@inproceedings{rademaker-etal-2024-deductive,
    title = "Deductive Verification of {LLM} Generated {SPARQL} Queries",
    author = "Rademaker, Alexandre  and
      Lima, Guilherme  and
      Fiorini, Sandro Rama  and
      da Silva, Viviane Torres",
    editor = "S{\'e}rasset, Gilles  and
      Oliveira, Hugo Gon{\c{c}}alo  and
      Oleskeviciene, Giedre Valunaite",
    booktitle = "Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.dlnld-1.4",
    pages = "45--52",
    abstract = "Considering the increasing applications of Large Language Models (LLMs) to many natural language tasks, this paper presents preliminary findings on developing a verification component for detecting hallucinations of an LLM that produces SPARQL queries from natural language questions. We suggest a logic-based deductive verification of the generated SPARQL query by checking if the original NL question{'}s deep semantic representation entails the SPARQL{'}s semantic representation.",
}

Considering the increasing applications of Large Language Models (LLMs) to many natural language tasks, this paper presents preliminary findings on developing a verification component for detecting hallucinations of an LLM that produces SPARQL queries from natural language questions. We suggest a logic-based deductive verification of the generated SPARQL query by checking if the original NL question{'}s deep semantic representation entails the SPARQL{'}s semantic representation.
Rademaker, Alex re Lima, Guilherme Fiorini, S ro Rama da Silva, Viviane Torres
Deductive Verification of LLM Generated SPARQL Queries