4 papers
Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions
Chen Xia, Zexi Kuang, Yuqing Hu
Large language model (LLM) agents offer a generative approach to simulating human behavior under conditions that may have few or no direct historical analogues, a common challenge…
TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction
Chengye Wang, Lin Fu, Zexi Kuang +1
Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientific publishing. We study page…
SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification
Chengye Wang, Yifei Shen, Zexi Kuang +2
We introduce SciVer, the first benchmark specifically designed to evaluate the ability of foundation models to verify claims within a multimodal scientific context. SciVer consists…
Can LLMs Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure
Zheyuan Yang, Zexi Kuang, Xue Xia +1
We introduce TestCase-Eval, a new benchmark for systematic evaluation of LLMs in test-case generation. TestCase-Eval includes 500 algorithm problems and 100,000 human-crafted solut…