3 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
EvoWiki: Evaluating LLMs on Evolving Knowledge
Wei Tang, Yixin Cao, Yang Deng +8
Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks…
Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning
Kai Xiong, Xiao Ding, Li Du +4
Large Language Models (LLMs) are versatile and demonstrate impressive generalization ability by mining and learning information from extensive unlabeled text. However, they still e…
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement
Jiahao Ying, Mingbao Lin, Yixin Cao +5
This paper introduces the innovative "LLMs-as-Instructors" framework, which leverages the advanced Large Language Models (LLMs) to autonomously enhance the training of smaller targ…
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential
Wei Tang, Yixin Cao, Jiahao Ying +4
Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs). Targeting its bottleneck of retriever performance,…
QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism
Bo Wang, Heyan Huang, Yixin Cao +3
While large language models (LLMs) have made notable advancements in natural language processing, they continue to struggle with processing extensive text. Memory mechanism offers…
Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models
Jiahao Ying, Yixin Cao, Yushi Bai +7
Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for large…