVA, H2O, and K-Norm) on Llama3.1 8B and Qwen2.5 14B under multi-instruction prompting with IFEval. Importantly, we show that certain instructions degrade much more rapidly with compression, effectively causing them to be completely ignored by the LLM. As a practical example, we highlight system prompt leakage as a case study, empirically demonstrating the impact of compression on leakage and general instruction-following. We identify several factors that contribute to system prompt leakage: compression method, instruction order, and KV eviction bias. We then propose simple changes to KV cache eviction policies that can reduce the impact of these factors and improve the overall performance in multi-instruction tasks.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1927ACL Anthology · ACL 2026

Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models

Georg Ahnert Universität Mannheim, Anna-Carolina Haensch University of Maryland, College Park and Ludwig-Maximilians-Universität München, Barbara Plank Ludwig-Maximilians-Universität München, Markus Strohmaier Universität Mannheim and GESIS – Leibniz Institute for the Social Sciences

原文摘要

Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LLMs are typically trained to generate open-ended text instead. Previous research has used a diverse range of methods for generating closed-ended survey responses with LLMs and a standard practice remains to be identified. In this paper, we systematically investigate the impact that various Survey Response Generation Methods have on predicted survey responses. We present the results of 32 mio. simulated survey responses across 8 Survey Response Generation Methods, 4 political attitude surveys, and 10 open-weight language models. We find significant differences between the Survey Response Generation Methods in both individual-level and subpopulation-level alignment. Our results show that Restricted Generation Methods perform best overall, and that reasoning output does not consistently improve alignment. Our work underlines the significant impact that Survey Response Generation Methods have on simulated survey responses, and we develop practical recommendations on the application of Survey Response Generation Methods.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1928ACL Anthology · ACL 2026

Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards

Yiming Ni University of Washington, Zhi-Qi Cheng University of Washington Tacoma and University of Washington Graduate School, Jiayu Li University of Washington, Wei Cheng University of Washington

原文摘要

Sign languages are expressive visual languages used by Deaf and Hard-of-Hearing (DHH) communities. Despite substantial progress in sign-language recognition, translation, and production, advances remain constrained by fragmented datasets, inconsistent annotations, and limited linguistic coverage. Existing benchmarks often fail to reflect real-world communication needs, and systematic analyses of these limitations remain limited. In this survey, we present a comprehensive index of sign-language datasets, covering 120 resources across 35 sign languages. We analyze key challenges such as modality imbalance, annotation granularity, and signer bias, and outline considerations for future dataset design. We also introduce a 24-field Sign-Language Datasheet and release a public GitHub repository to support standardized documentation and reproducible evaluation. Overall, our work provides a unified and practical foundation for developing inclusive, robust, and scalable sign-language technologies in real-world applications.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1929ACL Anthology · ACL 2026

Anchoring the Affective Manifold: Learning Canonical and Disentangled Representations via Generative Cross-Modal Alignment

Weibin Li, Jintao Cheng, Xiaoyu Tang, Chi Man Vong University of Macau

原文摘要

Dominant multimodal emotion recognition paradigms often neglect the intrinsic geometric structure of affect, resulting in representations heavily entangled with non-affective factors. To address this, we propose a Canonical Disentangled Multimodal Generative Framework aimed at recovering the canonical affective manifold from raw data. We explicitly decompose the latent space into a canonical Shared Affective Subspace (z_{vad}) and a Private Modality Subspace (z_{priv}). We facilitate this factorization through Supervised Manifold Anchoring and Cross-Modal Manifold Alignment. Experiments demonstrate that our model effectively disentangles affect from private attributes (e.g., identity), achieving superior robustness in zero-shot cross-domain transfer compared to fully supervised baselines, while enabling controllable emotion generation.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.193ACL Anthology · ACL 2026

Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering

Li Zheng, Xin Zhang Ant Group and Microsoft, Shuyi He, Fei Li Wuhan University, Chong Teng Wuhan University, Jiang-Ming Yang, Donghong Ji Wuhan University, Zhuang Li Royal Melbourne Institute of Technology

原文摘要

Accurate comprehension and controllable generation of emotion and rhetoric are pivotal for enhancing the reasoning capabilities of large language models (LLMs). Existing studies mostly rely on external optimizations, lacking in-depth exploration of internal representation mechanisms, thus failing to achieve fine-grained steering at the neuron level. A handful of works on neurons are confined to emotions, neglecting rhetoric neurons and their intrinsic connections. Traditional neuron masking also exhibits counterintuitive phenomena, making reliable verification of neuron functionality infeasible. To address these issues, we systematically investigate the neurons representation mechanisms and inherent associations of 6 emotion categories and 4 core rhetorical devices. We propose a neuron identification framework that integrates multi-dimensional screening, and design an adaptive masking method incorporating dynamic filtering, attenuation masking, and feedback optimization, enabling reliable functional validation of neuron functionality. Through neuron regulation, we achieve directed induction of non-target sentences and enhancement of emotion tasks via rhetoric neurons. Experiments on 5 commonly used datasets validate the effectiveness of our method, providing a novel paradigm for the fine-grained steering of emotion and rhetoric expressions in LLMs.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1930ACL Anthology · ACL 2026

Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction

Yifan Song University of Illinois at Urbana-Champaign, Wenxuan Wendy Shi California Polytechnic State University, Pomona, Brian Bailey, Tal August University of Illinois at Urbana-Champaign

原文摘要

Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or data-driven clustering rather than theory-grounded taxonomies. We operationalize a taxonomy of eight communication roles grounded in education literature and annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project. We evaluate whether LLMs can approximate expert labels, enabling scalable, taxonomy-driven role annotation. Using these role labels, we characterize role dynamics over teams’ lifecycles, finding that different roles peak at different moments and that students enact a more diverse set of roles as projects progress. To evaluate the utility of our role constructs, we use them to predict peer recognition, outperforming lexical, conversational, and LLM-prompting baselines. To assess generalizability beyond the educational context, we apply the same role constructs to a public dataset (DeliData) to predict team performance improvement after deliberation, again exceeding prior performance.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1931ACL Anthology · ACL 2026

Incomplete In-context Learning

Wenqiang Wang, Wen Yujia, Yan Xiao Sun Yat-Sen University, Zhifeng Chen, Yangshijie Zhang Lanzhou University, Peng Chen, Mingbo Yang, Xiaochun Cao

原文摘要

Existing In-context Learning (ICL) typically assumes the retrieval dataset contains demonstrations for all output label spaces. However, in real-world scenarios, delays in dataset updates or incomplete data annotation may result in the retrieval dataset containing labeled demonstrations for only a subset of the output space. We refer to this phenomenon as an incomplete retrieval dataset and define the in-context learning under this condition as Incomplete In-context Learning (IICL). To address IICL, we propose Iterative Judgments and Integrated Prediction (IJIP), a framework with train-free and train-based variants. For classification, the iterative judgments stage of IJIP reformulates an (m)-class problem into (m) binary tasks, converting IICL into standard ICL. The integrated prediction stage of IJIP then refines results using both the input and initial predictions. We further extend IJIP to text regression and generation, and introduce lightweight variants that reduce computation and token costs. Across six LLMs, seven tasks, and eight datasets, IJIP achieves state-of-the-art results under two incompleteness settings and even outperforms standard ICL with complete labels. IJIP also supports a semi-supervised variant and can serve as a plug-and-play enhancement for existing ICL and zero-shot methods.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1932ACL Anthology · ACL 2026

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective

Bishwamittra Ghosh MPI-SWS, Soumi Das MPI-SWS, Till Speicher Aleph Alpha Research, Qinyuan Wu, Mohammad Aflah Khan, Deepak Garg MPI-SWS, Krishna P. Gummadi MPI-SWS, Evimaria Terzi Boston University, Boston University

原文摘要

Large language models (LLMs) operate in two fundamental learning modes – fine-tuning (FT) and in-context learning (ICL) – raising key questions about which mode yields greater language proficiency and whether they differ in their inductive biases. Prior studies comparing FT and ICL have yielded mixed and inconclusive results due to inconsistent experimental setups. To enable a rigorous comparison, we propose a formal language learning task – offering precise language boundaries, controlled string sampling, and no data contamination – and introduce a discriminative test for language proficiency, where an LLM succeeds if it assigns higher generation probability to in-language strings than to out-of-language strings.Empirically, we find that: (a) FT has greater language proficiency than ICL on in-distribution generalization, but both perform equally well on out-of-distribution generalization. (b) Their inductive biases, measured by the correlation in string generation probabilities, are similar when both modes partially learn the language but diverge at higher proficiency levels. (c) Unlike FT, ICL performance differs substantially across models of varying sizes and families and is sensitive to the token vocabulary of the language. Thus, our work demonstrates the promise of formal languages as a controlled testbed for evaluating LLMs, behaviors that are difficult to isolate in natural language datasets. Our source code is available at https://github.com/bishwamittra/formallm.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1933ACL Anthology · ACL 2026

Where Paths Split: Localized, Calibrated Control of Moral Reasoning in Large Language Models

Chenchen Yuan Technical University of Munich, Zheyu Zhang Technical University of Munich, Gjergji Kasneci Technische Universität München and University of Tuebingen

原文摘要

Large language models often display heterogeneous moral preferences across settings. We study inference-time steering toward a desired ethical framework while preserving general competence. We present Convergent-Divergent Routing, which traces and edits minimal branch points inside transformer blocks where ethical-framework-related pathways first converge and then diverge. Gating non-target branches at these loci blocks the downstream propagation while leaving upstream computations intact. We find that this intervention alone increases targeted ethical-framework reasoning. To achieve fine-grained control, we adapt Common Spatial Patterns to the residual stream and extract, for each branch-point layer, a pair of directions that discriminate between utilitarian and deontological frameworks. We then introduce Dual Logit Calibration, a closed-form, minimum-\ell_2-norm update that moves the residual within this two-dimensional subspace so the resulting directional projections align with user-specified preference weights. Experiments on real-life moral dilemmas show that our method reliably achieves preference calibration and largely preserves general capabilities, outperforming recent baselines while providing an interpretable mechanism.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1934ACL Anthology · ACL 2026

Social Story Frames: Contextual Reasoning about Narrative Intent and Reception

Joel Mire, Maria Antoniak University of Colorado at Boulder, Steven R Wilson University of Michigan - Flint, Zexin Ma University of Connecticut, Achyutarama R Ganti, Andrew Piper McGill University, Maarten Sap Carnegie Mellon University

原文摘要

Reading stories evokes rich interpretive, affective, and evaluative responses, such as inferences about narrative intent or judgments about characters. Yet, computational models of reader response are limited, preventing nuanced analyses. To address this gap, we introduce SocialStoryFrames, a formalism for distilling plausible inferences about reader response, such as perceived author intent, explanatory and predictive reasoning, affective responses, and value judgments, using conversational context and a taxonomy grounded in narrative theory, linguistic pragmatics, and psychology. We develop two models, SSF-Generator and SSF-Classifier, validated through human surveys (N=382 participants) and expert annotations, respectively. We conduct pilot analyses to showcase the utility of the formalism for studying storytelling at scale. Specifically, applying our models to SSF-Corpus, a curated dataset of 6,140 social media stories from diverse contexts, we characterize the frequency and interdependence of storytelling intents, and we compare and contrast narrative practices (and their diversity) across communities. By linking fine-grained, context-sensitive modeling with a generic taxonomy of reader responses, SocialStoryFrames enable new research into storytelling in online communities.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1935ACL Anthology · ACL 2026

Lightweight and Faithful Visual Condition Checking in Behavior Trees via Expert-Regularized Reinforcement Learning

Hyosik Moon, Eldan Cohen University of Toronto

原文摘要

Behavior trees provide a transparent and modular structure for encoding expert-designed policies, enabling interpretable decision-making in complex tasks. Yet, applying behavior trees to high-dimensional perceptual inputs such as images or language is challenging as defining symbolic predicates over raw perceptual data is non-trivial. While state-of-the-art large multimodal models (such as vision-language models) can overcome this issue by utilizing natural language queries over perceptual inputs, they incur high computational cost, making them unsuitable for many applications. Imitation learning offers a way to distill these expert models into compact models, though it requires extensive supervision. In contrast, reinforcement learning reduces the need for costly supervision but risks misalignment of condition nodes with their intended semantics as well as poor credit assignment. To address these challenges, we introduce CERL (Condition-node Expert-regularized Reinforcement Learning), a framework that leverages expert-regularized reinforcement learning to preserve semantic faithfulness, while employing a factorized policy that aggregates sequential condition-node decisions into a single decision unit to alleviate credit assignment challenges. Experiments across seven tasks from the GymCards, FrozenLake, and BabyAIText suites demonstrate that our framework outperforms pure imitation learning or reinforcement learning baselines, retains strong agreement with expert decisions, and achieves substantial gains in inference speed and model size over expert models. Our implementation is available in https://github.com/HyosikMoon/CERL.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1936ACL Anthology · ACL 2026

When in Doubt, Consult: Expert Debate for Sexism Detection via Confidence-Based Routing

Anwar Alajmi, Gabriele Pergola University of Warwick

原文摘要

Online sexism increasingly appears in subtle, context-dependent forms that evade traditional detection methods. Its interpretation often depends on overlapping linguistic, psychological, legal, and cultural dimensions, which produce mixed and sometimes contradictory signals in annotated datasets. These inconsistencies, combined with label scarcity and class imbalance, result in unstable decision boundaries and cause fine-tuned models to overlook subtler, underrepresented forms of harm. To address these challenges, we propose a two-stage framework that unifies (i) targeted training procedures to better regularize supervision to scarce and noisy data with (ii) selective, reasoning-based inference to handle ambiguous or borderline cases. First, we stabilize the training combining class-balanced focal loss, class-aware batching, and post-hoc threshold calibration, strategies for the firs time adapted for this domain to mitigate label imbalance and noisy supervision. Second, we bridge the gap between efficiency and reasoning with a a dynamic routing mechanism that distinguishes between unambiguous instances and complex cases requiring a deliberative process. This reasoning process results in the novel Collaborative Expert Judgment (CEJ) module which prompts multiple personas and consolidates their reasoning through a judge model. Our approach outperforms existing approaches across several public benchmarks, with F1 gains of +4.48% and +1.30% on EDOS Tasks A and B, respectively, and a +2.79% improvement in ICM on EXIST 2025 Task 1.1.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1937ACL Anthology · ACL 2026

Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck

Meiru Zhang, Zaiqiao Meng Huazhong University of Science and Technology, Nigel Collier University of Cambridge

原文摘要

Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which causes them to overlook information at certain positions. Whether these failures stem from an inability to locate evidence (recognition failure) or integrate it (synthesis failure) is unclear. We introduce Multi-Focus Attention Instruction (MFAI), a semantic probe to disentangle these mechanisms by explicitly steering attention towards selected positions. Across 5 LLMs on two multi-hop QA tasks (MuSiQue and NeoQA), we identify the “Weakest Link Effect”: in our 18-document, 3-bucket setting, multi-hop reasoning performance collapses to the level of the least visible evidence, governed by absolute position rather than the linear distance between facts. While matched MFAI resolves recognition bottlenecks, improving accuracy by up to 11.49% in low-visibility positions, misleading MFAI yields divergent effects modulated by task topology: entity-centric tasks with vertical reasoning chains are vulnerable, whereas event-centric tasks with horizontal evidence structures are more resilient. Finally, we demonstrate that “thinking” models utilizing System-2 reasoning effectively locate and integrate the required information, matching gold-only baselines even in noisy, long-context settings. Supplementary experiments on 2WikiMultiHopQA, extended 3-4 hop counts, and a 32B model confirm these findings generalize across datasets, reasoning depths, and model scales.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1938ACL Anthology · ACL 2026

TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding

Mingyue Huo, Yiwen Shao Nuance Labs, Yuheng Zhang University of Illinois, Urbana Champaign

原文摘要

We present TagSpeech, a unified LLM-based framework that utilizes Temporal Anchor Grounding for joint multi-speaker ASR and diarization. The framework is built on two key designs: (1) decoupled semantic and speaker streams fine-tuned via Serialized Output Training (SOT) to learn turn-taking dynamics; and (2) an interleaved time anchor mechanism that not only supports fine-grained timestamp prediction but also acts as a synchronization signal between semantic understanding and speaker tracking. Compared to previous works that primarily focus on speaker-attributed ASR or implicit diarization, TagSpeech addresses the challenge of fine-grained speaker-content alignment and explicitly models who spoke what and when in an end-to-end manner. Experiments on AMI and AliMeeting benchmarks demonstrate that our method achieves consistent improvements in Diarization Error Rate (DER) over strong end-to-end baselines, including Qwen-Omni and Gemini, particularly in handling complex speech overlaps. Moreover, TagSpeech employs a parameter-efficient training paradigm in which the LLM backbone is frozen and only lightweight projectors are trained, resulting in strong performance with low computational cost

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1939ACL Anthology · ACL 2026

On the Effect of Hyperparameters in Language Modeling for Computational Linguistics

Ruoxi Ning, Yongpeng Zhu, Qingcheng Zeng, Tatsuki Kuribayashi Mohamed bin Zayed University of Artificial Intelligence, Freda Shi Vector Institute and University of Waterloo

原文摘要

Training language models and examining their linguistic behaviors have been a common protocol in computational linguistics for studying linguistic phenomena and modeling human language processing. However, work in this area is often limited to proof-of-concept demonstrations with arbitrary model configurations, without considering hyperparameter sensitivity, an important source of variation in model performance. In this work, we replicate three prior studies (Chang and Bergen, 2022; Hu et al., 2020b; Kuribayashi et al., 2024) with hyperparameters varied within a practical range, and show that modest hyperparameter changes can alter some qualitative conclusions about models’ linguistic abilities and even reverse the ranking of model performance. Our results highlight the risk that prior work may have reflected optimization artifacts rather than the genuine inductive biases of model classes, and that hyperparameter sensitivity should receive more attention as a factor that can meaningfully influence model behavior. We suggest future work to report the variation of performance across the configuration space to enhance the reliability and generalizability of conclusions. Code: https://github.com/compling-wat/tune-linguistic-lms.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.194ACL Anthology · ACL 2026

Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training

Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou Tencent, Zengrui Jin Tsinghua University, Guanrou Yang 等 11 位作者

原文摘要

Modeling fine-grained speaking styles remains challenging for language-speech representation pre-training, as existing speech-text models are typically trained with coarse captions or task-specific supervision, and scalable fine-grained style annotations are unavailable. We present FCaps, a large-scale dataset with fine-grained free-text style descriptions, encompassing 47k hours of speech and 19M fine-grained captions annotated via a novel end-to-end pipeline that directly grounds detailed captions in audio, thereby avoiding the error propagation caused by LLM-based rewriting in existing cascaded pipelines. Evaluations using LLM-as-a-judge demonstrate that our annotations surpass existing cascaded annotations in terms of correctness, coverage, and naturalness. Building on FCaps, we propose CLSP, a contrastive language-speech pre-trained model that integrates global and fine-grained supervision, enabling unified representations across multiple granularities. Extensive experiments demonstrate that CLSP learns fine-grained and multi-granular speech-text representations that perform reliably across global and fine-grained speech-text retrieval, zero-shot paralinguistic classification, and speech style similarity scoring, with strong alignment to human judgments. Code and dataset are publicly available at https://github.com/yfyeung/CLSP.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1940ACL Anthology · ACL 2026

Prosody as Supervision: Bridging the Non-Verbal–Verbal for Multilingual Speech Emotion Recognition

Girish, Mohd Mujtaba Akhtar, Muskaan Singh

原文摘要

In this work, we introduce a paralinguistic supervision paradigm for low-resource multilingual speech emotion recognition (LRM-SER) that leverages non-verbal vocalizations to exploit prosody-centric emotion cues. Unlike conventional SER systems that rely heavily on labeled verbal speech and suffer from poor cross-lingual transfer, our approach reformulates LRM-SER as non-verbal-to-verbal transfer, where supervision from a labelled non-verbal source domain is adapted to unlabeled verbal speech across multiple target languages. To this end, we propose NOVA-ARC, a geometry-aware framework that models affective structure in the Poincaré ball, discretizes paralinguistic patterns via a hyperbolic vector-quantized prosody codebook, and captures emotion intensity through a hyperbolic emotion lens. For unsupervised adaptation, NOVA-ARC performs optimal-transport-based prototype alignment between source emotion prototypes and target utterances, inducing soft supervision for unlabeled speech while being stabilized through consistency regularization. Experiments show that NOVA-ARC delivers the strongest performance under both non-verbal-to-verbal adaptation and the complementary verbal-to-verbal transfer setting, consistently outperforming Euclidean counter parts and strong SSL baselines. To the best of our knowledge, this work is the first to move beyond verbal-speech–centric supervision by introducing a non-verbal–to–verbal transfer paradigm for SER.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1941ACL Anthology · ACL 2026

Mechanisms of Prompt-Induced Hallucination in Vision–Language Models

William Rudman University of Texas at Austin, Michal Golovanevsky Harvard University, Dana Arad Computer Science Departmen, Technion-Israel Institute of Technology, Yonatan Belinkov Technion - Israel Institute of Technology, Technion, Carsten Eickhoff Eberhard-Karls-Universität Tübingen, Ritambhara Singh Brown University, Kyle Mahowald The University of Texas at Austin

原文摘要

Large vision–language models (VLMs) are highly capable, yet often hallucinate by favoring textual prompts over visual evidence. We study this failure mode in a controlled object-counting setting, where the prompt overstates the number of objects in the image (e.g., asking a model to describe four waterlilies when only three are present). At low object counts, models often correct the overestimation, but as the number of objects increases, they increasingly conform to the prompt regardless of the discrepancy. Through mechanistic analysis of three VLMs, we identify a small set of attention heads whose ablation substantially reduces prompt-induced hallucinations (PIH) by at least 40% without additional training. Across models, PIH-heads mediate prompt copying in model-specific ways. We characterize these differences and show that PIH ablation increases correction toward visual evidence. Our findings offer insights into the internal mechanisms driving prompt-induced hallucinations, revealing model-specific differences in how these behaviors are implemented.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1942ACL Anthology · ACL 2026

LLM4Cell: Taxonomy and Evaluation of LLM and Agentic Models for Single-Cell Biology

Sajib Acharjee Dip, Adrika Zafor, Bikash Kumar Paul, Uddip Acharjee Shuvo Incognito Solution Ltd, Muhit Islam Emon, Xuan Wang Virginia Polytechnic Institute and State University, Liqing Zhang Virginia Polytechnic Institute and State University

原文摘要

Large language models (LLMs) and emerging agentic frameworks are beginning to influence single-cell biology by enabling natural-language interfaces, generative annotation, and multimodal data integration. However, progress remains fragmented across data modalities, model families, and evaluation practices. LLM4Cell presents a unified survey of 58 foundation and agentic models developed for single-cell research, spanning RNA, ATAC, multi-omic, and spatial modalities. We organize these methods into five families foundation, text-bridge, spatial/multimodal, epigenomic, and agentic and map them to eight key analytical tasks, including annotation, trajectory inference, perturbation modeling, and drug-response prediction. Drawing on over 40 public datasets, we analyze benchmark coverage, data diversity, and ethical or scalability constraints, and synthesize reported capabilities across ten domain-level dimensions related to biological grounding, multimodal alignment, fairness, privacy, and interpretability. By explicitly linking datasets, modeling paradigms, and evaluation domains, LLM4Cell provides an integrated perspective on language-driven single-cell analysis and highlights open challenges in standardization, interpretability, and trustworthy model development.

cs.CL · cs.AI · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1943ACL Anthology · ACL 2026

Wait! There’s a Way Out: A Decision Mechanism for Forecasting Conversational Derailment

Laerdon Kim, Vivian Nguyen, Cristian Danescu-Niculescu-Mizil Cornell University

原文摘要

Forecasting conversational derailment is the task of predicting, as the conversation unfolds, whether it will eventually derail into personal attacks. Since forecasting models operate in an online fashion, they must decide whether to “trigger” an alert after each utterance—for example, to notify participants or a moderator that the conversation is at risk of derailing. Existing approaches make this decision solely based on the estimated likelihood of derailment given the preceding utterances, implicitly assuming that the conversation’s future trajectory is fixed. As a result, they ignore the possibility of future recovery and incur an unnecessarily high rate of false positives.In this work we propose a method for decoupling the decision to trigger from derailment likelihood estimation. Our approach is inspired by the first human baseline on this task, which shows that humans achieve dramatically lower false positive rates by selectively deferring their decision to trigger when they anticipate that tension is likely to subside. We operationalize this insight with a deferral mechanism that uses forward-looking simulations to assess whether a tense moment admits plausible paths to recovery. Incorporating this mechanism into a state-of-the-art forecasting model substantially reduces false positives without sacrificing forecasting accuracy. More broadly, this work highlights the value of treating decision-making as a first-class component of forecasting systems.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1944ACL Anthology · ACL 2026

From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context

Peyman Baghershahi, Gregoire Fournier Amazon, Pranav Nyati, Sourav Medya University of Illinois at Chicago

原文摘要

Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs (TAGs), which are common in domains such as citation networks, social platforms, and knowledge graphs. GNNs are not inherently interpretable and thus, many explanation methods have been proposed. However, existing explanation methods often struggle to generate interpretable, fine-grained rationales, especially when node attributes include rich natural language. In this work, we introduce GSPELL, a lightweight, post-hoc framework that uses large language models (LLMs) to generate faithful and interpretable explanations for GNN predictions. GSPELL projects GNN node embeddings into the LLM embedding space and constructs hybrid prompts that interleave soft prompts with textual inputs from the graph structure. This enables the LLM to reason about GNN internal representations and to produce natural-language explanations, along with concise explanation subgraphs. Our experiments across real-world TAG datasets demonstrate that GSPELL achieves a favorable trade-off between fidelity and sparsity, while improving human-centric metrics such as insightfulness. GSPELL sets a new direction for LLM-based explainability in graph learning by aligning GNN internals with human reasoning.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1945ACL Anthology · ACL 2026

RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation

Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang 等 15 位作者

原文摘要

Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed. To address this gap, we introduce RealChart2Code, a new large-scale benchmark with over 2,800 instances grounded in authentic datasets and featuring tasks with clear analytical intent. Crucially, it is the first benchmark to systematically evaluate chart generation from large-scale raw data and assess iterative code refinement in a multi-turn conversational setting. Our comprehensive evaluation of 14 leading VLMs on RealChart2Code reveals significant performance degradation compared to simpler benchmarks, highlighting their struggles with complex plot structures and authentic data. Our analysis uncovers a substantial performance gap between proprietary and open-weight models and confirms that even state-of-the-art VLMs often fail to accurately replicate intricate, multi-panel charts. These findings provide valuable insights into the current limitations of VLMs and guide future research directions.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1946ACL Anthology · ACL 2026

Success and Cost Elicit Convention Formation for Efficient Communication

Saujas Vaduguru Carnegie Mellon University, Yilun Hua Department of Computer Science, Cornell University, Yoav Artzi Google DeepMind, arXiv and Cornell University, Daniel Fried Meta AI and Carnegie Mellon University

原文摘要

Humans leverage shared conversational context to become increasingly successful and efficient at communicating over time. One manifestation of this is the formation of ad hoc linguistic conventions, which allow people to coordinate on short, less costly utterances that are understood using shared conversational context. We present a method to train large multimodal models to form conventions, enabling efficient communication. Our approach uses simulated reference games between models, and requires no additional human-produced data. In repeated reference games involving photographs and tangram images, our method enables models to communicate efficiently with people: reducing the message length by up to 41% while increasing success by 15% over the course of the interaction. Human listeners respond faster when interacting with our model that forms conventions. We also show that training based on success or cost alone is insufficient – both are necessary to elicit convention formation.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1947ACL Anthology · ACL 2026

Measuring User’s Mental Models of Speech Translation in Human-AI Collaboration

HyoJung Han Department of Computer Science, University of Maryland, College Park, Nishant Balepur, Jordan Lee Boyd-Graber University of Maryland, College Park, Marine Carpuat University of Maryland, College Park

原文摘要

Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper studies users’ mental models of speech translation systems through a new framework based on cross-lingual question answering, where users either accept MT output or request professional re-translation to answer questions based on the information presented in a foreign language. By analyzing user behavior and accuracy trends across varying translation qualities, we examine to what extent they can predict where the system is likely to be wrong, and how this mental model evolves. Users develop stronger mental models with practice, especially when they have some knowledge of the source language, primarily by relying on surface-level error cues. Moreover, providing speech transcriptions can help users develop better mental models. Our results show the promise of cross-lingual question answering as a downstream task for studying MT mental models and advancing our understanding of human–AI collaboration.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表