EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM? (2410.04571v1)

Abstract: How can we harness the collective capabilities of multiple 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.

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

The paper "EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM?" primarily investigates the feasibility and effectiveness of using an ensemble of weak models to enhance the generalizability and performance of stronger models in the context of LLMs. Specifically, this paper explores the problem of weak-to-strong (w2s) generalization, a significant challenge in artificial intelligence alignment, especially when AI systems exceed human supervisory capabilities—a phenomenon known as super-alignment.

Methodology and Framework

The authors introduce an easy-to-hard (e2h) framework to approach w2s generalization, wherein weak models trained on relatively simpler tasks are utilized to supervise and improve the performance of stronger models on more complex tasks. This approach simulates real-world challenges where human oversight is constrained. The researchers propose a novel ensemble-based technique, inspired by AdaBoost, to effectively combine the outputs of several weak models, thereby providing a robust supervisory mechanism for training stronger LLMs.

Key components of this methodology include:

• Task Setup: The framework is designed to harness the combined capabilities of multiple weak models, focusing particularly on binary classification and generative tasks across complex question-answering datasets, such as SciQ, ARC, and Quartz.
• Ensemble Methodology: By adopting an AdaBoost-like approach, the ensemble of weak models can surpass traditional single-model supervision systems. This method involves a weighted combination of weak model predictions, allowing for a more precise aggregation that enhances the generalization capabilities of the resulting strong model.
• Application to Easy-to-Hard Transitions: The e2h framework facilitates transitioning from easy to hard tasks by leveraging the capabilities of weak models in simpler domains to instruct and enhance stronger models in tackling more intricate problems.

Experimental Results

The researchers report significant improvements in performance when applying their EnsemW2S method. Notably, the ensemble approach reaches or even surpasses the performance of strong models trained on ground-truth data in several instances:

• Binary Classification Tasks: Accuracy improvements up to 14% over existing baselines were observed, with average improvements of 7% across model pairs.
• Generative Tasks for Question-Answering: This method showed up to 10% improvement over baselines, with average increases of 3.34% for Quartz and 4.4% for ARC datasets.

These results underline the efficacy of the proposed method in achieving w2s generalization, especially in scenarios with limited high-quality labeled data.

Implications and Future Directions

The implications of this research are manifold, both in practical deployment and theoretical exploration. Practically, applying ensemble methods to LLM supervision can significantly democratize AI training by reducing dependency on resource-intensive human-labeled datasets. This could expand the development of advanced AI systems capable of self-improvement through collaborative weak model supervision.

Theoretically, the introduction of e2h generalization within the broader field of AI alignment presents a promising avenue for further exploration. The paper paves the way for extending ensemble learning techniques to other phases of LLM training, such as reward modeling and reinforcement learning. Future work may explore more sophisticated ensemble learning methodologies and adaptively integrate these techniques within the broader AI learning pipeline.

In conclusion, this paper establishes that ensemble learning, through collective supervision of weak models, can play a pivotal role in advancing AI capabilities. EnsemW2S not only advances current understanding of AI alignment challenges but also provides a viable solution for utilizing diverse model capabilities to improve collective supervision in AI systems.