Generative Large Language Models (LLMs), such as ChatGPT, offer interactive APIs that can answer common questions at a human-expert level. However, these models often give inaccurate or incorrect responses when faced with questions requiring domain-specific or professional-specific knowledge not covered in their training corpus. Furthermore, many state-of-the-art LLMs are not open-source, making it challenging to inject knowledge with model APIs only. In this work, we introduce KnowGPT, a black-box knowledge injection framework for LLMs in question answering. KnowGPT leverages deep reinforcement learning (RL) to extract relevant knowledge from Knowledge Graphs (KGs) and use Multi-Armed Bandit (MAB) to construct the most suitable prompt for each question. Our extensive experiments on three benchmark datasets showcase that KnowGPT significantly enhances the existing methods. Notably, KnowGPT achieves an average improvement of 23.7% over ChatGPT and an average improvement of 2.9% over GPT-4. Additionally, KnowGPT attains a 91.6% accuracy on the OpenbookQA official leaderboard, which is comparable to human-level performance.

翻译：生成式大规模语言模型（如ChatGPT）通过交互式API能够以人类专家水平回答常见问题。然而，当面对需要训练语料库中未涵盖的领域特定或专业特定知识的问题时，这些模型常会给出不准确或错误的回答。此外，许多最先进的大型语言模型并非开源，使得仅通过模型API进行知识注入面临挑战。本研究提出KnowGPT——一种面向问答任务的黑盒知识注入框架。该框架利用深度强化学习从知识图谱中提取相关知识，并采用多臂老虎机算法为每个问题构建最适配的提示词。我们在三个基准数据集上的大量实验证明，KnowGPT显著提升了现有方法的性能。值得注意的是，相比ChatGPT平均提升23.7%，相比GPT-4平均提升2.9%。此外，KnowGPT在OpenbookQA官方排行榜上达到91.6%的准确率，与人类水平相当。