Graph Alignment Topology as an Inductive Bias for Grounding Detection
The paper discusses a novel approach to grounding detection in large language models (LLMs) by utilizing graph alignment topology. This method aims to improve factual correctness in LLM outputs, particularly in critical domains like clinical decision support. By employing a graph neural network to model alignment structures, the authors achieve state-of-the-art results on various datasets.
- ▪Large Language Models are optimized for plausible continuations rather than factual correctness.
- ▪Existing hallucination detection methods do not directly learn from alignment topology.
- ▪The authors construct aligned bipartite graphs and use a graph neural network to enhance grounding detection.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22963 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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Computer Science > Computation and Language arXiv:2605.22963 (cs) [Submitted on 21 May 2026] Title:Graph Alignment Topology as an Inductive Bias for Grounding Detection Authors:Paul Landes, Pranav Herur, Adam Cross, Jimeng Sun View a PDF of the paper titled Graph Alignment Topology as an Inductive Bias for Grounding Detection, by Paul Landes and 3 other authors View PDF 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.
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