ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering
This work addresses the need for more reliable and explainable AI systems in domain-specific applications, representing an incremental advancement in neurosymbolic methods.
The paper tackled the problem of improving robustness and reliability in LLMs for question-answering by proposing a neurosymbolic framework that integrates formal logic for context gathering and validation, resulting in enhanced explainability and factual accuracy.
Neurosymbolic approaches can add robustness to opaque neural systems by incorporating explainable symbolic representations. However, previous approaches have not used formal logic to contextualize queries to and validate outputs of large language models (LLMs). We propose \systemname{}, a novel neurosymbolic framework, to improve the robustness and reliability of LLMs in question-answering tasks. We provide \systemname{} with a domain-specific knowledge base, a logical reasoning system, and an integration to an existing LLM. This framework has two capabilities (1) context gathering: generating explainable and relevant context for a given query, and (2) validation: confirming and validating the factual accuracy of a statement in accordance with a knowledge base (KB). Our work opens a new area of neurosymbolic generative AI text validation and user personalization.