# LP-LM: No Hallucinations in Question Answering with Logic Programming

Source: [LP-LM: No Hallucinations in Question Answering with Logic Programming](<https://www.sota2.com/research/paper/68fa76ee1e209131cea70018>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:46:56.735Z

Authors: Katherine Wu, Yanhong A. Liu

Published: 2025-02-13T11:48:31.000Z

## Abstract

Large language models (LLMs) are able to generate human-like responses to user queries. However, LLMs exhibit inherent limitations, especially because they hallucinate. This paper introduces LP-LM, a system that grounds answers to questions in known facts contained in a knowledge base (KB), facilitated through semantic parsing in Prolog, and always produces answers that are reliable.   LP-LM generates a most probable constituency parse tree along with a corresponding Prolog term for an input question via Prolog definite clause grammar (DCG) parsing. The term is then executed against a KB of natural language sentences also represented as Prolog terms for question answering. By leveraging DCG and tabling, LP-LM runs in linear time in the size of input sentences for sufficiently many grammar rules. Performing experiments comparing LP-LM with current well-known LLMs in accuracy, we show that LLMs hallucinate on even simple questions, unlike LP-LM.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2502.09212>)

[PDF](<https://arxiv.org/pdf/2502.09212.pdf>)