Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection
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. 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.
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- PossiblePossibly related (embedding) · 57%Researchers develop cost-efficient method for detecting hallucinations in large language models - Tech Xplore →
- PossiblePossibly related (embedding) · 55%IEEE Rolls Out Large Language Models Virtual Training Course →
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Renato Vukovic →
“Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection”
- LinkedLinked via arxiv author · 85%Hsien-chin Lin →
“Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection”
- LinkedLinked via arxiv author · 85%Carel van Niekerk →
“Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection”
- LinkedLinked via arxiv author · 85%Benjamin Ruppik →
“Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection”