3 papers
cs.CL2026
D-SCoRE: Document-Centric Segmentation and CoT Reasoning with Structured Export for QA-CoT Data Generation
Weibo Zhou, Lingbo Li, Shangsong Liang
The scarcity and high cost of high-quality domain-specific question-answering (QA) datasets limit supervised fine-tuning of large language models (LLMs). We introduce $\textbf{D-SC…
cs.CL2025
LETToT: Label-Free Evaluation of Large Language Models On Tourism Using Expert Tree-of-Thought
Ruiyan Qi, Congding Wen, Weibo Zhou +3
Evaluating large language models (LLMs) in specific domain like tourism remains challenging due to the prohibitive cost of annotated benchmarks and persistent issues like hallucina…
cs.CL2025
TAPO: Task-Referenced Adaptation for Prompt Optimization
Wenxin Luo, Weirui Wang, Xiaopeng Li +3
Prompt engineering can significantly improve the performance of large language models (LLMs), with automated prompt optimization (APO) gaining significant attention due to the time…