Towards Autonomous Hypothesis Verification via Language Models with Minimal Guidance

Shiro Takagi; Ryutaro Yamauchi; Wataru Kumagai

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arXiv · 2311.09706

Towards Autonomous Hypothesis Verification via Language Models with Minimal Guidance

Shiro Takagi·Ryutaro Yamauchi·Wataru Kumagai

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Abstract

Research automation efforts usually employ AI as a tool to automate specific tasks within the research process. To create an AI that truly conduct research themselves, it must independently generate hypotheses, design verification plans, and execute verification. Therefore, we investigated if an AI itself could autonomously generate and verify hypothesis for a toy machine learning research problem. We prompted GPT-4 to generate hypotheses and Python code for hypothesis verification with limited methodological guidance. Our findings suggest that, in some instances, GPT-4 can autonomously generate and validate hypotheses without detailed guidance. While this is a promising result, we also found that none of the verifications were flawless, and there remain significant challenges in achieving autonomous, human-level research using only generic instructions. These findings underscore the need for continued exploration to develop a general and autonomous AI researcher.

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BibTeX RIS

Shiro Takagi, Ryutaro Yamauchi, Wataru Kumagai. 2023-11-16. Towards Autonomous Hypothesis Verification via Language Models with Minimal Guidance. https://arxiv.org/abs/2311.09706

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