Enabling Language Models to Implicitly Learn Self-Improvement

arXiv:2310.00898v3 Announce Type: replace
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that there is always room for improvement in the quality of model responses. To address this challenge, various approaches have been proposed to enhance the performance of LLMs. There has been a growing focus on enabling LLMs to self-improve their response quality, thereby reducing the reliance on extensive human annotation efforts for collecting …

abstract arxiv capabilities challenge cs.cl enabling however improvement language language models large language large language models learn llms nature performance quality responses room self-improvement tasks text text generation type