princeton-nlp/LESS
[ICML 2024] LESS: Selecting Influential Data for Targeted Instruction Tuning
This project helps machine learning engineers and researchers improve the performance of large language models (LLMs) for specific tasks. It takes a large collection of instruction-tuning datasets and a target task, then identifies the most influential data points to train a new model. The output is a smaller, highly relevant dataset ready for fine-tuning.
514 stars. No commits in the last 6 months.
Use this if you need to fine-tune an LLM for a particular task but want to optimize training efficiency and performance by selecting the most impactful training examples instead of using an entire, potentially noisy, dataset.
Not ideal if you are looking for an out-of-the-box solution for general-purpose LLM fine-tuning without specific task-driven data optimization.
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MIT
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Last pushed
Oct 20, 2024
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