Why can't Lucene search be used to power LLM applications?

[quantumadmin]

Lucene is a high-performance, full-featured text search engine library that is widely used in information retrieval and text analysis applications. On the other hand, Large Language Models (LLMs) like GPT-3 are advanced AI models designed to generate human-like text and understand context and semantics in a broader and more complex way.

While Lucene is powerful for keyword-based search and retrieval, it lacks the natural language processing capabilities that LLMs possess. Lucene primarily relies on techniques like tokenization, stemming, and inverted indexing to match and retrieve documents based on keywords or phrases. It does not have the ability to comprehend context, generate coherent and contextually relevant responses, or understand the semantics of language in the same way that LLMs do.

LLMs, on the other hand, can understand and generate human-like text, complete sentences, paragraphs, or even longer documents. They can comprehend context, answer questions, summarize text, translate languages, and perform various other language-related tasks. LLMs achieve this through their extensive pre-training on large amounts of text data and their ability to generate text using complex probabilistic language models.

In essence, while Lucene is exceptional for keyword-based search and retrieval tasks, it lacks the capabilities necessary for the advanced language understanding and generation tasks that LLMs excel at. Therefore, Lucene would not be an ideal choice to power applications that require the level of natural language understanding and generation provided by LLMs. Instead, LLMs like GPT-3 would be more suitable for such applications.