The Power of Adaptation: Boosting In-Context Learning through Adaptive Prompting

• URL: http://arxiv.org/abs/2412.17891v1
• Date: Mon, 23 Dec 2024 15:49:43 GMT
• Title: The Power of Adaptation: Boosting In-Context Learning through Adaptive Prompting
• Authors: Shuzhang Cai, Twumasi Mensah-Boateng, Xander Kuksov, Jing Yuan, Shaojie Tang,
• Abstract summary: Large Language Models (LLMs) have demonstrated exceptional abilities across a broad range of language-related tasks. We propose textscAdaptive-Prompt, a novel method that adaptively selects exemplars by leveraging model feedback. Experimental results show that textscAdaptive-Prompt significantly enhances LLM performance across a variety of reasoning tasks.
• Score: 8.260097638532878
• License:
• Abstract: Large Language Models (LLMs) have demonstrated exceptional abilities across a broad range of language-related tasks, including generating solutions to complex reasoning problems. An effective technique to enhance LLM performance is in-context learning, which encourages a step-by-step reasoning process by including explanatory examples to guide the model's responses. However, selecting appropriate exemplars for the model poses a challenge, as each dataset demands a distinct set of exemplars to enable the LLM to learn effectively and perform well on the test set. Current studies often rely on uncertainty- or diversity-based selection strategies to select exemplars for annotation and to improve model learning. However, these studies typically employ a non-adaptive approach, selecting a set of exemplars all at once. We argue that this non-adaptive strategy may result in a set of exemplars with high redundancy in terms of the knowledge covered, ultimately reducing their overall informativeness. To address this limitation, we propose \textsc{Adaptive-Prompt}, a novel method that adaptively selects exemplars by leveraging model feedback from previously chosen exemplars. Experimental results show that \textsc{Adaptive-Prompt} significantly enhances LLM performance across a variety of reasoning tasks.

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