6 papers
DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education
Lingyue Fu, Haowei Yuan, Datong Chen +5
In programming education, Debugging and Teaching (DT) task is a common scenario where students receive assistance in correcting their erroneous code. The task involves multiple inp…
CATArena: Evaluating Evolutionary Capabilities of Code Agents via Iterative Tournaments
Lingyue Fu, Xin Ding, Linyue Pan +9
Current evaluation for Large Language Model (LLM) code agents predominantly focus on generating functional code in single-turn scenarios, which fails to evaluate the agent's capabi…
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks
Lingyue Fu, Hao Guan, Bolun Zhang +10
The evaluation of Large Language Models (LLMs) for software engineering has shifted towards complex, repository-level tasks. However, existing benchmarks predominantly rely on coar…
LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis
Weiming Zhang, Lingyue Fu, Qingyao Li +7
Cognitive diagnosis (CD) plays a crucial role in intelligent education, evaluating students' comprehension of knowledge concepts based on their test histories. However, current CD…
AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing
Lingyue Fu, Ting Long, Jianghao Lin +6
Knowledge Tracing (KT) monitors students' knowledge states and simulates their responses to question sequences. Existing KT models typically follow a single-step training paradigm,…
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction
Xinmeng Hou, Lingyue Fu, Chenhao Meng +3
Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opin…