@inproceedings{vukovic-etal-2026-leveraging,
title = "Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection",
author = "Vukovic, Renato and
Lin, Hsien-chin and
van Niekerk, Carel and
Ruppik, Benjamin Matthias and
Heck, Michael and
Feng, Shutong and
Lubis, Nurul and
Gasic, Milica",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.67/",
pages = "923--936",
ISBN = "979-8-89176-478-1",
abstract = "Hallucination{---}where a language model generates outputs that are factually incorrect or unsupported by the source{---}is a major challenge for both prompted and fine-tuned language models. Detecting hallucinations is difficult due to the opaque reasoning processes of LLMs, which often provide little insight into why a model{'}s output may be inaccurate.\par In this work, we investigate whether an LLM can use an alternative, low level, symbolic competence such as SQL for unsupervised hallucination detection in some high level task. For this, we make an LLM build an SQL database from reference documents. This SQL database is then used for reasoning over the reference and the sampled response in a hallucination detection pipeline that is grounded in the database, thereby providing a neurosymbolic checkup.\par On RAGTruth and DiaHalu hallucination detection datasets, we find that our approach improves on direct prediction and competes with state-of-the-art hallucination detection methods, while not requiring domain-specific fine-tuning. Instead it relies on a low-level general competence already present in LLMs. This warrants further investigation of low-level LLM competences in neurosymbolic approaches."
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<abstract>Hallucination—where a language model generates outputs that are factually incorrect or unsupported by the source—is a major challenge for both prompted and fine-tuned language models. Detecting hallucinations is difficult due to the opaque reasoning processes of LLMs, which often provide little insight into why a model’s output may be inaccurate.\par In this work, we investigate whether an LLM can use an alternative, low level, symbolic competence such as SQL for unsupervised hallucination detection in some high level task. For this, we make an LLM build an SQL database from reference documents. This SQL database is then used for reasoning over the reference and the sampled response in a hallucination detection pipeline that is grounded in the database, thereby providing a neurosymbolic checkup.\par On RAGTruth and DiaHalu hallucination detection datasets, we find that our approach improves on direct prediction and competes with state-of-the-art hallucination detection methods, while not requiring domain-specific fine-tuning. Instead it relies on a low-level general competence already present in LLMs. This warrants further investigation of low-level LLM competences in neurosymbolic approaches.</abstract>
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%0 Conference Proceedings
%T Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection
%A Vukovic, Renato
%A Lin, Hsien-chin
%A van Niekerk, Carel
%A Ruppik, Benjamin Matthias
%A Heck, Michael
%A Feng, Shutong
%A Lubis, Nurul
%A Gasic, Milica
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F vukovic-etal-2026-leveraging
%X Hallucination—where a language model generates outputs that are factually incorrect or unsupported by the source—is a major challenge for both prompted and fine-tuned language models. Detecting hallucinations is difficult due to the opaque reasoning processes of LLMs, which often provide little insight into why a model’s output may be inaccurate.\par In this work, we investigate whether an LLM can use an alternative, low level, symbolic competence such as SQL for unsupervised hallucination detection in some high level task. For this, we make an LLM build an SQL database from reference documents. This SQL database is then used for reasoning over the reference and the sampled response in a hallucination detection pipeline that is grounded in the database, thereby providing a neurosymbolic checkup.\par On RAGTruth and DiaHalu hallucination detection datasets, we find that our approach improves on direct prediction and competes with state-of-the-art hallucination detection methods, while not requiring domain-specific fine-tuning. Instead it relies on a low-level general competence already present in LLMs. This warrants further investigation of low-level LLM competences in neurosymbolic approaches.
%U https://aclanthology.org/2026.groundlm-1.67/
%P 923-936
Markdown (Informal)
[Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection](https://aclanthology.org/2026.groundlm-1.67/) (Vukovic et al., GroundLM 2026)
ACL
- Renato Vukovic, Hsien-chin Lin, Carel van Niekerk, Benjamin Matthias Ruppik, Michael Heck, Shutong Feng, Nurul Lubis, and Milica Gasic. 2026. Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection. In Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026), pages 923–936, Budapest, Hungary. Association for Computational Linguistics.