Papers by Xiao-Ping Zhang
Z1: Efficient Test-time Scaling with Code (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, but this often entails longer contexts and numerous reasoning token costs. |
| Approach: | They propose an efficient test-time scaling method that trains LLMs on code-related reasoning trajectories and a novel Shifted Thinking Window to mitigate overthinking overhead. |
| Outcome: | The proposed method reduces overthinking overhead while maintaining performance. |
HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation Task (2025.findings-acl)
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| Challenge: | Existing benchmarks for code generation tasks are inadequate, but performance declines on self-invoking tasks. |
| Approach: | They propose a general recipe for generating more challenging versions of existing benchmarks . they propose to use instruction-tuned models to evaluate LLMs on self-invoking code generation tasks . |
| Outcome: | The proposed model improves on humanEval and MBPP but on self-invoking code generation tasks. |