Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents

Hallucinations in large language models (LLMs) refer to the phenomenon where the LLM generates an output that is plausible but factually incorrect or made-up. This can occur when the model’s training data lacks the necessary information or when the model attempts to generate coherent responses by making logical inferences beyond its actual knowledge. Hallucinations arise because of the inherent limitations of the language modeling approach, which aims to produce fluent and contextually appropriate text without necessarily ensuring factual accuracy.

Remediating hallucinations is crucial for production applications that use LLMs, particularly in domains where incorrect information can have serious consequences, such as healthcare, finance, or legal applications. Unchecked hallucinations can undermine the reliability and trustworthiness of the system, leading to potential harm or legal liabilities. Strategies to mitigate hallucinations can include rigorous fact-checking mechanisms, integrating external knowledge sources using Retrieval Augmented Generation (RAG), applying confidence thresholds, and implementing human oversight ...