Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for
  Instruction Fine-Tuning
Hao Zhao Maksym Andriushchenko Francesco Croce Nicolas Flammarion
There is a consensus that instruction fine-tuning of LLMs requires
high-quality data, but what are they? LIMA (NeurIPS 2023) and AlpaGasus (ICLR
2024) are state-of-the-art methods for selecting such high-quality examples,
either via manual curation or using GPT-3.5-Turbo as a quality scorer. We show
that the extremely simple baseline of selecting the 1,000 instructions with
longest responses from standard datasets can consistently outperform these
sophisticated methods according to GPT-4 and PaLM-2 as judges, while remaining
competitive on the OpenLLM benchmarks that test factual knowledge. We
demonstrate this for several state-of-the-art LLMs (Llama-2-7B, Llama-2-13B,
and Mistral-7B) and datasets (Alpaca-52k and Evol-Instruct-70k). In addition, a
lightweight refinement of such long instructions can further improve the
abilities of the fine-tuned LLMs, and allows us to obtain the 2nd
highest-ranked Llama-2-7B-based model on AlpacaEval 2.0 while training on only
1,000 examples and no extra preference data. We also conduct a thorough
analysis of our models to ensure that their enhanced performance is not simply
due to GPT-4's preference for longer responses, thus ruling out any artificial
improvement. In conclusion, our findings suggest that fine-tuning on the
longest instructions should be the default baseline for any research on
instruction fine-tuning.