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. In this paper%2C we examine the potential of large language models (LLMs) to generate hypotheses. We focus on hypothesis generation based on data (i.e.%2C labeled examples). To enable LLMs to handle arbitrarily long contexts%2C we generate initial hypotheses from a small number of examples and then update them iteratively to improve the quality of hypotheses. Inspired by multi-armed bandits%2C we design a reward function to inform the exploitation-exploration tradeoff in the update process. Our algorithm is able to generate hypotheses that enable much better predictive performance than few-shot prompting in classification tasks%2C improving accuracy by 31.7%25 on a synthetic dataset and by 13.9%25%2C 3.3%25 and%2C 24.9%25 on three real-world datasets. We also outperform supervised learning by 12.8%25 and 11.2%25 on two challenging real-world datasets. Furthermore%2C we find that the generated hypotheses not only corroborate human-verified theories but also uncover new insights for the tasks.?quality=80&w=800)
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