LLMs Don’t Need Search Engines: They Can Search Their Own Brains

Author(s): MKWriteshere

Originally published on Towards AI.

SSRL Framework Proves AI Models Already Contain the Knowledge They Keep Looking Up

We’ve been training AI to ask Google for answers when we should have been teaching it to remember what it already knows. The implications for AI costs and autonomy are staggering.

This article explores the capabilities of large language models (LLMs) in searching their own knowledge without relying on external search engines. Researchers from Tsinghua University and Shanghai AI Laboratory demonstrated that LLMs can access embedded training knowledge effectively, leading to insights that question the necessity of traditional search engines. The key findings indicate that small models can match the performance of larger ones through repetitive querying, reducing operational costs. The piece also discusses the implications for AI autonomy and cost-efficiency, advocating for smarter training methodologies and cautioning against overconfidence in internal knowledge.

Read the full blog for free on Medium.

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Published via Towards AI