@inproceedings{chen-2025-llmsr,
    title = "{LLMSR}@{XLLM}25: {SWRV}: Empowering Self-Verification of Small Language Models through Step-wise Reasoning and Verification",
    author = "Chen, Danchun",
    editor = "Fei, Hao  and
      Tu, Kewei  and
      Zhang, Yuhui  and
      Hu, Xiang  and
      Han, Wenjuan  and
      Jia, Zixia  and
      Zheng, Zilong  and
      Cao, Yixin  and
      Zhang, Meishan  and
      Lu, Wei  and
      Siddharth, N.  and
      {\O}vrelid, Lilja  and
      Xue, Nianwen  and
      Zhang, Yue",
    booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.xllm-1.29/",
    pages = "322--335",
    ISBN = "979-8-89176-286-2",
    abstract = "Large language models (LLMs) have shown impressive reasoning capabilities through \textit{Chain-of-Thought} (CoT). However, the reasoning processes remain inexplicable and uncontrollable. In this paper, we tackle the task hosted by (CITATION) by introducing a \textit{ \textbf{S}tep-\textbf{W}ise \textbf{R}easoning and \textbf{V}erification} (SWRV) framework, a two-stage \textit{Parser{--}Verifier} one, that decomposes generated reasoning process into discrete inference steps and rigorously validates each one. First, our \textit{Parser} extracts problem constraints and the sequence of reasoning steps from the LLM{'}s reasoning process. Then, our \textit{Verifier} prompts itself or leverages a deterministic symbolic solver to formally check the logical correctness of every step. To ensure robust parsing, we also fine{-}tune a compact LM on a small, high{-}quality annotation set produced by a more powerful LLM. Experiments on the dataset (CITATION) demonstrate significant gains over baseline approaches, illustrating the effectiveness of our method for step{-}wise analysis of LLM chain-of-thought reasoning. The code is publicly available at https://github.com/Teganone/XLLM{\_}LLMSRhttps://github.com/Teganone/XLLM{\_}LLMSR."
}