RARR: Researching and Revising What Language Models Say, Using Language Models

• URL: http://arxiv.org/abs/2210.08726v3
• Date: Wed, 31 May 2023 17:55:02 GMT
• Title: RARR: Researching and Revising What Language Models Say, Using Language Models
• Authors: Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y. Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu
• Abstract summary: We propose RARR (Retrofit Attribution using Research and Revision), a system that automatically finds attribution for the output of any text generation model. We find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models.
• Score: 31.057495176599502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog. However, they sometimes generate unsupported or misleading content. A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism for attribution to external evidence. To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision), a system that 1) automatically finds attribution for the output of any text generation model and 2) post-edits the output to fix unsupported content while preserving the original output as much as possible. When applied to the output of several state-of-the-art LMs on a diverse set of generation tasks, we find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models. Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.

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