1 citations · 2 across the 8 of their papers we have counts for
8 papers
MMTutorBench: The First Multimodal Benchmark for AI Math Tutoring
Tengchao Yang, Sichen Guo, Mengzhao Jia +4
Effective math tutoring requires not only solving problems but also diagnosing students' difficulties and guiding them step by step. While multimodal large language models (MLLMs)…
AutoRubric: Rubric-Based Generative Rewards for Faithful Multimodal Reasoning
Mengzhao Jia, Zhihan Zhang, Ignacio Cases +3
Multimodal large language models (MLLMs) have rapidly advanced from perception tasks to complex multi-step reasoning, yet reinforcement learning with verifiable rewards (RLVR) ofte…
Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
Zhenyu Wu, Qingkai Zeng, Zhihan Zhang +3
Best-of-N decoding methods instruct large language models (LLMs) to generate multiple solutions, score each using a scoring function, and select the highest scored as the final ans…
TOWER: Tree Organized Weighting for Evaluating Complex Instructions
Noah Ziems, Zhihan Zhang, Meng Jiang
Evaluating the ability of large language models (LLMs) to follow complex human-written instructions is essential for their deployment in real-world applications. While benchmarks l…
RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph
Siru Ouyang, Wenhao Yu, Kaixin Ma +6
Large Language Models (LLMs) excel in code generation yet struggle with modern AI software engineering tasks. Unlike traditional function-level or file-level coding tasks, AI softw…
MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems
Zifeng Zhu, Mengzhao Jia, Zhihan Zhang +2
Multimodal Large Language Models (MLLMs) have demonstrated impressive abilities across various tasks, including visual question answering and chart comprehension, yet existing benc…
Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks
Mengzhao Jia, Wenhao Yu, Kaixin Ma +6
Text-rich images, where text serves as the central visual element guiding the overall understanding, are prevalent in real-world applications, such as presentation slides, scanned…
Large Language Models Can Self-Correct with Key Condition Verification
Zhenyu Wu, Qingkai Zeng, Zhihan Zhang +3
Intrinsic self-correct was a method that instructed large language models (LLMs) to verify and correct their responses without external feedback. Unfortunately, the study concluded…