7 citations · 13 across the 16 of their papers we have counts for
5 papers · 1 filter
When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Being
Harsh Kumar, Jasmine Chahal, Yinuo Zhao +4
Seeking advice is a core human behavior that the internet has reinvented twice: first through forums and Q&A communities that crowdsource public guidance, and now through large lan…
ChessQA: Evaluating Large Language Models for Chess Understanding
Qianfeng Wen, Zhenwei Tang, Ashton Anderson
Chess provides an ideal testbed for evaluating the reasoning, modeling, and abstraction capabilities of large language models (LLMs), as it has well-defined structure and objective…
SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models
Zhenwei Tang, Difan Jiao, Blair Yang +1
Evaluating whether vision-language models (VLMs) reason consistently across representations is challenging because modality comparisons are typically confounded by task differences…
Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess
Zhenwei Tang, Difan Jiao, Eric Xue +4
As humans seek to collaborate with, learn from, and better understand artificial intelligence systems, developing AIs that can accurately emulate individual decision-making becomes…
ChatBench: From Static Benchmarks to Human-AI Evaluation
Serina Chang, Ashton Anderson, Jake M. Hofman
With the rapid adoption of LLM-based chatbots, there is a pressing need to evaluate what humans and LLMs can achieve together. However, standard benchmarks, such as MMLU, measure L…