15 citations · 43 across the 11 of their papers we have counts for
12 papers
SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades
Man Ho Lam, Chaozheng Wang, Hange Liu +5
Coding agents powered by large language models are increasingly expected to perform realistic software maintenance tasks beyond isolated issue resolution. Existing benchmarks have…
EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection
Wenxin Tang, Jingyu Xiao, Yanpei Gong +6
Automated academic poster generation aims to distill lengthy research papers into concise, visually coherent presentations. Existing Multimodal Large Language Models (MLLMs) based…
ComUICoder: Component-based Reusable UI Code Generation for Complex Websites via Semantic Segmentation and Element-wise Feedback
Jingyu Xiao, Jiantong Qin, Shuoqi Li +5
Multimodal Large Language Models (MLLMs) have demonstrated strong performance on the UI-to-code task, which aims to generate UI code from design mock-ups. However, when applied to…
TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework
Shuzheng Gao, Eric John Li, Man Ho Lam +5
Large foundation models are fundamentally transforming the software engineering landscape, demonstrating exceptional capabilities across diverse tasks such as code generation, debu…
JSProtect: A Scalable Obfuscation Framework for Mini-Games in WeChat
Zhihao Li, Chaozheng Wang, Zongjie Li +12
The WeChat mini-game ecosystem faces rampant intellectual property theft to other platforms via secondary development, yet existing JavaScript obfuscation tools are ill-equipped fo…
The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies
Jiaxu Zhou, Jen-tse Huang, Xuhui Zhou +5
Large language models (LLMs) are increasingly used to simulate human collective behavior, yet claims that such simulations are human-like remain largely untested. We conducted a sy…
DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Jingyu Xiao, Ming Wang, Man Ho Lam +4
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in automated front-end engineering, e.g., generating UI code from visual designs. However, existi…
CodeCrash: Exposing LLM Fragility to Misleading Natural Language in Code Reasoning
Man Ho Lam, Chaozheng Wang, Jen-tse Huang +1
Large Language Models (LLMs) have recently demonstrated strong capabilities in code-related tasks, but their robustness in code reasoning under perturbations remains underexplored.…
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…
Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench
Jen-tse Huang, Wenxuan Wang, Eric John Li +6
Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical med…
Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench
Jen-tse Huang, Man Ho Lam, Eric John Li +5
Evaluating Large Language Models' (LLMs) anthropomorphic capabilities has become increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psych…
Revisiting the Reliability of Psychological Scales on Large Language Models
Jen-tse Huang, Wenxiang Jiao, Man Ho Lam +3
Recent research has focused on examining Large Language Models' (LLMs) characteristics from a psychological standpoint, acknowledging the necessity of understanding their behaviora…