4 ms·

I feel like all of the interesting stuff when it comes to search+LLM is in real hybridization that's still a few steps out from what's happening right now in te

by sho_hn 4y ago

I feel like all of the interesting stuff when it comes to search+LLM is in real hybridization that's still a few steps out from what's happening right now in terms of productization. For example: (a) Addressing the recency problem by making a model generate instructions to a browser as output token stream, so it can, well, Google for you and incorporate latest info into output by adding it to the "internal prompt". (b) Doing something similar where you teach models how to drive computational engines to make actual, computed facts and figures, not guess them from statistics over training data. (c) Maybe running a traditional search and then using LLMs to summarize over them. etc. etc. It feels increasingly clear that "complete prompt from a stale training data set" is a neat trick but doesn't solve any particular problem well enough. This also seems intuitively true: If you ask your friend a difficult question, you often don't expect them to be able to answer it on the spot. You probably expect them to have relevant experience and a rough understanding of the topics and the tools to look up or figure out the rest. Similarly, it feels intuitively wrong to expect a LLM to come ready-made with all knowledge baked in. It feels more like we'd want something with the working knowledge/method to plan and execute fact discovery and computation. It's fun to play around a bit with ChatGPT yourself and try to approximate what a more complex pipeline might be. For example, instead of asking it to answer a question, ask it to re-state the question or make a list of questions prompted by the original question, then let it "contemplate" those or propose a method for how to go after answering them, etc. It runs aground pretty often, but sometimes the results take you a lot further than the direct approach.

PaulDavisThe1st 4y ago
> This also seems intuitively true: If you ask your friend a difficult question, you often don't expect them to be able to answer it on the spot. You probably expect them to have relevant experience and a rough understanding of the topics and the tools to look up or figure out the rest. Similarly, it feels intuitively wrong to expect a LLM to come ready-made with all knowledge baked in. Not sure. In theory, this being computers and all, I sort of do expect my search engine to effectively come "ready-made with all knowledge baked in". I do not expect Google to start a new web crawl because I gave it a novel query, and I don't expect a language model (certainly not one being used for search purposes) to say "hang on, I need to go read some more before I answer that".
- sho_hn 4y ago
  I've seen some studies that call what I mean a "chain of thought" approach (also reasonable is just "higher-level reasoning", but maybe we need a term for the engineered implementation), and I like that terminology. I don't know, it's a gut feeling, but a one-shot prompt completion feels intuitively too simple for me - it seems like we need to build up more elaborate "thought processes" next where we use these methods for individual steps in those thought processes, but also have steps that can carry out a computation or go on a fact search. A bit similar, again, to how humans will answer plenty of questions on the spot from working knowledge, but are also able to classify when they need to drop into a slower "let's do the math" mode and then carry that out. We may be able to from these "thought processes" then generate training data sets that again reasonably cache "all possible accurate answers" as you demand, but it's not clear to me if that caching will really save computational cost or just redistribute it to different times. At that point it's probably a latency/throughput question of when you do vs. defer the "thinking".
  - PaulDavisThe1st 4y ago
    That's undoubtedly true for "reasoning". I don't think it is true for "search". When I ask <search engine> a question that is "demonstrably" answerable based on "reliable" sources (e.g. what is the capital of Mexico), I don't need or expect it to reason. I want it to (a) tell me the most likely answer, probably (b) provide a list a of URLs that also provide an answer, probably the same one. And that's what Google and others do right now; ignoring the results presentations being skewed by commercial interests, they do it rather well. Figuring out when a question is not answerable in this way, but requires reasoning, is certainly a part of the challenge.

by sho_hn 4y ago

PaulDavisThe1st 4y ago

> This also seems intuitively true: If you ask your friend a difficult question, you often don't expect them to be able to answer it on the spot. You probably expect them to have relevant experience and a rough understanding of the topics and the tools to look up or figure out the rest. Similarly, it feels intuitively wrong to expect a LLM to come ready-made with all knowledge baked in. Not sure. In theory, this being computers and all, I sort of do expect my search engine to effectively come "ready-made with all knowledge baked in". I do not expect Google to start a new web crawl because I gave it a novel query, and I don't expect a language model (certainly not one being used for search purposes) to say "hang on, I need to go read some more before I answer that".

sho_hn 4y ago
I've seen some studies that call what I mean a "chain of thought" approach (also reasonable is just "higher-level reasoning", but maybe we need a term for the engineered implementation), and I like that terminology. I don't know, it's a gut feeling, but a one-shot prompt completion feels intuitively too simple for me - it seems like we need to build up more elaborate "thought processes" next where we use these methods for individual steps in those thought processes, but also have steps that can carry out a computation or go on a fact search. A bit similar, again, to how humans will answer plenty of questions on the spot from working knowledge, but are also able to classify when they need to drop into a slower "let's do the math" mode and then carry that out. We may be able to from these "thought processes" then generate training data sets that again reasonably cache "all possible accurate answers" as you demand, but it's not clear to me if that caching will really save computational cost or just redistribute it to different times. At that point it's probably a latency/throughput question of when you do vs. defer the "thinking".
- PaulDavisThe1st 4y ago
  That's undoubtedly true for "reasoning". I don't think it is true for "search". When I ask <search engine> a question that is "demonstrably" answerable based on "reliable" sources (e.g. what is the capital of Mexico), I don't need or expect it to reason. I want it to (a) tell me the most likely answer, probably (b) provide a list a of URLs that also provide an answer, probably the same one. And that's what Google and others do right now; ignoring the results presentations being skewed by commercial interests, they do it rather well. Figuring out when a question is not answerable in this way, but requires reasoning, is certainly a part of the challenge.