Summary of Better Alignment with Instruction Back-and-forth Translation, by Thao Nguyen et al.
Better Alignment with Instruction Back-and-Forth Translation
by Thao Nguyen, Jeffrey Li, Sewoong Oh, Ludwig Schmidt, Jason Weston, Luke Zettlemoyer, Xian Li
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a new method called instruction back-and-forth translation to create high-quality synthetic data grounded in world knowledge for aligning large language models (LLMs). The approach generates and curates synthetic instructions using the backtranslation approach proposed by Li et al. (2023a) and rewrites responses based on initial documents. Fine-tuning with resulting pairs yields higher win rates on AlpacaEval than other common instruction datasets, including Humpback, ShareGPT, Open Orca, Alpaca-GPT4, and Self-instruct. The paper also demonstrates that rewriting responses with an LLM outperforms direct distillation, showing significant distinction in embedding space between generated text distributions. Additionally, the backtranslated instructions are found to be of higher quality than other sources of synthetic instructions, while rewritten responses are more diverse and complex than those obtained from distillation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates fake data that helps train language models. They use a special way to translate instructions and responses to make it better for training large language models. This approach does well on tests compared to other ways of creating synthetic data. The results show that rewriting responses with the language model is better than just copying what’s already there. |
Keywords
» Artificial intelligence » Distillation » Embedding space » Fine tuning » Language model » Synthetic data » Translation
