Papers by Zhangchen Zhou

2 papers
Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism (2025.findings-emnlp)

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Challenge: Large language models struggle with complex reasoning tasks, such as mathematical problem-solving.
Approach: They constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning.
Outcome: The proposed algorithm improves on 7 multi-step reasoning datasets, while introducing only 132 trainable parameters.
KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding (2025.findings-acl)

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Challenge: Existing code-focused resources typically fail to ensure either the breadth of coverage or verifiable correctness.
Approach: They propose a synthetic dataset that provides high-quality, verifiable training data for Large Language Models for coding.
Outcome: The proposed dataset surpasses Qwen2.5-Coder-32B-Instruct and DeepSeek-R1-Distill-Llama-70B in performance on coding benchmarks.

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