Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more. Here, we explore alternative objectives for pretraining LMs in a way that also guides them to generate text aligned with human preferences. We benchmark five objectives for pretraining with human feedback across three tasks and study how they affect the trade-off between alignment and
capabilities of pretrained LMs. We find a Pareto optimal and simple approach among those we explored: conditional training, or learning distribution over tokens conditional on their human preference scores given by a reward model. Conditional training reduces the rate of undesirable content by up to an order of magnitude, both when generating without a prompt and with an adversarially chosen prompt. Moreover, conditional training maintains the downstream task performance of standard LM pretraining, both before and after
task-specific finetuning. Pretraining with human feedback results in much better preference satisfaction than standard LM pretraining followed by finetuning with feedback, i.e., learning and then unlearning undesirable behavior. Our results suggest that we should move beyond imitation learning when pretraining LMs and incorporate human preferences from the start of training.
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Preference-grounded Token-level Guidance for Language Model Fine-tuning
Aligning language models (LMs) with preferences is an important problem in
natural language generation. A key challenge is that preferences are typically
provided at the sequence level while LM training and generation both occur at the
token level. There is, therefore, a granularity mismatch between the preference
and the LM training losses, which may complicate the learning problem. In this
paper, we address this issue by developing an alternate training process, where we
iterate between grounding the sequence-level preference into token-level training
guidance, and improving the LM with the learned guidance. For guidance learning,
we design a framework that extends the pairwise-preference learning in imitation
learning to both variable-length LM generation and the utilization of the preference
among multiple generations. For LM training, based on the amount of supervised
data, we present two minimalist learning objectives that utilize the learned guidance.
In experiments, our method performs competitively on two distinct representative
LM tasks — discrete-prompt generation and text summarization. Source codes are
released at https://github.com/Shentao-YANG/Preference_Grounded_Guidance.
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- Award ID(s):
- 2212418
- PAR ID:
- 10536737
- Publisher / Repository:
- Neural Information Processing Systems
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Language models (LMs) are pretrained to imitate text from large and diverse datasets that contain content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, among others. Here, we explore alternative objectives for pretraining LMs in a way that also guides them to generate text aligned with human preferences. We benchmark five objectives for pretraining with human feedback across three tasks and study how they affect the alignment and capabilities of pretrained LMs. We find a Pareto-optimal and simple approach among those we explored: conditional training, or learning distribution over tokens conditional on their human preference scores. Conditional training reduces the rate of undesirable content by up to an order of magnitude, both when generating without a prompt and with an adversarially-chosen prompt. Moreover, conditional training maintains the downstream task performance of standard LM pretraining, both before and after task-specific finetuning. Pretraining with human feedback results in much better preference satisfaction than standard LM pretraining followed by finetuning with feedback, i.e., learning and then unlearning undesirable behavior. Our results suggest that we should move beyond imitation learning when pretraining LMs and incorporate human preferences from the start of training.more » « less
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Accepted Manuscript1.0
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- The DOI is not currently available.
