Papers by Haokun Zhao
Explore the Reasoning Capability of LLMs in the Chess Testbed (2025.naacl-short)
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| Challenge: | a recent study shows that large language models struggle with long-term, complex reasoning tasks. |
| Approach: | They propose to integrate annotated strategy and tactic into large language models to improve reasoning capability. |
| Outcome: | The proposed model performs better than GPT, Claude, and Gemini models . it integrates annotated strategy and tactic into the model . |
CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but the complexity of emerging tasks and higher performance demands highlight the need for continuous improvement. |
| Approach: | They propose a method that refines evaluation results and characterizes model profiles at the knowledge component level. |
| Outcome: | The proposed method improves performance across multiple benchmarks and academic exams. |