@inproceedings{arakkal-remesh-etal-2026-detection,
title = "Detection of parametric or contextual knowledge: are the schemas similar across modalities?",
author = "Arakkal Remesh, Binesh and
Gerald, Thomas and
Guinaudeau, Camille and
Ghannay, Sahar and
Feillet, Eva",
editor = {Aziz, Wilker and
Berant, Jonathan and
Eikema, Bryan and
Ilia, Evgenia and
de Marneffe, Marie-Catherine and
Plank, Barbara and
Shelmanov, Artem and
Swayamdipta, Swabha and
Tiedemann, J{\"o}rg and
Vazhentsev, Artem and
V{\'a}zquez, Ra{\'u}l and
Zerva, Chrysoula},
booktitle = "The Proceedings of the Third Workshop on Uncertainty-Aware {NLP}",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.uncertainlp-main.13/",
pages = "219--234",
ISBN = "979-8-89176-486-6",
abstract = "In LLMs, the source of information used to produce an answer is necessary to explain the generation process.This work considers the case where the answer can be extracted from the question{'}s context ( contextual knowledge) or from the model weights (parametric knowledge). While methods have been developed to uncover the grounding sources of LLMs in question answering, these approaches mainly rely on the text modality, whereas most tasks involve multiple modalities. This study proposes to uncover LLMs' internal signals to determine whether they use parametric or contextual knowledge in a bimodal setting. To this end, we create a counterfactual dataset to evaluate whether the model relies on parametric or contextual knowledge on text and/or image-based questions. We leverage probing methods based on the internal attention and the different latent representations to determine the model{'}s source of information. We evaluate the performances of the probing methods, focusing on the different modalities. We show that models share common signals across the corpus and modalities. This study opens the way to develop controllable methods by uncovering common signals in LLMs to determine the knowledge used in generation."
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<abstract>In LLMs, the source of information used to produce an answer is necessary to explain the generation process.This work considers the case where the answer can be extracted from the question’s context ( contextual knowledge) or from the model weights (parametric knowledge). While methods have been developed to uncover the grounding sources of LLMs in question answering, these approaches mainly rely on the text modality, whereas most tasks involve multiple modalities. This study proposes to uncover LLMs’ internal signals to determine whether they use parametric or contextual knowledge in a bimodal setting. To this end, we create a counterfactual dataset to evaluate whether the model relies on parametric or contextual knowledge on text and/or image-based questions. We leverage probing methods based on the internal attention and the different latent representations to determine the model’s source of information. We evaluate the performances of the probing methods, focusing on the different modalities. We show that models share common signals across the corpus and modalities. This study opens the way to develop controllable methods by uncovering common signals in LLMs to determine the knowledge used in generation.</abstract>
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%0 Conference Proceedings
%T Detection of parametric or contextual knowledge: are the schemas similar across modalities?
%A Arakkal Remesh, Binesh
%A Gerald, Thomas
%A Guinaudeau, Camille
%A Ghannay, Sahar
%A Feillet, Eva
%Y Aziz, Wilker
%Y Berant, Jonathan
%Y Eikema, Bryan
%Y Ilia, Evgenia
%Y de Marneffe, Marie-Catherine
%Y Plank, Barbara
%Y Shelmanov, Artem
%Y Swayamdipta, Swabha
%Y Tiedemann, Jörg
%Y Vazhentsev, Artem
%Y Vázquez, Raúl
%Y Zerva, Chrysoula
%S The Proceedings of the Third Workshop on Uncertainty-Aware NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-486-6
%F arakkal-remesh-etal-2026-detection
%X In LLMs, the source of information used to produce an answer is necessary to explain the generation process.This work considers the case where the answer can be extracted from the question’s context ( contextual knowledge) or from the model weights (parametric knowledge). While methods have been developed to uncover the grounding sources of LLMs in question answering, these approaches mainly rely on the text modality, whereas most tasks involve multiple modalities. This study proposes to uncover LLMs’ internal signals to determine whether they use parametric or contextual knowledge in a bimodal setting. To this end, we create a counterfactual dataset to evaluate whether the model relies on parametric or contextual knowledge on text and/or image-based questions. We leverage probing methods based on the internal attention and the different latent representations to determine the model’s source of information. We evaluate the performances of the probing methods, focusing on the different modalities. We show that models share common signals across the corpus and modalities. This study opens the way to develop controllable methods by uncovering common signals in LLMs to determine the knowledge used in generation.
%U https://aclanthology.org/2026.uncertainlp-main.13/
%P 219-234
Markdown (Informal)
[Detection of parametric or contextual knowledge: are the schemas similar across modalities?](https://aclanthology.org/2026.uncertainlp-main.13/) (Arakkal Remesh et al., UncertaiNLP 2026)
ACL