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Arxiv
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102
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Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
- Large Language Models (LLMs) suffer from outdated knowledge and hallucinations, hindering their reliability.
- Retrieval-Augmented Generation helps ground LLMs with external knowledge, but current pipelines mostly use unstructured text, limiting interpretability and structured reasoning.
- Knowledge graphs offer a more structured and compact alternative to unstructured text, representing facts as relational triples.
- Recent studies have integrated knowledge graphs with LLMs for Knowledge Graph Question Answering (KGQA), often using a retrieve-then-reason paradigm.
- Graph-based retrievers in KGQA have shown strong empirical performance but struggle with generalization ability.
- A new framework called RAPL is proposed for efficient and effective graph retrieval in KGQA.
- RAPL addresses limitations through a two-stage labeling strategy, a model-agnostic graph transformation approach, and a path-based reasoning strategy.
- The two-stage labeling strategy combines heuristic signals with parametric models to provide causally grounded supervision.
- The model-agnostic graph transformation captures intra- and inter-triple interactions, enhancing representational capacity.
- The path-based reasoning strategy enables learning from rational knowledge injections and supports downstream reasoners with structured inputs.
- Empirically, RAPL outperforms state-of-the-art methods by 2.66%-20.34% and reduces the performance gap between different LLM-based reasoners as well as in cross-dataset settings.
- The framework highlights superior retrieval capability and generalizability in KGQA.
- Codes for RAPL are available at: https://github.com/tianyao-aka/RAPL.
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