8 citations · 27 across the 32 of their papers we have counts for
7 papers · 1 filter
Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
Zhenran Xu, Senbao Shi, Baotian Hu +4
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have ex…
A Survey of Large Language Models Attribution
Dongfang Li, Zetian Sun, Xinshuo Hu +5
Open-domain generative systems have gained significant attention in the field of conversational AI (e.g., generative search engines). This paper presents a comprehensive review of…
Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs
Yunxin Li, Zhenyu Liu, Baotian Hu +4
Recent advancements in multimodal large language models (MLLMs) have achieved significant multimodal generation capabilities, akin to GPT-4. These models predominantly map visual i…
Temporal Knowledge Question Answering via Abstract Reasoning Induction
Ziyang Chen, Dongfang Li, Xiang Zhao +2
In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). LLMs often struggle with this task, leading to the generation of…
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
Yunxin Li, Longyue Wang, Baotian Hu +5
The emergence of multimodal large models (MLMs) has significantly advanced the field of visual understanding, offering remarkable capabilities in the realm of visual question answe…
A Read-and-Select Framework for Zero-shot Entity Linking
Zhenran Xu, Yulin Chen, Baotian Hu +1
Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability. Previous methods largely focus on the candidate retrieval…
Revisiting Sparse Retrieval for Few-shot Entity Linking
Yulin Chen, Zhenran Xu, Baotian Hu +1
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base. One of the key challenges comes from insufficient labeled data for specific doma…