2 code implementations • 25 Apr 2024 • Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou
While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge.
no code implementations • 29 Feb 2024 • Zexiong Ma, Shengnan An, Bing Xie, Zeqi Lin
However, the performance remains unsatisfactory in generating library-oriented code, especially for the libraries not present in the training data of LLMs.
1 code implementation • 31 Oct 2023 • Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, Weizhu Chen
To further improve their reasoning capabilities, this work explores whether LLMs can LEarn from MistAkes (LEMA), akin to the human learning process.