54 citations · 72 across the 9 of their papers we have counts for
10 papers
Learnable Assessment Skills for LLM-based Automated Scoring: Rubric Construction via Iterative Optimization
Yun Wang, Xin Xia, Xuansheng Wu +2
LLM-based automated scoring approaches near-human performance, but scaling to new tasks remains bottlenecked by the per-item human configuration of upstream stages such as rubric c…
Using Learning Progressions to Guide AI Feedback for Science Learning
Xin Xia, Nejla Yuruk, Yun Wang +1
Generative artificial intelligence (AI) offers scalable support for formative feedback, yet most AI-generated feedback relies on task-specific rubrics authored by domain experts. W…
Heterogeneous Agent Collaborative Reinforcement Learning
Zhixia Zhang, Zixuan Huang, Gongxun Li +10
We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem that addresses the inefficiencies…
Benchmarking Code LLM-as-a-Judge via Constrained Perturbation and Calibration
Ruiqi Wang, Xinchen Wang, Cuiyun Gao +3
Code from LLM-assisted development may contain defects like API hallucinations, motivating the development of code evaluation metrics to assess such code before deployment. As conv…
An Empirical Study of Knowledge Distillation for Code Understanding Tasks
Ruiqi Wang, Zezhou Yang, Cuiyun Gao +2
Pre-trained language models (PLMs) have emerged as powerful tools for code understanding. However, deploying these PLMs in large-scale applications faces practical challenges due t…
PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models
Shi Qiu, Shaoyang Guo, Zhuo-Yang Song +51
Current benchmarks for evaluating the reasoning capabilities of Large Language Models (LLMs) face significant limitations: task oversimplification, data contamination, and flawed e…
Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering
Ruiqi Wang, Jiyu Guo, Cuiyun Gao +3
Recently, large language models (LLMs) have been deployed to tackle various software engineering (SE) tasks like code generation, significantly advancing the automation of SE tasks…
The Current Challenges of Software Engineering in the Era of Large Language Models
Cuiyun Gao, Xing Hu, Shan Gao +2
With the advent of large language models (LLMs) in the artificial intelligence (AI) area, the field of software engineering (SE) has also witnessed a paradigm shift. These models,…
ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code
Jia Feng, Jiachen Liu, Cuiyun Gao +4
In recent years, the application of large language models (LLMs) to code-related tasks has gained significant attention. However, existing evaluation benchmarks often focus on limi…
Multi-Agent Causal Discovery Using Large Language Models
Hao Duong Le, Xin Xia, Haijie Xu +1
Causal discovery aims to identify causal relationships between variables and is a fundamental problem across the sciences. Traditional statistical causal discovery (SCD) methods re…