Are LLMs Databases?

Are LLMs Databases? This topic has been hot recently, and it's an interesting question, if only because it helps us to think more deeply about what the LLM is actually doing.

The argument in favor of this, from what I've seen, is that LLMs, like databases, take in a query and return some data related to the query, just like a database. But the metaphor falls apart under closer examination.

Databases let you write queries with an expected result. LLMs sort of do this, but the query parsing is unpredictable even when you set temperature to 0. Databases have a way to query and or aggregate for exactly what you want, whereas LLMs do not.

Most significantly, LLMs will invent results instead of returning an empty set.

Much of this is because the LLM doesn't actually store individual records. It has only sets of probabilities, given a string of tokens, for what the next token should be. While some argue that these connections are database-like, I would counter that tokens are not the actual unit of information that the user cares about.

At best, an LLM could be thought of as a database which is lossily compressed. The data goes in, is translated into a smaller form, and then records can be pulled back out but with a loss of precision. Even this metaphor doesn't describe them well though. Lossy compression is usually applied on images, audio, or time-series data older than a certain date. But in this case, the nature of the compression is understood, and the quality of the reconstructed output is well-characterized. This is not the case for LLMs.

Others have argued that LLMs are better thought of as "reasoning engines" than as databases. This is better, partially because it comes closer to describing the behavior, but also because it is a term without decades of prior art to compare with.