Large Language Models are not a search engine
Large Language Models (LLMs) from Google and Meta generate algorithmic content, causing nonsensical "hallucinations." Companies struggle to manage errors post-generation due to factors like training data and temperature settings. LLMs aim to improve user interactions but raise skepticism about delivering factual information.
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Large Language Models (LLMs) like those used by Google and Meta are transforming search functions into platforms generating algorithmically generated content, sometimes leading to nonsensical outcomes. These "hallucinations" stem from the challenge of predicting probability distributions within vast text collections. LLMs are not designed to produce truth but rather statistically likely outcomes. Companies are now grappling with how to control these errors post-generation. Factors like temperature settings and training data influence the text generated by LLMs, leading to unpredictable results. Social media companies leverage human feedback to refine these models, aiming to improve user interactions. Despite their creative potential, LLMs may not always deliver factual information, prompting skepticism about their role as search engines. Google's CEO acknowledges the inherent challenges of LLMs, emphasizing the importance of grounding them with contextual information for a better user experience. The complexity of LLMs highlights the intricate balance between variety and accuracy in information retrieval systems. Ultimately, the debate continues on the suitability of LLMs for search engine functions in light of their unpredictable nature and potential for generating misleading content.
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13 commentsCertainly I find LLMs replace a lot of searches for me and google/microsoft is right to eat its own breakfast to get ahead of it.
How I interpret it: it is a more powerful version of stemming and synonym expansion of information retrieval classics when generating the queries it feeds into traditional information systems (such as the Bing search engine via API, or other index).
After retrieval, it's a selector and summarizer of repetition seen in the results to give you something of a blended outcome, pertinent to the prompt you gave it. Like any other tool, you get a feel for when it has is having problems, and some of those problems can be assessed by at least glancing at the sources it consulted. You get all sorts of weird stuff when your sources don't include relevant results or biased results
The first problem happens when the documents you are searching for do not exist, or something about your prompt -- it's usually obvious what it is -- is not sourcing documents you know to exist.
The second, bias, I've seen when researching something like the design conceits of Infiniband. While it has its genuine virtues, almost nobody talks about it...and many of those things that discuss it are Infiniband marketing materials that are both a bit too fluffy and sometimes stretch the truth, as marketing materials are wont to do. But you can spot this in the sources panel immediately.
I never found "disembodied" LLMs very useful.
yep!
Being able to ask one of the better open tunes a question I would normally ask Google makes it possible to work from anywhere in a way that hasn’t been true since I was a kid.
Big, overfunded, ethically and legally dubious data-vacuum black box APIs are and should be controversial.
Stack Overflow and much else in 30-70Gb on my MacBook Pro on a beach is strictly awesome. That should not be controversial.
cc Scott Wiener and the thugs paying.
For us with lower level of comprehension, LLMs are literally brain extensions already.
I don’t know about you, but I am using search engines a lot less, and asking LLM’s a lot more.
It doesn’t bode well for the search engine business overall.
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