Summary of Concept Distillation From Strong to Weak Models Via Hypotheses-to-theories Prompting, by Emmanuel Aboah Boateng et al.
Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting
by Emmanuel Aboah Boateng, Cassiano O. Becker, Nabiha Asghar, Kabir Walia, Ashwin Srinivasan, Ehi Nosakhare, Soundar Srinivasan, Victor Dibia
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Concept Distillation (CD) technique optimizes prompts for weaker language models, enhancing their performance on complex tasks. CD collects mistakes from initial prompts, generates reasons for these errors using a strong model, and filters these rules based on validation set performance to create updated prompts. This approach was evaluated on NL2Code and mathematical reasoning tasks, showing significant boosts in accuracy for small and weaker models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We can improve the performance of language models by creating better prompts. A new technique called Concept Distillation helps us do this. It looks at what mistakes are made with a first prompt, then uses a stronger model to understand why these mistakes happened. The technique combines these ideas into a new prompt that works better than the original one. This makes it possible for smaller or weaker language models to perform as well as bigger ones. |
Keywords
» Artificial intelligence » Distillation » Prompt
