4 papers
Attention Basin: Why Contextual Position Matters in Large Language Models
Zihao Yi, Delong Zeng, Zhenqing Ling +6
The performance of Large Language Models (LLMs) is significantly sensitive to the contextual position of information in the input. To investigate the mechanism behind this position…
MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?
Zhe Xu, Daoyuan Chen, Zhenqing Ling +2
Large foundation models face challenges in acquiring transferable, structured thinking abilities, especially when supervised with rigid templates or crowd-annotated instruction dat…
Intent-driven In-context Learning for Few-shot Dialogue State Tracking
Zihao Yi, Zhe Xu, Ying Shen
Dialogue state tracking (DST) plays an essential role in task-oriented dialogue systems. However, user's input may contain implicit information, posing significant challenges for D…
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
Jiayi Kuang, Jingyou Xie, Haohao Luo +6
Visual Question Answering (VQA) is a challenge task that combines natural language processing and computer vision techniques and gradually becomes a benchmark test task in multimod…