让大模型学会总结经验，连贯推理多跳知识图谱问题
TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering
- 用自然语言描述推理路径，保持逻辑连贯
- 提炼过往探索模式作先验，减少重复尝试
- 适合需要深度推理的问答系统研究者
多跳知识图谱问答需要在关系路径间进行连贯推理，但现有方法常孤立处理每一步，未能有效利用先前探索经验，导致推理碎片化和重复探索。为此，我们提出轨迹感知的上下文与探索先验融合框架TRACE，将动态演化的推理路径转化为自然语言叙事以维持语义连续性，同时将过往探索轨迹抽象为可复用的经验先验，捕捉常见探索模式。双反馈重排序机制进一步融合上下文叙事与探索先验，指导关系选择。在多个KGQA基准上的实验表明，TRACE持续优于现有最优基线。
原文摘要 · Abstract (English)
Multi-hop Knowledge Graph Question Answering (KGQA) requires coherent reasoning across relational paths, yet existing methods often treat each reasoning step independently and fail to effectively leverage experience from prior explorations, leading to fragmented reasoning and redundant exploration. To address these challenges, we propose Trajectoryaware Reasoning with Adaptive Context and Exploration priors (TRACE), an experiential framework that unifies LLM-driven contextual reasoning with exploration prior integration to enhance the coherence and robustness of multihop KGQA. Specifically, TRACE dynamically translates evolving reasoning paths into natural language narratives to maintain semantic continuity, while abstracting prior exploration trajectories into reusable experiential priors that capture recurring exploration patterns. A dualfeedback re-ranking mechanism further integrates contextual narratives with exploration priors to guide relation selection during reasoning. Extensive experiments on multiple KGQA benchmarks demonstrate that TRACE consistently outperforms state-of-the-art baselines.
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