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Workshop: ES-FoMo: Efficient Systems for Foundation Models

Large Language Models Are Implicitly Topic Models: Explaining and Finding Good Demonstrations for In-Context Learning

Xinyi Wang · Wanrong Zhu · Michael Saxon · Mark Steyvers · William Wang


In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. However, existing literature has highlighted the sensitivity of this capability to the selection of few-shot demonstrations. Current understandings of the underlying mechanisms by which this capability arises from regular language model pretraining objectives remain disconnected from the real-world LLMs. This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as implicit topic models. On this premise, we propose an algorithm to select optimal demonstrations from a set of annotated data with a small LLM, then directly generalize the selected demonstrations to larger LLMs. We demonstrate a significant 12.5\% improvement relative to the random selection baseline, averaged over eight GPT models on eight real-world text classification datasets. Our empirical findings support our hypothesis that LLMs implicitly infer a latent variable containing task information.

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