Pretrained language models often do not perform
tasks in ways that are in line with our
preferences, e.g., generating offensive text or
factually incorrect summaries. Recent work
approaches the above issue by learning from
a simple form of human evaluation: comparisons
between pairs of model-generated task
outputs. Comparison feedback conveys limited
information about human preferences per
human evaluation. Here, we propose to learn
from natural language feedback, which conveys
more information per human evaluation.
We learn from language feedback on model
outputs using a three-step learning algorithm.
First, we condition the language model on the
initial output and feedback to generate many
refinements. Second, we choose the refinement
with the highest similarity to the feedback.
Third, we finetune a language model to
maximize the likelihood of the chosen refinement
given the input. In synthetic experiments,
we first evaluate whether language models accurately
incorporate feedback to produce refinements,
finding that only large language
models (175B parameters) do so. Using only
100 samples of human-written feedback, our
learning algorithm finetunes a GPT-3 model to
roughly human-level summarization ability.
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This content will become publicly available on February 28, 2025
Learning from Natural Language Feedback
The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development. We build upon this observation by formalizing an algorithm for learning from natural language feedback at training time instead, which we call Imitation learning from Language Feedback (ILF). ILF requires only a small amount of human-written feedback during training and does not require the same feedback at test time, making it both user-friendly and sample-efficient. We further show that ILF can be seen as a form of minimizing the KL divergence to the target distribution and demonstrate proof-of-concepts on text summarization and program synthesis tasks. For code generation, ILF improves a Codegen-Mono 6.1B model’s pass@1 rate from 22% to 36% on the MBPP benchmark, outperforming both fine-tuning on MBPP and on human- written repaired programs. For summarization, we show that ILF can be combined with learning from human preferences to improve a GPT-3 model’s summarization performance to be comparable to human quality, outperforming fine-tuning on human-written summaries. Overall, our results suggest that ILF is both more effective and sample-efficient than training exclusively on demonstrations for improving an LLM’s performance on a variety of tasks.
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- Award ID(s):
- 2046556
- PAR ID:
- 10544335
- Publisher / Repository:
- OpenReview
- Date Published:
- Journal Name:
- Transactions on machine learning research
- ISSN:
- 2835-8856
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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