Yuchong Chen, Bowei Zou A*STAR, Yuhan Chen Soochow University, Yifan Fan, Xinyu Li, Shujun Cao, Yu Hong Suzhou University
Visual questions are often ambiguous: the same image–question pair may admit multiple valid answers depending on which region is referenced. However, current Visual Question Answering (VQA) systems typically collapse this ambiguity, committing to a single interpretation during decoding and evaluation. In this work, we study visual question ambiguity from a grounded, region-centric perspective. We operationalize ambiguity as the existence of multiple distinct answer-supporting regions in an image, each independently yielding a valid answer. This formulation makes ambiguity observable without requiring exhaustive multi-answer annotations. Based on this definition, we conduct a systematic empirical study of state-of-the-art Visual Large Language Models (VLLMs). We find that, under default decoding, VLLMs consistently under-report ambiguity—even when multiple valid visual groundings are present. Importantly, probing model hidden states reveals that ambiguity-related signals are already encoded in their internal representations, despite not being reliably expressed in outputs. Finally, we show that selectively activating multi-focus answering based on these signals can recover additional valid answers while avoiding excessive hallucination. Together, our results suggest that ambiguity in VQA is not merely an annotation artifact or capability limitation, but a property that VLLMs internally recognize yet often fail to surface under standard decoding assumptions.
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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
Guanglu Sun Harbin University of Science and Technology, Xinyu Liu, Lili Liang Harbin University of Science and Technology, Yang Yu Harbin University of Science and Technology, Fei Lang Harbin University of Science and Technology, Suxia Zhu, Ming Liu Harbin Institute of Technology
In real-world scenarios, multimodal information continuously evolves, with new entity and relation types emerging, necessitating timely updates to multimodal knowledge graphs for supporting downstream tasks. However, existing methods struggle to balance real-time adaptability and computational efficiency in continual learning scenarios. To this end, this paper proposes the Continual Multimodal Entity and Relation Joint Extraction (CMERJE) task and a Multimodal Prompt-based Boundary-enhanced Continual (MPBoCo) framework. Specifically, MPBoCo incrementally stores task-specific knowledge via learnable multimodal prompts, dynamically matches relevant prompts for each instance, and fuses them into a frozen backbone model for task-specific reasoning. Subsequently, the boundary-enhanced dual-branch module leverages the auxiliary branch to preserve local syntactic continuity and provide boundary guidance. Experimental results demonstrate that MPBoCo achieves superior performance in real-world scenarios, significantly outperforming baseline methods by 5.5% and 7.2% in 10-task and 5-task settings, respectively.
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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
Junhao Ruan, Abudukeyumu Abudula, Bei Li Meituan, Yongjing Yin Meituan, Xinyu Liu, Kechen Jiao, Xin Chen Meituan, Jingang Wang Meituan 等 11 位作者
Accurate evaluation of conversational retrieval is pivotal for advancing Retrieval-Augmented Generation (RAG) systems. However, existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics. To address these challenges, we introduce MTR-Suite, a unified framework for auditing, synthesizing, and benchmarking retrieval. It features: (1) MTR-Eval, an LLM-based auditor quantifying alignment gaps in previous benchmarks; (2) MTR-Pipeline, a multi-agent system using greedy traversal clustering to generate high-fidelity dialogues at 1/400th human cost; and (3) MTR-Bench, a rigorous general-domain benchmark. MTR-Bench mimics production-style challenges (hard topic switching, verbosity), offering superior discriminative power. We make our code and data publicly available to facilitate future research.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Xinyu Li, Kexi Chen, Jiajie Shen Fudan University, Ying Zheng, Hong Lu Fudan University, Jin Zhao Fudan University, Xin Wang Fudan University
In multivariate long-term time series forecasting, it is widely believed that the effectiveness of self-attention arises from its attention matrix. We challenge this assumption with a counterintuitive finding: our experiments, conducted on three classic and three latest Transformer models, show that dot-product attention can be replaced by element-wise operations without token interaction, such as the addition and Hadamard product, while maintaining or even improving accuracy. This leads to our central hypothesis: the effectiveness of self-attention in this task stems not from the dynamic attention matrix, but from the multi-branch feature extraction inherent in the parallel projections to Query, Key, and Value matrices and their fusion. To validate this, we construct a simple multi-branch MLP that isolates the ‘multi-branch mapping with element-wise operation’ structure from the Transformer and show that it achieves competitive performance. Our results indicate that the source of performance in self-attention has been misattributed, suggesting that the true benefit lies in the architectural principle of multi-branch mapping and fusion, not in the attention matrix.
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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