Self-Instruct: Research paper for aligning language models with self-generated instructions
Top 11.2% on sourcepulse
This repository provides the Self-Instruct framework and associated data for aligning pre-trained language models with instructions. It enables instruction-following capabilities without extensive manual annotation by using a model's own generations to create instructional data, benefiting researchers and developers aiming to improve LLM usability.
How It Works
Self-Instruct employs an iterative bootstrapping algorithm. It starts with a seed set of instructions, prompts a language model (like GPT-3) to generate new instructions and input-output instances, and then filters these generations for quality and diversity. The refined data is added back to the pool, allowing for repeated cycles to build a large, instruction-following dataset. This approach reduces reliance on costly manual data creation.
Quick Start & Requirements
data/gpt3-generations/batch_221203/all_instances_82K.jsonl
. A finetuning-ready version is at data/finetuning/self_instruct_221203
.generate_instructions.sh
, is_clf_or_not.sh
, generate_instances.sh
, and prepare_for_finetuning.sh
.Highlighted Details
Maintenance & Community
The project is marked as "still in progress" with potential for code and data updates. No specific community channels or contributor details are listed in the README.
Licensing & Compatibility
The repository does not explicitly state a license. The code and data are presented for research purposes, and users are encouraged to cite the associated paper. Commercial use is not specified.
Limitations & Caveats
The data generation pipeline is only tested with GPT-3 via the OpenAI API. The generated data may contain errors or biases, with a significant portion identified as potentially problematic.
2 years ago
1+ week