5 ms·

Strong doubt. The problem is LLMs don’t have a robust epistemology (they can’t), and are structurally unable to provide a basis over which they’ve “reasoned”.

by klysm 3y ago

blackbear_ 3y ago
Don't get too hung up on the present technical definition of LLM. Perhaps it is possible to find new architectures that are more suited to ground their claims.
- SrslyJosh 3y ago
  > Don't get too hung up on the present technical definition of LLM. The paper is literally about LLMs. Speculation about future model architectures is irrelevant.
lukev 3y ago
Using retrieval to look up a fact and then citing that fact in response to a query (with attribution) is absolutely within the capabilities of current LLMs. LLMs "hallucinate" all the time when pulling data from their weights (because that's how that works, it's all just token generation). But if the correct data is placed within their context they are very capable of presenting the data in natural language.
- ikekkdcjkfke 3y ago
  Like some kind pre-processing db lookup?
  - lukev 3y ago
    If you read the article, that’s literally what this is talking about. There’s a simple diagram and everything.
kelseyfrog 3y ago
Humans, when probed, don't have a robust epistemology either. Our knowledge(and reality) is grounded in heuristics we reify into natural order and it's easy for us to forget that our conclusions exist as a veneer on our perceptions. Nearly every bit of knowledge we hold has an opposite twin that we hold as well. We favor completeness over consistency. When pressed, humans tend to justify their heuristics rather than reexamine them because our minds have a clarity bias - ie: we would rather feel like things are clear even if they are wrong. Often times we can't go back and test if they are wrong which biases epistemological justifications even more. So no, our rationality, the vast proportion of times is used to rationalize rather than conclude.

Strong doubt. The problem is LLMs don’t have a robust epistemology (they can’t), and are structurally unable to provide a basis over which they’ve “reasoned”.

by klysm 3y ago

Strong doubt. The problem is LLMs don’t have a robust epistemology (they can’t), and are structurally unable to provide a basis over which they’ve “reasoned”.

blackbear_ 3y ago

Don't get too hung up on the present technical definition of LLM. Perhaps it is possible to find new architectures that are more suited to ground their claims.

SrslyJosh 3y ago
> Don't get too hung up on the present technical definition of LLM. The paper is literally about LLMs. Speculation about future model architectures is irrelevant.

lukev 3y ago

Using retrieval to look up a fact and then citing that fact in response to a query (with attribution) is absolutely within the capabilities of current LLMs. LLMs "hallucinate" all the time when pulling data from their weights (because that's how that works, it's all just token generation). But if the correct data is placed within their context they are very capable of presenting the data in natural language.

ikekkdcjkfke 3y ago
Like some kind pre-processing db lookup?
- lukev 3y ago
  If you read the article, that’s literally what this is talking about. There’s a simple diagram and everything.

kelseyfrog 3y ago

Humans, when probed, don't have a robust epistemology either. Our knowledge(and reality) is grounded in heuristics we reify into natural order and it's easy for us to forget that our conclusions exist as a veneer on our perceptions. Nearly every bit of knowledge we hold has an opposite twin that we hold as well. We favor completeness over consistency. When pressed, humans tend to justify their heuristics rather than reexamine them because our minds have a clarity bias - ie: we would rather feel like things are clear even if they are wrong. Often times we can't go back and test if they are wrong which biases epistemological justifications even more. So no, our rationality, the vast proportion of times is used to rationalize rather than conclude.