4 ms·
I am already starting to hear from product managers that they want to replace simple deterministic code with llm solutions. There are certainly domains where it
by mrbombastic 3y ago
I am already starting to hear from product managers that they want to replace simple deterministic code with llm solutions. There are certainly domains where it makes sense to use these tools but I am afraid they are good enough to essentially make a shiny sales demo for anything that we are gonna be inundated soon with unreliable services.
- cjk2 3y agoAs it always goes: Monkey get shiny hammer. Monkey hit toes.
- jvanderbot 3y agoWhat about summaries? Maybe LLMs are really good human approximates if you need to mashup two or more deterministically computed results into a coherent message. A RAG that pulls meta data about the system and takes as input some plain old computer output could ELI5 just about any result. This is more "project this result onto a context -specific human like space of answers" than "compute it all in LLM"
- cjk2 3y agoThis is actually one of my principal objections. A summary of a deterministic computation requires careful thought around how it is explained so that it is interpreted as intended. That requires understanding of the domain, understanding of the semantics of the data being presented and understanding the audience. A failure in this particular space in an observability product we use leads people wildly astray. While the data is correct, the summary is definitely not because it does not have enough understanding of the context.
- bluefirebrand 3y agoI have been working on a report building webapp for the past few months. Recently a PM floated the idea of using LLMs for generating a summary portion of the report I said to him "Would you really take a report into a meeting without knowing 100% what it was going to say on it beforehand? Even in the summary, do you want to risk presenting some wildly inaccurate LLM output to your boss and peers?" Essentially: Accuracy is only one thing we want out of software. Replicability is another one. LLMs are terrible at the latter and only "good enough" at the former
- HarHarVeryFunny 3y agoYeah, things like summarization and general chatbot use are some of the most impressive things LLMs can do, yet maybe the last things they should be used for in a business setting where the accuracy of the output matters. Air Canada found this out by being dumb enough to put an LLM-based chatbot into production. Better use cases for LLMs are either where: a) There is a non-judging human in the loop who is using the LLM as a tool, and accepts it's errors/limitations. It's a bit like asking a co-worker for advice - they might have something useful on occasion, but you're not just going to take what they say unchecked and put it on the company web site or send it to your boss as accepted truth. b) You can verify the output of the LLM in someway - maybe easier if it's a structured output of some sort (SQL query perhaps) rather than free-form text.
- cjk2 3y agoa) it is more effort to rationalise something that is written than it is to write something rationalised. b) if it was that deterministic, then you could generate it without an LLM.
- HarHarVeryFunny 3y agoAgreed. Other than just playing with them, the only place I've personally found any utility in LLMs is essentially as a kind of search to get a toehold in a subject matter I'm not familiar with.
- bluefirebrand 3y agoYeah, my thoughts are that the best usecase for LLMs is as an input to a process that is then controlled by a human For the summary thing, I think we eventually settled on a workflow that generates the summary using LLM, then we store the output in our dataset for the report. This also allows the human building the report to modify it if necessary
- sorokod 3y agoA problem with summaries is that it is not possible to verify their correctness without creating a summary by a human from scratch. In other words validation of the task costs at least as much as the original task. This is different from say code generation where, to first approximation at least, automated tools (compilers, unit tests) can do this cheaply.
- pixl97 3y agoI mean technically you have no idea how accurate the human summary is without multiple summaries.
- vsnf 3y agoBut I can incentivize, hold accountable, and hire people I trust to be competent. I cannot incentivize, hold accountable, or higher better quality LLMs.
- sorokod 3y agoTrue, we can and do make assumptions about quality based on our previous experience. I don't see an equivalent approach that can be applied to LLMs.