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Comment by lnxg33k1 | Hacker News Reader

I don't know, it seems recently that we've detached what things are from what the people behind them say things are, so bing AI is an LLM, phind is an LLM, and they market themselves as search engine, like to see in details you say

>> It is not a mechanism to retrieve facts

Then bing AI powered by chatgpt shows on its site

>> [Hello, this is Bing! I’m the new AI-powered chat mode of Microsoft Bing that] can help you quickly get information.

if get is a synonym of retrieve and facts is a synonym of information, I'd argue that maybe it's not people misunderstanding what LLMs are but maybe someone explaining it wrong?

Guess in the world of marketing words don't carry any value anymore, then I agree with you

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Bing is not just an LLM. It’s RAG, the LLM is just a layer on top of a typical search engine. The LLM is not “getting” any facts, it’s just synthesizing them in a human-readable format.

LLMs are also often used in the search component of RAG, by generating embeddings that are then indexed and searched.

(I don’t know if that’s how Bing AI works)

How is LLM+RAG any different from a fine-tuned LLM without RAG? They're both trained on data, and have the capability to hallucinate.

Theoretically some people think RAG sounds more feasible to make factually accurate.

After all, if you've trained an LLM on a masses of unchecked data you've scraped from the internet, your training data probably includes "Joe Biden is the president" and "Donald Trump is the president" and "Barrack Obama is the president" and "Emmanuel Macron est le président" and so on. It would be understandable if an LLM was confused about who the president was.

These people think handing an LLM the contents of https://en.wikipedia.org/wiki/President_of_the_United_States then asking who the president is sounds a lot more feasible.

Personally I'm not so sure - I've never seen a RAG implementation that impressed me.

The point is that it should be able to point you to the source article. The synthesis isn’t necessarily more accurate, but you can check its work easily.

I believe what the parent comment is trying to imply is that the search results are fetched/retrieved from Bing's own internal ranking vector(?) database and then passed to the LLM, which then converts the received documents into a more human readable format and fills in any missing gaps in the information with it's own data.

So the gaps are the only areas where the LLM can hallucinate on and if your search query is easily available information on the internet, then hallucinations will be less or none.

Edit: I have used RAG with a project that I am working on and it's quite hard to ascertain if the LLM used the information provided as part of the RAG documents or just made up information on it's own, since even without RAG, we were getting similar responses 7 times out of 10.

I can see how people are confused by this though. Bing presents things it finds as facts, without really making it clear that they're just a synthesis of things it found on websites limited by it's search algorithm.

So if I ask Bing about me it says "Rory McCune is a Cloud Native Security Advocate at Aqua Security." without any ref.

The problem is, that's not correct, that's a job I had two years ago, but someone reading that could be forgiven for thinking that's a fact, given how it was presented.

In this case that's harmless, but I could easily see cases where it would not be harmless.

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