Summary of Learning to Generate Instruction Tuning Datasets For Zero-shot Task Adaptation, by Nihal V. Nayak et al.
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
by Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach
First submitted to arxiv on: 28 Feb 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 proposed Bonito model enables zero-shot task adaptation of large language models on users’ private data by converting unannotated text into task-specific training datasets for instruction tuning. The model is trained on a new dataset created by remixing existing instruction tuning datasets into meta-templates, which produces training examples with input text and output instructions. Bonito generates synthetic tasks for various domains and adapts language models, significantly improving their average performance over the self-supervised baseline. For instance, adapting Mistral-Instruct-v2 with Bonito improves zero-shot performance by 22.1 F1 points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bonito is a new tool that helps computers learn to do specific tasks without needing labeled training data. It takes unmarked text and turns it into instructions that can be used to train language models. This means people can use their own private data to fine-tune language models for specific domains, like medicine or law. Bonito was trained on a large dataset of mixed instruction tuning datasets, which allows it to generate tasks in different formats. It works well across various task types and improves the performance of language models. |
Keywords
* Artificial intelligence * Instruction tuning * Self supervised * Zero shot
