4 citations · 10 across the 9 of their papers we have counts for
5 papers · 1 filter
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
Yuwei Zhao, Ziyang Luo, Yuchen Tian +4
Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. However, these…
Decompose and Compare Consistency: Measuring VLMs' Answer Reliability via Task-Decomposition Consistency Comparison
Qian Yang, Weixiang Yan, Aishwarya Agrawal
Despite tremendous advancements, current state-of-the-art Vision-Language Models (VLMs) are still far from perfect. They tend to hallucinate and may generate biased responses. In s…
An Approach to Detect Abnormal Submissions for CodeWorkout Dataset
Alex Hicks, Yang Shi, Arun-Balajiee Lekshmi-Narayanan +2
Students interactions while solving problems in learning environments (i.e. log data) are often used to support students learning. For example, researchers use log data to develop…
ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World
Weixiang Yan, Haitian Liu, Tengxiao Wu +9
LLMs have achieved significant performance progress in various NLP applications. However, LLMs still struggle to meet the strict requirements for accuracy and reliability in the me…
CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification
Yuchen Tian, Weixiang Yan, Qian Yang +6
Large Language Models (LLMs) have made significant progress in code generation, offering developers groundbreaking automated programming support. However, LLMs often generate code…