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home / papers / 2023 / AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2305.14387

Summary

Training instruction-following LLMs with human feedback is costly and slow to iterate on. It is also hard to evaluate reliably, and commercial vendors document it poorly. AlpacaFarm is a sandbox for cheaply studying learning from pairwise feedback (LPF). It has three parts:

  • Simulated annotators: prompted API LLMs such as GPT-4, tuned to reproduce human judgments, including disagreement between annotators and stylistic preferences.
  • Automatic evaluation: win-rate against a baseline model, measured on public instructions chosen to resemble real user queries from the Alpaca Demo.
  • Reference implementations: LPF methods including PPO, best-of-n, expert iteration and Quark.

Instructions come from the 52k-example Alpaca data, divided into stage-specific splits. To validate the framework end to end, the authors trained eleven methods on both simulated and real human feedback. The method rankings agreed almost perfectly. Methods using a reward model, especially PPO, clearly beat supervised fine-tuning. The practical value is that researchers can compare algorithms and hyperparameters in simulation, in hours and at a fraction of the cost, and then transfer the best choices to real human data.

Key points

  • Simulated feedback is roughly 45–50x cheaper than crowdworkers (the paper reports both figures), about $70 versus $3,150 per experiment, with iteration taking hours instead of days.
  • Method rankings in simulation match rankings from 10k pairs of real human feedback, with a Spearman correlation of 0.98 across eleven models. The simulator also reproduces reward-model over-optimization.
  • PPO raises the win-rate of an instruction-tuned LLaMA 7B against Davinci003 from 44% to 55%.
  • Alpaca data is repurposed into 42k examples across four splits, including 10k for SFT and 10k for pairwise preferences, with 10k held back for future use. Code is released at github.com/tatsu-lab/alpaca_farm.
  • Limitations:
    • Instructions are simple and single-turn, and all models start from LLaMA 7B.
    • Human validation relies on only 13 crowdworkers.
    • Simulated annotators favor the first-listed output and outputs from their own model.
    • Good hyperparameters (e.g., the KL coefficient) differ between simulated and human feedback.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

355 papers of our library cite it (of the 140673 whose bibliographies we read); 32 of them have a page here, 323 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2305.14387 · arXiv 2305.14387

@misc{dubois2023alpacafarm,
  title = {{AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback}},
  author = {Dubois, Yann and Li, Xuechen and Taori, Rohan and Zhang, Tianyi and Gulrajani, Ishaan and Ba, Jimmy and Guestrin, Carlos and Liang, Percy and Hashimoto, Tatsunori B.},
  year = {2023},
  doi = {10.48550/arxiv.2305.14387},
  eprint = {2305.14387},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.14387}
}

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