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Yongqiang Lyu

2 accepted papers

2026

On the Evaluation of Capability Estimation Methods for Large Language Models

AAAI 2026technical

The emergence of large language models (LLMs) marks a transformative era in artificial intelligence~(AI). However, systematically evaluating the capability of LLMs is challenging due to the necessity of a large number of labeled test data. To tackle this problem, in the conventional AI field, AutoEv

Cited by 0SourcePDFScholar
2026

ProCURE: Addressing the Programming Concept Understanding Gap for Code Generation in LLMs via Concept-Aware Consistency Learning

IJCAI 2026

Although Large Language Models (LLMs) excel at code generation, recent research reveals that they exhibit an insufficient grasp of core programming concepts, such as data flow and control flow. This limitation undermines their robustness when encountering variations in these concepts in practice; ho

Cited by 0Scholar