Optimizing LLMs from a Dataset Perspective

Instruction finetuning improves large language models’ ability to follow prompts by training them on instruction, optional input, and desired response pairs through supervised next-token prediction, with reinforcement learning from human feedback presented as a separate optional stage. The discussion contrasts LLM-generated datasets, including Self-Instruct, Alpaca, and instruction backtranslation, with human-created data, noting that synthetic-data-trained models can imitate an upstream model’s style without necessarily matching its factual accuracy. It highlights evidence from LIMA and AlpaGasus that smaller, carefully selected or filtered datasets can outperform much larger collections, emphasizing data quality over quantity. The article also outlines how to prepare datasets and efficiently finetune open-source models such as Llama 2 using Lit-GPT and LoRA, including support for custom CSV-formatted instruction datasets and model-specific tokenization. In the context of the NeurIPS LLM Efficiency Challenge, which prohibits LLM-generated training data and limits training to one GPU for one day, it recommends eligible human-generated datasets such as LIMA, Dolly, Open Assistant, Natural Instructions, P3, and FLAN, while proposing experiments involving dataset merging, ordering, repeated training epochs, and quality filtering.

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