Summary of Self-directed Synthetic Dialogues and Revisions Technical Report, by Nathan Lambert et al.
Self-Directed Synthetic Dialogues and Revisions Technical Report
by Nathan Lambert, Hailey Schoelkopf, Aaron Gokaslan, Luca Soldaini, Valentina Pyatkin, Louis Castricato
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 Self Directed Synthetic Dialogues (SDSD), an experimental dataset consisting of guided conversations between language models, aiming to advance open fine-tuning methods. SDSD features multi-turn conversations generated using DBRX, Llama 2 70B, and Mistral Large, with instructions to follow a conversation plan. The authors also explore incorporating Constitutional AI principles to create synthetic preference data by revising the final conversation turn. This work encourages further research on multi-turn data and open models for expanding synthetic data’s impact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special dataset called Self Directed Synthetic Dialogues, where language models talk to themselves in conversations. These conversations are like having a discussion with yourself, but the models follow rules given beforehand. The goal is to help improve how we fine-tune language models and make them better at understanding human instructions. By making these conversations happen, the researchers hope to inspire more people to work on creating multi-turn data and using open models. |
Keywords
* Artificial intelligence * Fine tuning * Llama * Synthetic data
