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Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels
April 23, 2024, 4:50 a.m. | Jan-Philipp Fr\"anken, Eric Zelikman, Rafael Rafailov, Kanishk Gandhi, Tobias Gerstenberg, Noah D. Goodman
cs.CL updates on arXiv.org arxiv.org
Abstract: When prompting a language model (LM), users frequently expect the model to adhere to a set of behavioral principles across diverse tasks, such as producing insightful content while avoiding harmful or biased language. Instilling such principles into a model can be resource-intensive and technically challenging, generally requiring human preference labels or examples. We introduce SAMI, a method for teaching a pretrained LM to follow behavioral principles that does not require any preference labels or demonstrations. …
abstract alignment arxiv cs.cl diverse expect information labels language language model prompting set tasks type
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