CoEvoT：用于图语言模型推理的协同进化思维链提示
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
另 4 家 · 查看机构详情
- Sun Yat-Sen University（中山大学）
- The Chinese University of Hong Kong（香港中文大学）
- Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
- National University of Singapore（新加坡国立大学）
- University of Electronic Science and Technology of China（电子科技大学）
- Beijing University of Posts and Telecommunieations（北京邮电大学）
- Singapore Management University（新加坡管理大学）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
研究分布转移下的图学习问题，提出CoEvoT框架，通过文本到图令牌重写与图到文本推理指导的闭环协同进化，实现逐步的、状态感知的证据细化，在八个数据集实验中性能优于现有基准模型。
中文摘要 AI 辅助
分布转移下的图学习面临持续挑战，模型需在有限或无监督下适应新图。近期图语言模型方法通过将图线性化为提示并使用大语言模型作为预测器来实现高效标签预测，还可采用思维链提示利用大语言模型的多步推理能力。然而，现有基于思维链的图语言模型方法在固定图令牌条件下生成中间思维，限制了结构线索的逐步细化。本文提出CoEvoT，一种简单而有效的用于图语言模型推理的协同进化思维链提示框架。CoEvoT在闭环中结合文本到图令牌重写和图到文本推理指导：每个中间文本思维通过轻量级条件网络更新图令牌证据状态，更新后的令牌反馈到下一步指令以指导后续大语言模型推理。这实现了逐步的、状态感知的证据细化，而非基于固定图快照进行推理。在八个数据集上的大量实验表明，CoEvoT始终优于现有基准模型。
英文摘要
Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision. Recent graph--LLM approaches move toward label-efficient prediction by linearizing graphs into prompts and using large language models (LLMs) as predictors, and can adopt Chain-of-Thought (CoT) prompting to exploit LLM's multi-step reasoning capability. However, existing CoT-based graph--LLM methods generate intermediate thoughts while conditioning on fixed graph tokens, limiting step-wise refinement of structural cues. In this paper, we propose CoEvoT, a simple yet effective co-evolving CoT prompting framework for graph--LLM reasoning. CoEvoT couples text-to-graph token rewriting and graph-to-text reasoning guidance in a closed loop: each intermediate textual thought is used to update the graph token evidence state via a lightweight condition network, and the updated tokens are fed back into the next-step instruction to guide subsequent LLM reasoning. This enables step-wise, state-aware evidence refinement, rather than reasoning over a fixed graph snapshot. Extensive experiments on eight datasets demonstrate that CoEvoT consistently outperforms state-of-the-art baselines.
