21 citations · 29 across the 8 of their papers we have counts for
8 papers
Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation
Kehang Zhu, Nithum Thain, Vivian Tsai +2
As AI usage becomes more prevalent in social contexts, understanding agent-user interaction is critical to designing systems that imp rove both individual and group outcomes. We pr…
Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining
Crystal Qian, Kehang Zhu, John Horton +4
Markets increasingly accommodate large language models (LLMs) as autonomous decision-making agents. As this transition occurs, it becomes critical to evaluate how these agents beha…
The Evolution of LLM Adoption in Industry Data Curation Practices
Crystal Qian, Michael Xieyang Liu, Emily Reif +7
As large language models (LLMs) grow increasingly adept at processing unstructured text data, they offer new opportunities to enhance data curation workflows. This paper explores t…
Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration
Crystal Qian, James Wexler
Although recent developments in generative AI have greatly enhanced the capabilities of conversational agents such as Google's Gemini (formerly Bard) or OpenAI's ChatGPT, it's uncl…
ConstitutionalExperts: Training a Mixture of Principle-based Prompts
Savvas Petridis, Ben Wedin, Ann Yuan +2
Large language models (LLMs) are highly capable at a variety of tasks given the right prompt, but writing one is still a difficult and tedious process. In this work, we introduce C…
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
Emily Reif, Crystal Qian, James Wexler +1
Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data workers often rely on dataset summaries, especially…
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models
Minsuk Kahng, Ian Tenney, Mahima Pushkarna +7
Automatic side-by-side evaluation has emerged as a promising approach to evaluating the quality of responses from large language models (LLMs). However, analyzing the results from…
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles
Savvas Petridis, Ben Wedin, James Wexler +5
Large language model (LLM) prompting is a promising new approach for users to create and customize their own chatbots. However, current methods for steering a chatbot's outputs, su…