In this paper, we present GeneGPT, a novel method for teaching large language
models (LLMs) to use the Web Application Programming Interfaces (APIs) of the
National Center for Biotechnology Information (NCBI) and answer genomics
questions. Specifically, we prompt Codex (code-davinci-002) to solve the
GeneTuring tests with few-shot URL requests of NCBI API calls as demonstrations
for in-context learning. During inference, we stop the decoding once a call
request is detected and make the API call with the generated URL. We then
append the raw execution results returned by NCBI APIs to the generated texts
and continue the generation until the answer is found or another API call is
detected. Our preliminary results show that GeneGPT achieves state-of-the-art
results on three out of four one-shot tasks and four out of five zero-shot
tasks in the GeneTuring dataset. Overall, GeneGPT achieves a macro-average
score of 0.76, which is much higher than retrieval-augmented LLMs such as the
New Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as
well as other LLMs such as GPT-3 (0.16) and ChatGPT (0.12).

本文介绍了 GeneGPT，一种利用 Web 应用程序编程接口解决基因组学问题的大型语言模型（LLMs）的新方法。在经过少量 NCBI API 调用的 URL 请求演示后，GeneGPT 能够提供具有状态良好的结果，并且优于其他一些先进的 LLMs，例如 GPT-3 和 ChatGPT。