aggregating the results, LLM generates responses that actively respond to user requests, providing a
reliable decision with a conﬁdence level.

4 Experiments

4.1 Setting

In our experiments, we employed the gpt-3.5-turbo and text-davinci-003 variants of the GPT
models as the large language models, which are publicly accessible through the OpenAI API 4.
To make the LLM output more stable, we set the decoding temperature to 0. Also to regulate
the LLM output to conform to the expected format, we set the logit_bias to 0.2 on the format
constraints. We provide detailed prompts designed for the task planning, model selection, and
response generation stages in Table 5, where {{variable}} indicates that the slot needs to be populated
with the corresponding text before the prompt can be fed into the LLM.

4https://platform.openai.com/

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HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face