2 citations · 2 across the 8 of their papers we have counts for
7 papers · 1 filter
Can Large Language Models Simulate Human Cognition Beyond Behavioral Imitation?
Yuxuan Gu, Lunjun Liu, Xiaocheng Feng +4
An essential problem in artificial intelligence is whether LLMs can simulate human cognition or merely imitate surface-level behaviors, while existing datasets suffer from either s…
Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering
Kun Zhu, Lizi Liao, Yuxuan Gu +3
The rapid growth of scientific literature demands efficient methods to organize and synthesize research findings. Existing taxonomy construction methods, leveraging unsupervised cl…
Length Controlled Generation for Black-box LLMs
Yuxuan Gu, Wenjie Wang, Xiaocheng Feng +5
Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is…
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Kun Zhu, Xiaocheng Feng, Xiyuan Du +7
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when con…
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
Zheng Chu, Jingchang Chen, Qianglong Chen +6
Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval…
Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System
Haotian Wang, Xiyuan Du, Weijiang Yu +5
Multi-agent debate system (MAD) imitating the process of human discussion in pursuit of truth, aims to align the correct cognition of different agents for the optimal solution. It…
Hierarchical Catalogue Generation for Literature Review: A Benchmark
Kun Zhu, Xiaocheng Feng, Xiachong Feng +2
Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while…