Summary of Ensemw2s: Can An Ensemble Of Llms Be Leveraged to Obtain a Stronger Llm?, by Aakriti Agrawal et al.
EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM?
by Aakriti Agrawal, Mucong Ding, Zora Che, Chenghao Deng, Anirudh Satheesh, John Langford, Furong Huang
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed innovative approach aims to harness the capabilities of multiple Large Language Models (LLMs) to create an even more powerful model. The work introduces an easy-to-hard (e2h) framework for studying weak-to-strong generalization, which mirrors real-world challenges where direct human supervision is limited. The authors develop a novel AdaBoost-inspired ensemble method and demonstrate that an ensemble of weak supervisors can enhance the performance of stronger LLMs across classification and generative tasks on difficult QA datasets. The approach matches the performance of models trained on ground-truth data in several cases, establishing a new benchmark for weak-to-strong generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how we can combine the strengths of many Large Language Models (LLMs) to make an even better one. Scientists are trying to figure out how to teach AI models using small amounts of information and see if they can learn to do things on their own. The researchers created a new way to test this idea, where simple tasks help train more complex ones. They used this approach with different types of questions and answers and found that it worked well. This is important because it might be the key to teaching AI models when we don’t have much information. |
Keywords
» Artificial intelligence » Classification » Generalization
