MQPF: A MULTI-DIMENSIONAL QUALITY-AWARE PATH FUSION FRAMEWORK FOR QUESTION ANSWERING

In multi-hop question answering (MHQA) tasks, existing methods typically integrate multiple reasoning paths from knowledge graphs (KGs) and chains of thought (CoTs). Early KG-enhanced methods primarily focus on obtaining relevant knowledge but fail to consider the multi-dimensional quality of reasoning paths. Subsequent works filter paths but treat all retained paths as equally important without further differentiation. Although some recent works attempt to rank paths by quality, they only provide a relative order without quantifying the actual quality differences between paths. To address these limitations, we propose a Multi-dimensional Quality-aware Path Fusion (MQPF) framework. MQPF introduces a multi-dimensional evaluation mechanism that quantifies path quality from semantic, structural, and outcome-based dimensions. Based on the overall scores, MQPF first filters out low-quality paths to reduce noise and then assigns adaptive weights to the remaining paths according to their scores. This method effectively removes unreliable information and enhances the utilization of trustworthy information during reasoning. Experiments show that MQPF performs comparably to baselines on multiple datasets. Moreover, as a model-agnostic module, it can be used as a plug-and-play module to enhance the performance of existing multi-path reasoning methods.