Memory-assisted prompt editing to improve GPT-3 after deployment

Conference on Empirical Methods in Natural Language Processing 2022 183 cites
2022:152023:292024:422025:482026:49
2022 2026
183

Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang

Abstract

Large LMs such as GPT -3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT -3 would mistakenly interpret “What word is similar to good ?” to mean a homonym, while the user intended a synonym. Our goal is to effectively correct such errors via user interactions with the sys-tem but without retraining, which will be pro-hibitively costly. We pair GPT -3 with a growing memory of recorded cases where the model misunderstood the user’s intents, along with user feedback for clarification. Such a memory allows our system to produce enhanced prompts for any new query based on the user feedback for error correction on similar cases in the past. On four tasks (two lexical tasks, two advanced ethical reasoning tasks), we show how a (simulated) user can interactively teach a deployed GPT -3, substantially increasing its accuracy over the queries with different kinds of misunderstandings by the GPT -3. Our approach is a step towards the low-cost utility enhancement for very large pre-trained LMs. 1

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