LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning

• URL: http://arxiv.org/abs/2505.12328v1
• Date: Sun, 18 May 2025 09:46:30 GMT
• Title: LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning
• Authors: Xinye Li, Mingqi Wan, Dianbo Sui,
• Abstract summary: We present Team asdfo123's submission to the LLMSR@XLLM25 shared task.<n>We evaluate large language models on producing fine-grained, controllable, and interpretable reasoning processes.<n>Our method ranks 5th overall, achieving macro F1 scores on par with substantially more complex and resource-consuming pipelines.
• Score: 6.700515856842664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Team asdfo123's submission to the LLMSR@XLLM25 shared task, which evaluates large language models on producing fine-grained, controllable, and interpretable reasoning processes. Systems must extract all problem conditions, decompose a chain of thought into statement-evidence pairs, and verify the logical validity of each pair. Leveraging only the off-the-shelf Meta-Llama-3-8B-Instruct, we craft a concise few-shot, multi-turn prompt that first enumerates all conditions and then guides the model to label, cite, and adjudicate every reasoning step. A lightweight post-processor based on regular expressions normalises spans and enforces the official JSON schema. Without fine-tuning, external retrieval, or ensembling, our method ranks 5th overall, achieving macro F1 scores on par with substantially more complex and resource-consuming pipelines. We conclude by analysing the strengths and limitations of our approach and outlining directions for future research in structural reasoning with LLMs. Our code is available at https://github.com/asdfo123/LLMSR-asdfo123.

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