132 citations · 202 across the 6 of their papers we have counts for
6 papers
Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao +2
Large language models (LLMs) can produce erroneous responses that sound fluent and convincing, raising the risk that users will rely on these responses as if they were correct. Mit…
ECBD: Evidence-Centered Benchmark Design for NLP
Yu Lu Liu, Su Lin Blodgett, Jackie Chi Kit Cheung +3
Benchmarking is seen as critical to assessing progress in NLP. However, creating a benchmark involves many design decisions (e.g., which datasets to include, which metrics to use)…
"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu +2
Widely deployed large language models (LLMs) can produce convincing yet incorrect outputs, potentially misleading users who may rely on them as if they were correct. To reduce such…
Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking
Nikhil Sharma, Q. Vera Liao, Ziang Xiao
Large language models (LLMs) powered conversational search systems have already been used by hundreds of millions of people, and are believed to bring many benefits over convention…
Canvil: Designerly Adaptation for LLM-Powered User Experiences
K. J. Kevin Feng, Q. Vera Liao, Ziang Xiao +3
Advancements in large language models (LLMs) are sparking a proliferation of LLM-powered user experiences (UX). In product teams, designers often craft UX to meet user needs, but i…
Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions
Helena Vasconcelos, Gagan Bansal, Adam Fourney +2
Large-scale generative models enabled the development of AI-powered code completion tools to assist programmers in writing code. However, much like other AI-powered tools, AI-power…