Exploring the Synergy of Grammar-Aware Prompt Engineering and Formal Methods for Mitigating Hallucinations in LLMs

Recent advancements in Artificial Intelligence (AI), particularly in the advanced machine learning for the Natural Language Processing (NLP) paradigm, have led to the development of powerful Large Language Models (LLMs) capable of impressive feats in tasks like translation, text summarisation, text generation and code generation. However, a critical challenge hindering their real-world deployment is their susceptibility to hallucinations, where they generate plausible looking but factually incorrect outputs. These limitations come with adverse effects, such as the propagation of misinformation and reducing user trustworthiness in the related technologies, even when they possess transformative potential in various sectors. This study aims to enhance the performance of LLMs by presenting a new strategy that combines grammar-aware prompt engineering (GAPE) and formal methods (FMs) to leverage their synergy in the LLM process logic. We argue that by combining linguistic principles using GAPE and constructing a basis of formal structures using FMs, we could improve the LLM's ability to analyse language, decrease ambiguity in prompts, improve consistency in output, and eventually, greatly diminish LLM hallucinations. To do this, we propose a collaboration between linguists and AI experts while also providing specialised training for LLMs that emphasises linguistic precision. Additionally, we suggest implementing iterative design and development procedures for LLMs that use GAPE and FM principles to continuously enhance the performance of LLMs. By following these techniques, we may create a future in which LLMs are more trustworthy for a wide range of users and use cases with reliable LLM technologies and efficient advancements in practical situations