84 citations · 655 across the 44 of their papers we have counts for
6 papers · 1 filter
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts
Daniel Khashabi, Shane Lyu, Sewon Min +8
Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the fe…
MetaICL: Learning to Learn In Context
Sewon Min, Mike Lewis, Luke Zettlemoyer +1
We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learni…
Noisy Channel Language Model Prompting for Few-Shot Text Classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi +1
We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as di…
FaVIQ: FAct Verification from Information-seeking Questions
Jungsoo Park, Sewon Min, Jaewoo Kang +2
Despite significant interest in developing general purpose fact checking models, it is challenging to construct a large-scale fact verification dataset with realistic real-world cl…
Joint Passage Ranking for Diverse Multi-Answer Retrieval
Sewon Min, Kenton Lee, Ming-Wei Chang +2
We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question. This task requires joint model…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…