Grounding the Ungrounded: A Spectral-Graph Framework for Quantifying Hallucinations in Multimodal LLMs
Hallucinations in LLMs--especially in multimodal settings--undermine reliability. We present a rigorous information-geometric framework, grounded in diffusion dynamics, to quantify hallucinations in MLLMs where model outputs are embedded via spectral decompositions of multimodal graph Laplacians, and their gaps to a truth manifold define a semantic distortion metric. We derive Courant-Fischer bounds on a temperature-dependent hallucination profile and use RKHS eigenmodes to obtain modality-aware, interpretable measures that track evolution over prompts and time. This reframes hallucination as quantifiable and bounded, providing a principled basis for evaluation and mitigation.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia
Large language models (LLMs) have an impressive ability to draw on novel information supplied in their context. Yet the mechanisms underlying this contextual grounding remain unknown, especially in situations where conte…
counterfactualLanguage ModelingLanguage ModellingRetrieval+1Paladin-mini: A Compact and Efficient Grounding Model Excelling in Real-World Scenarios
This paper introduces two significant contributions to address the issue of grounding claims in a given context. Grounding means that given a context (document) and a claim, there's at least one supportive evidence for t…
Neural Compositional Denotational Semantics for Question Answering
Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approa…
Question AnsweringSemantic ParsingSentenceClassical Planning as QBF without Grounding (extended version)
Most classical planners use grounding as a preprocessing step, reducing planning to propositional logic. However, grounding comes with a severe cost in memory, resulting in large encodings for SAT/QBF based planners. Des…
AEGIS: From Clues to Verdicts -- Graph-Guided Deep Vulnerability Reasoning via Dialectics and Meta-Auditing
Large Language Models (LLMs) are increasingly adopted for vulnerability detection, yet their reasoning remains fundamentally unsound. We identify a root cause shared by both major mitigation paradigms (agent-based debate…
Vulnerability Detection