30 citations · 39 across the 6 of their papers we have counts for
6 papers
PERSONAJUDGE: Simulating Individual Human Preference Judgments with Evaluator-Specific Demonstration Data
Zeyu He, Xuan Qi, Subramanian Chidambaram +4
Large language models increasingly serve as judges in AI evaluation, but current approaches rely on consensus preferences that ignore individual evaluator variation. We propose a n…
How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning
Zeyu He, Hannah Kim, Dan Zhang +1
In orchestrated multi-agent systems, humans often struggle to manage plans due to their complexity and limited transparency. Existing approaches rely on outcome-level supervision,…
Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absent
Zeyu He, Saniya Naphade, Ting-Hao 'Kenneth' Huang
Millions of users prompt large language models (LLMs) for various tasks, but how good are people at prompt engineering? Do users actually get closer to their desired outcome over m…
What Color Scheme is More Effective in Assisting Readers to Locate Information in a Color-Coded Article?
Ho Yin Ng, Zeyu He, Ting-Hao 'Kenneth' Huang
Color coding, a technique assigning specific colors to cluster information types, has proven advantages in aiding human cognitive activities, especially reading and comprehension.…
How Does Conversation Length Impact User's Satisfaction? A Case Study of Length-Controlled Conversations with LLM-Powered Chatbots
Shih-Hong Huang, Ya-Fang Lin, Zeyu He +2
Users can discuss a wide range of topics with large language models (LLMs), but they do not always prefer solving problems or getting information through lengthy conversations. Thi…
If in a Crowdsourced Data Annotation Pipeline, a GPT-4
Zeyu He, Chieh-Yang Huang, Chien-Kuang Cornelia Ding +2
Recent studies indicated GPT-4 outperforms online crowd workers in data labeling accuracy, notably workers from Amazon Mechanical Turk (MTurk). However, these studies were criticiz…