Mitigating Hallucinations in Large Language Models via Semantic Enrichment of Prompts: Insights from B io BERT and Ontological Integration (2024)

Abstract: The advent of Large Language Models (LLMs) has been transformative for natural language processing, yet their tendency to produce “hallucinations”—outputs that are factually incorrect or entirely fabricated— remains a significant hurdle. This paper introduces a proactive methodology for reducing hallucinations by strategically enriching LLM prompts. This involves identifying key entities and contextual cues from varied domains and integrating this information into the LLM prompts to guide the model towards more accurate and relevant responses. Leveraging examples from BioBERT for biomedical entity recognition and ChEBI for chemical ontology, we illustrate a broader approach that encompasses semantic prompt enrichment as a versatile tool for enhancing LLM output accuracy. By examining the potential of semantic and ontological enrichment in diverse contexts, we aim to present a scalable strategy for improving the reliability of AI-generated content, thereby contributing to the ongoing efforts to refine LLMs for a wide range of applications.

## openai api | openai proceedings of clib 2024 3.2.3 retrieving ontological data from chebi: once entities are identified, we query the chebi database to fetch ontological information related to these entities. such information provides a layer of semantic depth to the prompt, grounding the llm's output in factual accuracy.