Yimeng Zhang, Yingying Zhuang, Ziyi Wang, Yuxuan Lu, Pei Chen, Aman Gupta, Zhe Su, Ming Tan 等 15 位作者
Uncertainty estimation is essential for the trustworthy deployment of LLMs: it underpins self-refinement and serves as a decision signal for downstream systems, from verifiers to collaborating agents. However, existing approaches operate at suboptimal granularities: token-level scores lack semantic coherence, while sequence-level scores fail to localize errors. We formalize Span-Level Uncertainty Estimation (SLUE), a new task that targets the natural granularity for uncertainty: semantically coherent text spans, each conveying a single assessable unit of meaning. To address this task, we introduce SPANUQ, a lightweight probe that distills the uncertainty knowledge from expensive multi-sample inference into a single forward pass over LLM hidden states. SPANUQ employs a DETR-style span decoder to simultaneously detect spans and estimate their uncertainty via a Mixture of Beta distribution, trained with a principled combination of Beta NLL regression and contrastive ranking objectives. We construct SPANUQ-BENCH, the first span-level uncertainty benchmark comprising 20K prompts, 293K annotated spans, and continuous soft labels derived from multi-sample claim verification. Experiments on five LLM backbones show that SPANUQ consistently achieves the best span-level uncertainty quality , outperforming the strongest probe baseline and all sampling-based methods while being 10~20x faster. Its DETR-based span detector attains 0.910 F1, surpassing the best heuristic by 39.4%, enabling precise error localization that sequence-level methods cannot provide. The same architecture ports to five LLMs spanning two model families, with one probe trained per backbone, and we additionally observe that sequence-level uncertainty is partially decomposable, suggesting that span-level estimation subsumes sequence-level as a special case. The project page is available damon-demon.github.io/SpanUQ.
Zhaoxuan Tan University of Notre Dame, Zixuan Zhang Amazon, Haoyang Wen Amazon, Zheng Li, Rongzhi Zhang Georgia Institute of Technology, Zhejiang University and Amazon, Pei Chen Amazon, Fengran Mo, Zheyuan Liu University of Notre Dame 等 11 位作者
Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) methods, such as the “One-PEFT-Per-User” (OPPU) paradigm, require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. We introduce Profile-to-PEFT, a scalable framework that employs a hypernetwork, trained end-to-end, to map a user’s encoded profile directly to a full set of adapter parameters (e.g., LoRA), eliminating per-user training at deployment. This design enables instant adaptation, generalization to unseen users, and privacy-preserving local deployment. Experimental results demonstrate that our method outperforms both prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. The framework exhibits strong generalization to out-of-distribution users and maintains robustness across varying user activity levels and different embedding backbones. The proposed Profile-to-PEFT framework enables efficient, scalable, and adaptive LLM personalization suitable for large-scale applications.
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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
Ming Li, Pei Chen Amazon, Zhenhao Zhang Amazon, Tao Yang Amazon, Xinyang Zhang Anthropic, Han Li Shopify, Tianyu Cao, Ming Zeng Amazon 等 13 位作者
Large Language Models demonstrate strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC), a degradation in performance as information is revealed progressively in multi-turn settings. Motivated by the current progress on Reinforcement Learning with Verifiable Rewards (RLVR), we propose Curriculum Reinforcement Learning with Verifiable Accuracy and Abstention Rewards (RLAAR), a framework that encourages models not only to generate correct answers, but also to judge the solvability of questions in the multi-turn conversation setting. Our approach employs a competence-gated curriculum that incrementally increases dialogue difficulty (in terms of instruction shards), stabilizing training while promoting reliability. Using multi-turn, on-policy rollouts and a mixed-reward system, RLAAR teaches models to balance problem-solving with informed abstention, reducing premature answering behaviors that cause LiC. Evaluated on LiC benchmarks, RLAAR significantly mitigates LiC performance decay (62.6% to 75.1%) and improves calibrated abstention rates (33.5% to 73.4%). Together, these results provide a practical recipe for building multi-turn reliable and trustworthy LLMs.
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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
Tool-calling agents are increasingly deployed in real-world customer-facing workflows. Yet most studies on tool-calling agents focus on idealized settings with general, fixed, and well-specified tasks.In real-world applications, user requests are often (1) ambiguous, (2) changing over time, or (3) infeasible due to policy constraints, and training and evaluation data that cover these diverse, complex interaction patterns remain under-represented.To bridge the gap, we present Trajectory2Task a verifiable data generation pipeline for studying tool use at scale under three realistic user scenarios: ambiguous intent, changing intent, and infeasible intents.The pipeline first conducts multi-turn exploration to produce valid tool-call trajectories. It then converts these trajectories into user-facing tasks with controlled intent adaptations. This process yields verifiable task that support closed-loop evaluation and training. We benchmark several state-of-the-art LLMs on the generated complex user scenario tasks and observe frequent failures.Finally, using successful trajectories obtained from task rollouts, we fine-tune lightweight LLMs and find consistent improvements across all three conditions, along with better generalization to unseen tool-use domains, indicating stronger tool-calling ability.
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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
Yichen Cai, Jiayang Li, Junyuan Qiu Zhejiang University, Jingya Guo Alibaba Group and University of Hong Kong, Weitao You, Changyuan Yang, Lingyun Sun Zhejiang University, Pei Chen
Current conversational agents often follow static learning paradigms and miss the implicit, evolving feedback embedded in users’ follow-up behaviors. We propose IEvoAgent, an evolving conversational agent framework that leverages the structured dependency between agent responses and user reactions. We construct an annotated dataset from LMSYS-Chat-1M and WildChat and find consistent response-conditioned feedback patterns. Based on this finding, IEvoAgent uses a conditional feedback distribution matrix to estimate expected feedback rewards, combining offline KTO alignment with an inference-time prompt-evolution mechanism driven by a dynamic matrix. Experiments on MT-Bench-101, WildBench, and FB-Bench show improvements over open-source baselines, indicating that mining implicit feedback supports better multi-turn alignment under evolving user preferences. Our code and dataset are available at https://github.com/Hualeez/IEvoAgent.
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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
Yichen Cai, Pei Chen, Jiayang Li, Jingya Guo Alibaba Group and University of Hong Kong, Zejian Li, Changyuan Yang, Lingyun Sun Zhejiang University
Large Language Models are increasingly utilized as Role-Playing Agents (RPAs) to simulate personas in interactive settings. However, current RPAs often produce flattened and stereotypical personas with limited depth and fidelity. This limitation arises from two core challenges: insufficient modeling of complex personal histories and internal logic, and ungrounded reasoning that fails to preserve persona coherence as dialogue context evolves. To address these challenges, we propose ThinkPersona, a role-playing agent trained to explicitly ground responses in individual identity. We introduce Persona Graphs as structured representations that encode life trajectories, values, relationships, and events as interconnected knowledge. We construct 1,201 Persona Graphs from real-world interviews and derive a Question–Reasoning–Answer (QRA) dataset of 23,401 samples that supervises reasoning over persona evidence. Fine-tuning on QRA enables ThinkPersona to internalize persona logic and generate persona-consistent responses in long-context dialogues. Experiments on three benchmarks show that ThinkPersona improves role-playing fidelity, behavioral consistency, and grounded reasoning over existing methods, while preserving general instruction-following capabilities. Our code and dataset are available at https://github.com/Hualeez/ThinkPersona.
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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
Pei Chen, Xilai Wang, Shiqixu, Zejian Li, Lingyun Sun Zhejiang University
Multimodal Continual Instruction Tuning (MCIT) is essential for adapting Multimodal Large Language Models (MLLMs) to dynamic data streams, yet preventing catastrophic forgetting remains a major challenge. Existing parameter-efficient approaches often face a dilemma: fixed architectures suffer from knowledge interference, while dynamic strategies incur inefficient capacity expansion, limiting scalability. We propose MoBLoRA (Mixture-of-Bases LoRA), a novel framework for MCIT. Motivated by our geometric analysis revealing subspace redundancy across sequential tasks, MoBLoRA shifts the paradigm from expert selection to subspace mixing: it decomposes adaptation weights into a globally shared pool of orthonormal bases to capture task-invariant knowledge, and lightweight mixing matrices to encode task-specific variations. This design effectively decouples knowledge accumulation from task reconstruction. Experiments on standard benchmarks show MoBLoRA significantly outperforms state-of-the-art methods while maintaining superior parameter efficiency.
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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
Yuanfei Wang Peking University, Xinju Huang, Fangwei Zhong, Yaodong Yang Peking University, Yizhou Wang Peking University, Yuanpei Chen Peking University, Hao Dong Peking University
Effective real-world human–agent interactions, such as household robotic services, are often long-term and repeated. Beyond executing tasks, agents are expected to quickly become familiar with individual users. In everyday use, people do not want to repeatedly specify precise instructions. Instead, they prefer agents that adapt to their habits and preferences over interaction while minimizing communication effort. This poses a key challenge: enabling agents to rapidly align with user needs and provide proactive assistance within limited communication. To study this problem in a realistic embodied setting, we first introduce HA-Desire, a home assistance simulation environment. HA-Desire features an LLM-driven proxy user with value-driven preferences and natural language behavior, enabling systematic evaluation of how agents adapt to users across interactions and satisfy their desires. We further propose FAMER, a framework that integrates goal-relevant memory, desire-centered mental reasoning, and efficient communication to infer user preferences from interaction while reducing unnecessary dialogue. Experiments across embodied household tasks and different LLMs show that FAMER improves both task success and interaction efficiency compared to existing baselines, highlighting the importance of communication-efficient desire alignment for proactive embodied agents that support users without requiring frequent instructions.
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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