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Alicia Parrish

17 papers hereh-index 156.5k citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author7
  • middle author8

Across the 15 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CL13
  • cs.HC2
  • cs.CY1
  • cs.LG1
same name
  • Alicia Parrish — 11 papers, h 7
  • Alicia Parrish — 4 papers, h 5
  • Alicia Parrish — 4 papers, h 3
  • Alicia Parrish — 2 papers
  • Alicia Parrish — 1 paper, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 796 across the 16 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CL2021★ 10 cited

QuALITY: Question Answering with Long Input Texts, Yes!

Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi +8

To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average lengt…

cs.CL2021★ 2 cited

BBQ: A Hand-Built Bias Benchmark for Question Answering

Alicia Parrish, Angelica Chen, Nikita Nangia +5

It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA…

cs.CL2021

NOPE: A Corpus of Naturally-Occurring Presuppositions in English

Alicia Parrish, Sebastian Schuster, Alex Warstadt +5

Understanding language requires grasping not only the overtly stated content, but also making inferences about things that were left unsaid. These inferences include presupposition…

cs.CL2021

Does Putting a Linguist in the Loop Improve NLU Data Collection?

Alicia Parrish, William Huang, Omar Agha +7

Many crowdsourced NLP datasets contain systematic gaps and biases that are identified only after data collection is complete. Identifying these issues from early data samples durin…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.