325 citations · 601 across the 37 of their papers we have counts for
8 papers · 1 filter
Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach
Yuxuan Wan, Chaozheng Wang, Yi Dong +4
Websites are critical in today's digital world, with over 1.11 billion currently active and approximately 252,000 new sites launched daily. Converting website layout design into fu…
Exploring Multi-Lingual Bias of Large Code Models in Code Generation
Chaozheng Wang, Zongjie Li, Cuiyun Gao +6
Code generation aims to synthesize code and fulfill functional requirements based on natural language (NL) specifications, which can greatly improve development efficiency. In the…
How Well Can LLMs Echo Us? Evaluating AI Chatbots' Role-Play Ability with ECHO
Man Tik Ng, Hui Tung Tse, Jen-tse Huang +3
The role-play ability of Large Language Models (LLMs) has emerged as a popular research direction. However, existing studies focus on imitating well-known public figures or fiction…
How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments
Jen-tse Huang, Eric John Li, Man Ho Lam +7
Decision-making is a complex process requiring diverse abilities, making it an excellent framework for evaluating Large Language Models (LLMs). Researchers have examined LLMs' deci…
A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models
Jie Liu, Wenxuan Wang, Yihang Su +8
The significant breakthroughs of Medical Multi-Modal Large Language Models (Med-MLLMs) renovate modern healthcare with robust information synthesis and medical decision support. Ho…
New Job, New Gender? Measuring the Social Bias in Image Generation Models
Wenxuan Wang, Haonan Bai, Jen-tse Huang +5
Image generation models can generate or edit images from a given text. Recent advancements in image generation technology, exemplified by DALL-E and Midjourney, have been groundbre…
Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models
Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling +7
Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone…
LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models
Yuxuan Wan, Wenxuan Wang, Yiliu Yang +5
We introduce LogicAsker, a novel approach for evaluating and enhancing the logical reasoning capabilities of large language models (LLMs) such as ChatGPT and GPT-4. Despite LLMs' p…