Text-Graph Encoders and Retrieval-Augmented Generation
Abstract
Modern information-seeking systems increasingly rely on Large Language Models (LLMs) paired with external knowledge sources to generate accurate, context-aware responses. However, current Retrieval-Augmented Generation (RAG) pipelines face limitations in conversational question answering, including limited retrieval coverage and high reader (generator) latency, which affect answer quality and system efficiency. Evaluating responses grounded in retrieved evidence also presents challenges. Reward Models (RMs), typically trained on general preference data, are not equipped to distinguish between plausible responses based on parametric knowledge alone and those grounded in non-parametric evidence retrieved at inference time, as their training lacks retrieval-aware supervision and evidence-conditioned evaluation signals. Although the shift from parametric to non-parametric knowledge has enabled models to access more up-to-date and domain-specific information, most systems continue to rely solely on unstructured (text) inputs. This unstructured evidence often contains implicit, incomplete, or scattered information, making it difficult for models to piece together accurate responses. A promising direction is to incorporate structured non-parametric knowledge, such as knowledge graphs, which make relational information explicit and easier to leverage during inference. This requires architectures capable of jointly encoding unstructured and structured sources in a unified and…
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