Self-Alignment with Instruction Backtranslation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909134385840128 |
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| author | Li, Xian Yu, Ping Zhou, Chunting Schick, Timo Levy, Omer Zettlemoyer, Luke Weston, Jason Lewis, Mike |
| author_facet | Li, Xian Yu, Ping Zhou, Chunting Schick, Timo Levy, Omer Zettlemoyer, Luke Weston, Jason Lewis, Mike |
| contents | We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approach, named instruction backtranslation, starts with a language model finetuned on a small amount of seed data, and a given web corpus. The seed model is used to construct training examples by generating instruction prompts for web documents (self-augmentation), and then selecting high quality examples from among these candidates (self-curation). This data is then used to finetune a stronger model. Finetuning LLaMa on two iterations of our approach yields a model that outperforms all other LLaMa-based models on the Alpaca leaderboard not relying on distillation data, demonstrating highly effective self-alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_06259 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Self-Alignment with Instruction Backtranslation Li, Xian Yu, Ping Zhou, Chunting Schick, Timo Levy, Omer Zettlemoyer, Luke Weston, Jason Lewis, Mike Computation and Language We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approach, named instruction backtranslation, starts with a language model finetuned on a small amount of seed data, and a given web corpus. The seed model is used to construct training examples by generating instruction prompts for web documents (self-augmentation), and then selecting high quality examples from among these candidates (self-curation). This data is then used to finetune a stronger model. Finetuning LLaMa on two iterations of our approach yields a model that outperforms all other LLaMa-based models on the Alpaca leaderboard not relying on distillation data, demonstrating highly effective self-alignment. |
| title | Self-Alignment with Instruction Backtranslation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.06259 |