Alicia Parrish

Orcid: 0000-0002-1054-0516

According to our database1, Alicia Parrish authored at least 20 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2023
DMLR: Data-centric Machine Learning Research - Past, Present and Future.
CoRR, 2023

A Framework to Assess (Dis)agreement Among Diverse Rater Groups.
CoRR, 2023

"Is a picture of a bird a bird": Policy recommendations for dealing with ambiguity in machine vision models.
CoRR, 2023

Intersectionality in Conversational AI Safety: How Bayesian Multilevel Models Help Understand Diverse Perceptions of Safety.
CoRR, 2023

Inverse Scaling: When Bigger Isn't Better.
CoRR, 2023

Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models.
CoRR, 2023

Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs.
CoRR, 2023


DICES Dataset: Diversity in Conversational AI Evaluation for Safety.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

What Do NLP Researchers Believe? Results of the NLP Community Metasurvey.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions.
CoRR, 2022

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
CoRR, 2022

Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions.
CoRR, 2022

QuALITY: Question Answering with Long Input Texts, Yes!
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

BBQ: A hand-built bias benchmark for question answering.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021
Does Putting a Linguist in the Loop Improve NLU Data Collection?
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

NOPE: A Corpus of Naturally-Occurring Presuppositions in English.
Proceedings of the 25th Conference on Computational Natural Language Learning, 2021

2020
Erratum: "BLiMP: The Benchmark of Linguistic Minimal Pairs for English".
Trans. Assoc. Comput. Linguistics, 2020

BLiMP: The Benchmark of Linguistic Minimal Pairs for English.
Trans. Assoc. Comput. Linguistics, 2020

2019
Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019


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