GRIL : Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025)

Abstract: Retrieval-Augmented Generation (RAG) has significantly mitigated the hallucinations of Large Language Models (LLMs) by grounding the generation with external knowledge. Recent extensions of RAG to graph-based retrieval offer a promising direction, leveraging the structural knowledge for multi-hop reasoning. However, existing graph RAG typically decouples retrieval and reasoning processes, which prevents the retriever from adapting to the reasoning needs of the LLM. They also struggle with scalability when performing multi-hop expansion over large-scale graphs, or depend heavily on annotated ground-truth entities, which are often unavailable in open-domain settings. To address these challenges, we propose a novel graph retriever trained end-to-end with LLM, which features an attention-based growing and pruning mechanism, adaptively navigating multi-hop relevant entities while filtering out noise. Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together, thereby enhancing its reasoning capability and facilitating interactive joint training of the graph retriever and the LLM reasoner. Experimental results across three QA benchmarks show that our approach consistently achieves state-of-the-art performance, validating the strength of joint graph–LLM optimization for complex reasoning tasks. Notably, our framework eliminates the need for predefined ground-truth entities by directly optimizing the retriever using LLM logits as implicit feedback, making it especially effective in open-domain settings.

## introduction large language models (llms) have shown remarkable abilities in natural language processing tasks @xcite @xcite . despite their success, llms often suffer from hallucinations, generating outputs that may be factually incorrect, particularly in scenarios requiring domain-intensive knowledge. to mitigate hallucinations and improve domain-specific performance, recent approaches have explored the retrieval-augmented generation (rag) framework, which enhances llms by retrieving external knowledge @xcite @xcite and has proven especially beneficial in tasks such as knowledge graph question answering (kgqa) @xcite @xcite . unlike traditional knowledge bases such as documents and textbooks, knowledge graphs (kgs) provide cleaner and well-structured relational knowledge, offering a more precise and efficient base for navigating complex reasoning paths and reducing hallucinations compared to unstructured data. consequently, recent efforts have extended rag by incorporating graph retrieval, where knowledge graphs-structured and domain-specific knowledge bases-are used to guide retrieval and reasoning. existing approaches use llms as a retriever for kgqa tasks (abbreviated as llm-as-retriever) @xcite @xcite , extracting relevant facts or relation paths from the kgs based on llms' internal knowledge. however, such llm-as-retriever approaches are typically ill-equipped to fully exploit the intricate structures within kgs, such as multihop logical relations, and require multiple calls to process different parts of the graph, which introduces scalability issues when dealing with largescale kgs. there have also been attempts using a graph neural network (gnn) during retrieval (abbreviated as gnn-as-retriever) @xcite @xcite . however, one limitation is that most existing approaches train the retrieval and reasoning components separately, resulting in a disjoint optimization process where the retriever focuses solely on relevant facts without aligning with the reasoning needs of the llm. moreover, many rely on ground truth retrieval paths for retriever training, limiting their applicability to open-domain scenarios where the ground truth entities are not available. (3) llm (gnn)-as-retriever approaches rely on the rag framework to reduce the hallucination and improve the accuracy of llms' output. different from previous approaches, we utilize the llm reasoner to supervise the gnn retriever, improving the retrieval quality and reasoning accuracy. to address these limitations, we propose gril, a novel framework that enables graph retrieval-integrated learning with llm in an end-to-end manner. as illustrated in figure 1 , our approach extends beyond conventional retriever-augmented reasoning by introducing a reverse feedback loop from the llm to guide the retriever. this llm feedback is essential for the model's generalizability in opendomain scenarios, by providing a complementary supervision signal, thus eliminating its reliance on answer entities during retriever training. intuitively, the reverse feedback shifts retrieval from mere relevance to actual usefulness for llm reasoning. moreover, we introduce a novel graph retriever that iteratively grows and prunes the extracted knowledge subgraph, which allows the retrieval process to focus on the most relevant multi-hop entities while filtering out irrelevant information, improving retrieval efficiency and accuracy. the proposed integrated training ensures the graph retriever and llm reasoner are tightly coupled and jointly optimized, fostering better synergy between retrieval and reasoning and improving overall performance. our contributions are threefold. (1) we introduce a novel framework that combines an adaptive attention-based graph retriever with joint training alongside the llm reasoner, improving the accu-racy and relevance of knowledge retrieval. (2) experiments demonstrate performance improvement over competitive models, achieving state-of-theart results on kgqa benchmarks, and showing strong open-domain generalizability, where most baselines fail. (3) we address the inefficiency of multiple llm calls during the retrieval process and enable small bert-level language models to match or even outperform 7b llms when paired with our graph retriever, improving inference efficiency and reducing deployment costs significantly. ## preliminaries and background knowledge graph question answering (kgqa) approaches aim to predict the correct answer a, given a question q and a kg g that provides relevant reasoning information. to reduce hallucination, a graph retriever extracts a subgraph g s ⊆ g that is the most relevant and useful for answering the question. the target is to learn a model that optimizes the conditional probability: the overall framework of gril is shown in figure 2. in kgqa tasks, a given question q is typically associated with query entities (aka seed entities) in the knowledge graph g. these seed entities