Summary of Instruct-skillmix: a Powerful Pipeline For Llm Instruction Tuning, by Simran Kaur et al.
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning
by Simran Kaur, Simon Park, Anirudh Goyal, Sanjeev Arora
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper introduces Instruct-SkillMix, an automated approach for generating diverse and high-quality SFT data. The method involves two stages: first, it extracts core “skills” using a powerful Language Model (LLM), either from existing datasets or by directly prompting the model. Then, it uses this LLM to generate instruction-response pairs that exhibit randomly chosen skill combinations. This promotes diversity and difficulty in the generated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make lots of good SFT data. They use two steps: first, they figure out what makes something “good” at following instructions (like skills), either from old datasets or by asking the computer directly. Then, they use that same powerful computer to make many instruction-response pairs with different skills mixed together. This helps keep things interesting and challenging. |
Keywords
* Artificial intelligence * Language model * Prompting
