Towards increased truthfulness in LLM applications

Application-oriented methods from current research

Abstract

This article explores methods to enhance the truthfulness of Retrieval Augmented Generation (RAG) application outputs, focusing on mitigating issues like hallucinations and reliance on pre-trained knowledge. I identify the causes of untruthful results, evaluate methods for assessing truthfulness, and propose solutions to improve accuracy. The study emphasizes the importance of groundedness and completeness in RAG outputs, recommending fine-tuning Large Language Models (LLMs) and employing element-aware summarization to ensure factual accuracy. Additionally, it discusses the …

llm llm applications retrieval-augmented retrieval-generation