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Large language models (LLMs), such as ChatGPT, are able to generatehuman-like, fluent responses for many downstream tasks, e.g., task-orienteddialog and question answering. However, applying LLMs to real-world,mission-critical applications remains challenging mainly due to their tendencyto generate hallucinations and their inability to use external knowledge. Thispaper proposes a LLM-Augmenter system, which augments a black-box LLM with aset of plug-and-play modules. Our system makes the LLM generate responsesgrounded in external knowledge, e.g., stored in task-specific databases. Italso iteratively revises LLM prompts to improve model responses using feedbackgenerated by utility functions, e.g., the factuality score of a LLM-generatedresponse. The effectiveness of LLM-Augmenter is empirically validated on twotypes of scenarios, task-oriented dialog and open-domain question answering.LLM-Augmenter significantly reduces ChatGPT's hallucinations withoutsacrificing the fluency and informativeness of its responses. We make thesource code and models publicly available.

Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback