@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Deductive Verification of LLM Generated SPARQL Queries
%A Rademaker, Alexandre
%A Lima, Guilherme
%A Fiorini, Sandro Rama
%A da Silva, Viviane Torres
%Y Sérasset, Gilles
%Y Oliveira, Hugo Gonçalo
%Y Oleskeviciene, Giedre Valunaite
%S Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F rademaker-etal-2024-deductive
%X 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.
%U https://aclanthology.org/2024.dlnld-1.4
%P 45-52
Markdown (Informal)
[Deductive Verification of LLM Generated SPARQL Queries](https://aclanthology.org/2024.dlnld-1.4) (Rademaker et al., DLnLD-WS 2024)
ACL
- Alexandre Rademaker, Guilherme Lima, Sandro Rama Fiorini, and Viviane Torres da Silva. 2024. Deductive Verification of LLM Generated SPARQL Queries. In Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024, pages 45–52, Torino, Italia. ELRA and ICCL.