AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback

arXiv:2309.17176v3 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) have demonstrated significant success across various domains. However, their application in complex decision-making tasks frequently necessitates intricate prompt engineering or fine-tuning, leading to challenges in unseen downstream tasks and heavy demands on computational resources. Meanwhile, Reinforcement Learning (RL) has been recognized as effective in decision-making problems but struggles in environments with sparse rewards, such as open-world games. To overcome these challenges, we introduce AdaRefiner, a novel framework designed to enhance the …

abstract application arxiv challenges computational cs.ai cs.cl decision decisions domains engineering feedback fine-tuning however language language models large language large language models llms making prompt reinforcement reinforcement learning resources success tasks type