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From the 1 of 18 linked papers with an AI index.

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20152022
most citedREALM: Retrieval-Augmented Language Model Pre-Training

521 citations · 1.3k across the 11 of their papers we have counts for

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Showing cs.CLShow all

18 papers · 1 filter

cs.CL2022

Meta-Learning Fast Weight Language Models

Kevin Clark, Kelvin Guu, Ming-Wei Chang +3

Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens and substantially improves LM performance. However,…

cs.CL202247 cited

Promptagator: Few-shot Dense Retrieval From 8 Examples

Zhuyun Dai, Vincent Y. Zhao, Ji Ma +7

Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is lim…

cs.CL202137 cited

Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer

Iulia Turc, Kenton Lee, Jacob Eisenstein +2

Despite their success, large pre-trained multilingual models have not completely alleviated the need for labeled data, which is cumbersome to collect for all target languages. Zero…

cs.CL202151 cited

Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations

Jonathan Herzig, Peter Shaw, Ming-Wei Chang +3

Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization. While specialized mode…

cs.CL2021

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…

cs.CL20204 cited

CapWAP: Captioning with a Purpose

Adam Fisch, Kenton Lee, Ming-Wei Chang +2

The traditional image captioning task uses generic reference captions to provide textual information about images. Different user populations, however, will care about different vi…