Kunpeng Kang, Shuaimin Li, Kaiyuan Zhang, Luyang Zhang, Jiasheng Si Qilu University of Technology (Shandong Academy of Sciences), Bing Xu, Kehai Chen Harbin Institute of Technology (Shenzhen), Muyun Yang 等 9 位作者
Word sense disambiguation (WSD) is a foundational task in natural language processing. Recent research has reformulated WSD for large language models (LLMs) as a generative task, where the model produces a definition to convey the intended meaning of an ambiguous word in context.In practice, most existing approaches implement this formulation through straightforward supervised fine-tuning, which tends to prioritize superficial context-to-gloss memorization over true contextual sense discrimination, leading to degraded performance on less frequent senses (LFS), particularly in unseen settings.To address this issue, we propose WSDPO, a training framework for generative WSD with chain-of-thought (CoT) and preference optimization. WSDPO consists of three stages: (1) disambiguation-aware CoT construction, which produces training data containing explicit disambiguation steps for the later stage;(2) disambiguation-guided supervised fine-tuning, which explicitly trains the model to discriminate word sense before generating the final definition; and(3) preference-based optimization, which further strengthens the model’s ability to generate sense-faithful definitions by optimizing it using preference pairs constructed from multiple sampled CoT outputs.Extensive experiments across benchmark datasets and multiple backbone LLMs demonstrate that WSDPO achieves substantial performance gains on rare and unseen settings, and exhibits strong generalization in standard evaluation settings.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
The ability to model sparse and underspecified rewards, characteristic of human preferences, is fundamental to scaling Reinforcement Learning (RL). Current preference-based reward modeling largely relies on verifiable rewards, where human-annotated labels define rule-based signals. However, these methods face a fundamental bottleneck we term the Matryoshka Doll Problem: a recursive dependency where each reward verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. In this work, we propose Dual RM, which couples discriminative and generative reward models (DisRMs and GenRMs) under a non-parametric meta-reward. Rather than verifying the correctness of GenRM’s reasoning, the meta-reward evaluates its practical impact on response quality. Specifically, GenRM identifies multi-dimensional evaluation rubrics and iteratively refines the response, while DisRM quantifies the quality shifts induced by each rubric. Furthermore, we implement rubric-based test-time scaling to improve sample efficiency and preference alignment under both DPO and GRPO. Our experiments demonstrate that Dual RM achieves strong performance across major preference benchmarks. Notably, even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Weiyang Huang Harbin Institute of Technology ,(Shenzhen), Xuefeng Bai, Kehai Chen Harbin Institute of Technology (Shenzhen), Xinyang Chen Harbin Institute of Technology, Shenzhen, Yibin Chen, Weili Guan, Min Zhang Harbin Institute of Technology, Shenzhen and Harbin Institute of Technology
Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive “overthinking”, generating unnecessarily long reasoning chains. While current solutions improve token efficiency, they often sacrifice fine-grained control or risk disrupting the logical integrity of the reasoning process. To address this, we introduce Stepwise Adaptive Thinking (SAT), a framework that performs step-level, difficulty-aware pruning while preserving the core reasoning structure. SAT formulates reasoning as a Finite-State Machine (FSM) with distinct thinking modes (SLOW, NORMAL, FAST, SKIP). It navigates these states dynamically using a lightweight Process Reward Model (PRM), compressing easy steps while preserving depth for hard ones. Experiments across 9 LRMs and 7 benchmarks show that SAT achieves up to 40% reduction in reasoning tokens while generally maintaining or improving accuracy. Code is available at https://github.com/byxw13/SAT_Code.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Gewen Liang, Mufan Xu, Kehai Chen Harbin Institute of Technology (Shenzhen), Wei Wang, Yuwei Wang Institute of Computing Technology Chinese Academy of Sciences, Muyun Yang, Tiejun Zhao Harbin Institute of Technology, Min Zhang Harbin Institute of Technology, Shenzhen and Harbin Institute of Technology
Large language models (LLMs) have demonstrated increasingly strong reasoning capabilities, achieving remarkable progress in knowledge graph question answering (KGQA). However, a key challenge in such systems is non-deterministic reasoning, where the model indecisively activates multiple semantically related knowledge graph edges for a given query, frequently leading to incorrect answers. To address this issue, we propose Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (DR2). DR2 identifies and localizes non-deterministic reasoning behaviors, uncovering the underlying semantic representation deficiencies in LLMs. Building on this diagnosis, we design abductive reasoning-based preference learning, which promotes fine-grained semantic discrimination and mitigates non-deterministic reasoning errors. Experimental results demonstrate that the proposed DR2 significantly outperforms several strong baselines, achieving state-of-the-art performance on the widely used WebQSP and CWQ benchmarks.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表