Graph Alignment Topology as an Inductive Bias for Grounding Detection
Quick Take
The paper introduces a method using graph alignment topology as an inductive bias for grounding detection in LLMs, achieving state-of-the-art performance on four datasets, surpassing models like GPT-4o. By employing aligned bipartite graphs and a graph neural network, the approach enhances factual correctness in critical applications such as clinical decision support.
Key Points
- Utilizes aligned bipartite graphs to model alignment structure.
- Employs a graph neural network for message passing.
- Achieves state-of-the-art results on diverse hallucination datasets.
- Surpasses foundational models like GPT-4o in performance.
- Addresses factual correctness in critical domains like clinical support.
Article Excerpt
From source RSS / original summaryarXiv:2605. 22963v1 Announce Type: new Abstract: Large Language Models (LLMs) are optimized to produce distributionally plausible continuations rather than to explicitly verify whether generated propositions are entailed by source documents. This inductive bias enables generalization, but it does not encode whether responses are grounded with respect to a reference. These issues limit the use of LLMs in domains where strict factual correctness is crucial, such as clinical decision support.
Existing hallucination detection approaches improve factuality through retrieval augmentation, self-consistency, or claim verification, but generally do not learn directly over alignment topology. To leverage alignment topology as an inductive bias, we construct aligned bipartite graphs between reference information and LLM outputs and train a graph neural network (GNN) to model alignment structure using message passing.
The method achieves state-of-the-art results on four diverse hallucination and question-answering datasets, outperforming all compared methods, including foundational LLMs such as GPT-4o.
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