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cs.CL2026

Instruction Anchor: Dissecting the Mechanistic Dynamics of Modality Arbitration

Yu Zhang, Mufan Xu, Xuefeng Bai +4

Modality following is the ability to selectively leverage multimodal contexts based on user instructions. It is fundamental to the safety and reliability of multimodal large langua…

cs.CL2026

Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition

Jinlong Ma, Yu Zhang, Xuefeng Bai +5

Grounded Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions. In this…

cs.CL2026

Evaluating and Steering Modality Preferences in Multimodal Large Language Model

Yu Zhang, Jinlong Ma, Yongshuai Hou +5

Multi-modal large language models (MLLMs) have achieved remarkable success on complex multi-modal tasks. However, it remains insufficiently explored whether they exhibit $\textbf{m…

cs.CL2024

BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition

Quanjiang Guo, Yihong Dong, Ling Tian +3

Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection s…

cs.CL2024

Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering

Yu Zhang, Kehai Chen, Xuefeng Bai +3

Knowledge graph question answering (KGQA) involves answering natural language questions by leveraging structured information stored in a knowledge graph. Typically, KGQA initially…

cs.CL2024

TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction

Yu Zhang, Zhao Kang

Few-shot document-level relation extraction suffers from poor performance due to the challenging cross-domain transferability of NOTA (none-of-the-above) relation representation. I…