26 citations · 29 across the 8 of their papers we have counts for
4 papers · 1 filter
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
Yuxin Wang, Minghua Ma, Zekun Wang +7
The colossal parameters and computational overhead of Large Language Models (LLMs) challenge their real-world applications. Network pruning, which targets unstructured or structure…
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…
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation
Jingchang Chen, Hongxuan Tang, Zheng Chu +4
Despite recent progress made by large language models in code generation, they still struggle with programs that meet complex requirements. Recent work utilizes plan-and-solve deco…