Large Language Models (LLMs) rely on statistical patterns for word prediction, enabling them to perform various tasks like summarization and content generation. However, these models don't truly understand context and can produce false or inconsistent outputs, a phenomenon known as hallucination. While retrieval augmented generation (RAG) methods can help reduce hallucinations by incorporating external knowledge, they do not eliminate them entirely. The article discusses methods to evaluate the trustworthiness of LLM outputs and introduces an example implementing LlamaParse for assessing responses through scoring.
The basic principle of Large Language Models (LLMs) is to predict the next word in a sequence based on patterns in training data; however, they lack true understanding.
LLMs are capable of tasks like text summarization and code generation, but they can generate false or inconsistent content—referred to as hallucinations.
Retrieval-augmented generation methods can reduce hallucinations but cannot fully eliminate them, highlighting the need for reliable criteria to assess LLM outputs.
The article explores how a trustworthy language model can score LLM outputs for trustworthiness, leading to the development of an example RAG using LlamaParse.
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