2 citations · 7 across the 11 of their papers we have counts for
7 papers · 1 filter
WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation
Kuang-Da Wang, Zhao Wang, Yotaro Shimose +2
Witnessed by the recent advancements on leveraging LLM for coding and multimodal understanding, we present WebGen-V, a new benchmark and framework for instruction-to-HTML generatio…
Extending Automatic Machine Translation Evaluation to Book-Length Documents
Kuang-Da Wang, Shuoyang Ding, Chao-Han Huck Yang +4
Despite Large Language Models (LLMs) demonstrating superior translation performance and long-context capabilities, evaluation methodologies remain constrained to sentence-level ass…
Benchmarking Agentic Newswriting via Journalistic Workflows
Yen-Che Chien, Kuang-Da Wang, Wei-Yao Wang +1
Recent advances in autonomous digital agents from industry (e.g., Manus AI and Gemini's research mode) highlight their potential for structured tasks through autonomous decision-ma…
DDOT: A Derivative-directed Dual-decoder Ordinary Differential Equation Transformer for Dynamic System Modeling
Yang Chang, Kuang-Da Wang, Ping-Chun Hsieh +2
Uncovering the underlying ordinary differential equations (ODEs) that govern dynamic systems is crucial for advancing our understanding of complex phenomena. Traditional symbolic r…
Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression
Yung-Chien Wang, Kuang-Da Wang, Wei-Yao Wang +1
Tabular data serve as a fundamental and ubiquitous representation of structured information in numerous real-world applications, e.g., finance and urban planning. In the realm of t…
Template-Based Financial Report Generation in Agentic and Decomposed Information Retrieval
Yong-En Tian, Yu-Chien Tang, Kuang-Da Wang +2
Tailoring structured financial reports from companies' earnings releases is crucial for understanding financial performance and has been widely adopted in real-world analytics. How…
Test-Time Alignment for Large Language Models via Textual Model Predictive Control
Kuang-Da Wang, Teng-Ruei Chen, Yu Heng Hung +7
Aligning Large Language Models (LLMs) with human preferences through finetuning is resource-intensive, motivating lightweight alternatives at test time. We address test-time alignm…