Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting

Emmanuel Aboah Boateng; Cassiano O. Becker; Nabiha Asghar; Kabir Walia; Ashwin Srinivasan; Ehi Nosakhare; Soundar Srinivasan; Victor Dibia

arXiv · 2408.09365

Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting

Emmanuel Aboah Boateng·Cassiano O. Becker·Nabiha Asghar·Kabir Walia·Ashwin Srinivasan·Ehi Nosakhare·Soundar Srinivasan·Victor Dibia

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Abstract

Hand-crafting high quality prompts to optimize the performance of language models is a complicated and labor-intensive process. Furthermore, when migrating to newer, smaller, or weaker models (possibly due to latency or cost gains), prompts need to be updated to re-optimize the task performance. We propose Concept Distillation (CD), an automatic prompt optimization technique for enhancing weaker models on complex tasks. CD involves: (1) collecting mistakes made by weak models with a base prompt (initialization), (2) using a strong model to generate reasons for these mistakes and create rules/concepts for weak models (induction), and (3) filtering these rules based on validation set performance and integrating them into the base prompt (deduction/verification). We evaluated CD on NL2Code and mathematical reasoning tasks, observing significant performance boosts for small and weaker language models. Notably, Mistral-7B's accuracy on Multi-Arith increased by 20%, and Phi-3-mini-3.8B's accuracy on HumanEval rose by 34%. Compared to other automated methods, CD offers an effective, cost-efficient strategy for improving weak models' performance on complex tasks and enables seamless workload migration across different language models without compromising performance.

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Emmanuel Aboah Boateng, Cassiano O. Becker, Nabiha Asghar, Kabir Walia, Ashwin Srinivasan, Ehi Nosakhare, Soundar Srinivasan, Victor Dibia. 2024-08-18. Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting. https://arxiv.org/abs/2408.09365

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