565 citations · 796 across the 16 of their papers we have counts for
17 papers
"Is a picture of a bird a bird": Policy recommendations for dealing with ambiguity in machine vision models
Alicia Parrish, Sarah Laszlo, Lora Aroyo
Many questions that we ask about the world do not have a single clear answer, yet typical human annotation set-ups in machine learning assume there must be a single ground truth la…
Intersectionality in Conversational AI Safety: How Bayesian Multilevel Models Help Understand Diverse Perceptions of Safety
Christopher M. Homan, Greg Serapio-Garcia, Lora Aroyo +5
Conversational AI systems exhibit a level of human-like behavior that promises to have profound impacts on many aspects of daily life -- how people access information, create conte…
DICES Dataset: Diversity in Conversational AI Evaluation for Safety
Lora Aroyo, Alex S. Taylor, Mark Diaz +5
Machine learning approaches often require training and evaluation datasets with a clear separation between positive and negative examples. This risks simplifying and even obscuring…
Inverse Scaling: When Bigger Isn't Better
Ian R. McKenzie, Alexander Lyzhov, Michael Pieler +24
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, w…
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models
Alicia Parrish, Hannah Rose Kirk, Jessica Quaye +10
The generative AI revolution in recent years has been spurred by an expansion in compute power and data quantity, which together enable extensive pre-training of powerful text-to-i…
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Angelica Chen, Jason Phang, Alicia Parrish +4
Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consist…
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions
Alicia Parrish, Harsh Trivedi, Nikita Nangia +4
The use of language-model-based question-answering systems to aid humans in completing difficult tasks is limited, in part, by the unreliability of the text these systems generate.…
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,…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions
Alicia Parrish, Harsh Trivedi, Ethan Perez +4
Current QA systems can generate reasonable-sounding yet false answers without explanation or evidence for the generated answer, which is especially problematic when humans cannot r…
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…
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…
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…
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…
BLiMP: The Benchmark of Linguistic Minimal Pairs for English
Alex Warstadt, Alicia Parrish, Haokun Liu +4
We introduce The Benchmark of Linguistic Minimal Pairs (shortened to BLiMP), a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in Eng…
Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs
Alex Warstadt, Yu Cao, Ioana Grosu +13
Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical k…