Summary of Constitutionalexperts: Training a Mixture Of Principle-based Prompts, by Savvas Petridis et al.
ConstitutionalExperts: Training a Mixture of Principle-based Prompts
by Savvas Petridis, Ben Wedin, Ann Yuan, James Wexler, Nithum Thain
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces ConstitutionalExperts, a method for learning a prompt consisting of constitutional principles given a training dataset. Unlike prior methods, the approach incrementally improves the prompt by surgically editing individual principles. The authors also demonstrate that learning unique prompts for different semantic regions and using a mixture-of-experts (MoE) architecture can improve overall performance. They compare their method to other state-of-the-art prompt-optimization techniques across six benchmark datasets and investigate whether MoE improves these techniques. The results suggest that ConstitutionalExperts outperforms other methods by 10.9% (F1) and that MoE improves all techniques, indicating its broad applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do many things if you give them the right prompt, but creating a good prompt is still hard work. This paper finds a new way to make prompts better by adding small changes one rule at a time. They also show that using different prompts for different parts of the training data and combining multiple experts to help with predictions makes a big difference. The authors compare their method to other ways of making prompts better and see how well it works on six sets of data. The results are impressive, showing that this new approach is 10.9% better at getting the right answer. |
Keywords
» Artificial intelligence » Mixture of experts » Optimization » Prompt
