Unpopular Opinion: Big Labs Miss the Point of LLMs; Perplexity Shows the Viable AI Methodology

This article from r/ArtificialIntelligence argues that major AI labs are failing to address the fundamental issue of hallucinations in LLMs by focusing too much on knowledge compression. The author suggests that LLMs should be treated as text processors, relying on live data and web scraping for accurate output. They praise Perplexity's search-first approach as a more viable methodology, contrasting it with ChatGPT and Gemini's less effective secondary search features. The author believes this approach is also more reliable for coding applications, emphasizing the importance of accurate text generation based on input data.

• •Major AI labs are overly focused on knowledge compression, leading to hallucinations in LLMs.
• •LLMs should be treated as text processors, relying on external data sources for accuracy.
• •Perplexity's search-first approach is presented as a more viable and reliable methodology for AI.