EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM?

• URL: http://arxiv.org/abs/2410.04571v1
• Date: Sun, 6 Oct 2024 18:06:42 GMT
• Title: EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM?
• Authors: Aakriti Agrawal, Mucong Ding, Zora Che, Chenghao Deng, Anirudh Satheesh, John Langford, Furong Huang,
• Abstract summary: We propose an innovative approach to weak-to-strong (w2s) generalization. We show that weak models trained on simpler tasks collaboratively supervise stronger models on more complex tasks. We observe an improvement of up to 14% over existing baselines and average improvements of 5% and 4% for binary classification and generative tasks.
• Score: 28.43206274079919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How can we harness the collective capabilities of multiple Large Language Models (LLMs) to create an even more powerful model? This question forms the foundation of our research, where we propose an innovative approach to weak-to-strong (w2s) generalization-a critical problem in AI alignment. Our work introduces an easy-to-hard (e2h) framework for studying the feasibility of w2s generalization, where weak models trained on simpler tasks collaboratively supervise stronger models on more complex tasks. This setup mirrors real-world challenges, where direct human supervision is limited. To achieve this, we develop a novel AdaBoost-inspired ensemble method, demonstrating that an ensemble of weak supervisors can enhance the performance of stronger LLMs across classification and generative tasks on difficult QA datasets. In several cases, our ensemble approach matches the performance of models trained on ground-truth data, establishing a new benchmark for w2s generalization. We observe an improvement of up to 14% over existing baselines and average improvements of 5% and 4% for binary classification and generative tasks, respectively. This research points to a promising direction for enhancing AI through collective supervision, especially in scenarios where labeled data is sparse or insufficient.

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