1.1k citations · 1.9k across the 23 of their papers we have counts for
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Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?
Weijia Shi, Xiaochuang Han, Hila Gonen +3
Large language models can perform new tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, bu…
Contrastive Decoding: Open-ended Text Generation as Optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried +5
Given a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling…
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
Julian Michael, Ari Holtzman, Alicia Parrish +8
We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field,…
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Sewon Min, Xinxi Lyu, Ari Holtzman +4
Large language models (LMs) are able to in-context learn -- perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making prediction…