KnowGPT: Knowledge Injection for Large Language Models
CoRR(2023)
摘要
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 GPT-4. Additionally, KnowGPT attains a 91.6
official leaderboard, which is comparable to human-level performance.
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