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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Aligning pretrained language models with instruction data generated by themselves.
| Date | Stars |
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| 2026-07-31 | 4607 |
| 2026-08-02 | 4607 |
| 2026-08-04 | 4608 |
| 2026-08-06 | 4608 |
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# Self-Instruct: Aligning LM with Self Generated Instructions This repository contains code and data for the [Self-Instruct paper](https://arxiv.org/abs/2212.10560), a method for aligning pretrained language models with instructions. ## Introduction Self-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation. ### Background In recent years, there has been a growing interest in building models that can follow natural language instructions to perform a wide range of tasks. These models, known as "instruction-tuned" language models, have demonstrated the ability to generalize to new tasks. However, their performance is heavily dependent on the quality and quantity of the human-written instruction data used to train them, which can be limited in diversity and creativity. To overcome these limitations, it is important to develop alternative approaches for supervising instruction-tuned models and improving their instruction-following capabilities. ### How Self-Instruct works? The Self-Instruct process is an iterative bootstrapping algorithm that starts with a seed set of manually-written instructions and uses them to prompt the language model to generate new instructions and corresponding input-output instances. These generations are then filtered to remove low-quality or similar ones, and the resulting data is added back to the task pool. This process can be repeated multiple times, resulting in a large collection of instructional data that can be used to fine-tune the language model to follow instructions more effectively. Here is an overview of Self-Instruct:  ## Usage \* **This work is still in progress. We may update the code and data as we make progress. Please be cautious about the version control.** ### Instruction-tuning using our Self-Instruct data We release a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The entire model-generated data can be accessed in `data/gpt3-generations/batch_221203/all_instances_82K.jsonl`. This data (+ the 175 seed tasks) reformatted in clean GPT3-finetuning format (prompt + completion) is put in `data/finetuning/self_instruct_221203`. You can use the script in [`./scripts/finetune_gpt3.sh`](./scripts/finetune_gpt3.sh) to finetune GPT3 on this data. **Note**: This data is generated by a language model (GPT3) and inevitably contains some errors or biases. We analyzed the data quality on 200 random instructions in our paper, and found that 46% of the data points may have problems. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ### Evaluating instruction-following capabilities We also release a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of [the self-instruct paper](https://arxiv.org/abs/2212.10560). Please refer to [the human evaluation README](human_eval/README.md) for more details. ### Generating Self-Instruct data from scratch To generate Self-Instruct data using your own seed tasks or other models, we open-source our scripts for the entire pipeline here. Our current code is only tested on the GPT3 model accessible via the [OpenAI API](https://beta.openai.com/docs/models/gpt-3). Here are the scripts for generating the data: ```bash # 1. Generate instructions from the seed tasks ./scripts/generat
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Yizhong Wang · University of Washington · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7383cb054a541e28, topic:instruction-tuning
matched fp:7383cb054a541e28, topic:language-model