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On Google when I search for "X that does not Y" it would often return "X that does Y" instead. Hard to force Google to respect the query intent. LLMs on the oth

On Google when I search for "X that does not Y" it would often return "X that does Y" instead. Hard to force Google to respect the query intent. LLMs on the other hand process intent very well, but they hallucinate. So the obvious solution is a combination of them. Ideally Google search would have a flag to "follow my intent to the letter" and return empty if nothing is found. When you are searching for a specific thing, a response with other things feels like bullshit, Google trying to milk more clicks wasting your time. I don't mean exact phrase search, I mean exact semantic search. This is causing issues when searching for bug fixes by ignoring the exact version, when shopping it will ignore some of your filters and respond with bad product suggestions, and when searching something specific that looks like some other popular keyword, it will give you the popular one, as if if has an exclusion zone and you cannot search for anything else around it. "Minimum weight of a scooter with back suspension" -> matches information about carrying capacity. Of course more people discuss about max passenger weight than minimum scooter weight, but I really don't care about the other one.

kurthr 4y ago
I feel like what I want is a LLM that can tell me not only a summary answer, but what search terms are most likely to return documentation for the answer. Provide clickable citations documenting the answer using those terms (it can iterate on the answer and terms if they aren't internally consistent). Then give summaries of the information at each citation and/or parameters to input if the response requires further operations/searches. It could provide some commentary of the quality/recency of those citation/sources as well. That makes the response more current than the last LLM crawl&tune, allows a further directed search based on the new parameters, and provides a traceable path to sources and citations. If it finds that there's not a Python library with that name then iterate. Basically, an LLM should have a very good idea what good search terms are for the topic, and where to find the information, whereas I might not know the acronyms, jargon, or related fields and optimal answers. Yes, this is getting pretty close to writing an 8th grade class report that covers anything on the web. That's about where these seem to be. *ps I copied this from a post I made yesterday*

kurthr 4y ago

I feel like what I want is a LLM that can tell me not only a summary answer, but what search terms are most likely to return documentation for the answer. Provide clickable citations documenting the answer using those terms (it can iterate on the answer and terms if they aren't internally consistent). Then give summaries of the information at each citation and/or parameters to input if the response requires further operations/searches. It could provide some commentary of the quality/recency of those citation/sources as well. That makes the response more current than the last LLM crawl&tune, allows a further directed search based on the new parameters, and provides a traceable path to sources and citations. If it finds that there's not a Python library with that name then iterate. Basically, an LLM should have a very good idea what good search terms are for the topic, and where to find the information, whereas I might not know the acronyms, jargon, or related fields and optimal answers. Yes, this is getting pretty close to writing an 8th grade class report that covers anything on the web. That's about where these seem to be. *ps I copied this from a post I made yesterday*