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Betting on DSPy for Systems of LLMs
- okigan 2y agoCould we have a concise and specific explanation how DSPy works? All I've seen are vague definitions of new terms (ex. signatures) and "trust me this very powerful and will optimize it all for you". Also, what would a good way to reason between DSPy and TextGrad?
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- curious_cat_163 2y agoMy understanding is that is tries many variations of the set of few shot examples and prompts and picks the ones that work best as the optimized program.
- ktrnka 2y agoTextgrad mainly optimizes the prompt but does not inject few shot examples. Dspy mainly optimizes the few shot examples. At least that's my understanding from reading the textgrad paper recently.
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- NeutralCrane 2y agoThe more I’ve looked at DSPy, the less impressed I am. The design of the project is very confusing with non-sensical, convoluted abstractions. And for all the discussion surrounding it, I’ve yet to see someone actually using for something other than a toy example. I’m not sure I’ve even seen someone prove it can do what it claims to in terms of prompt optimization. It reminds me very much of Langchain in that it feels like a rushed, unnecessary set of abstractions that add more friction than actual benefit, and ultimately boils down to an attempt to stake a claim as a major framework in the still very young stages of LLMs, as opposed to solving an actual problem.
- Der_Einzige 2y agoAgreed 100%. DSPy along with libraries inspired by it (i.e. https://github.com/zou-group/textgrad https://github.com/zou-group/textgrad) are nothing more than fancy prompt chains under the hood. These libraries mostly exist as "cope" for the fact that we don't have good fine-tuning (i.e. lora) capabilities for ChatGPT et al, so we try to instead optimize the prompt.
- qeternity 2y agoGlad to see others saying this. I haven't looked at it in some months, but I previously realized it's mostly a very complicated way to optimize few-shot learning prompts. It's hardly whatever magical blackbox optimizer they try to market it as.
- dmarchand90 2y agoMy guess is it will be like pascal or smalltalk, an important development for illustrating a concept but is ultimately replaced by something more rigorous
- isaacbmiller 2y ago> These libraries mostly exist as "cope" > nothing more than fancy prompt chains under the hood Some approaches using steering vectors, clever ways of fine-tuning, transfer decoding, some tree search sampling-esque approaches, and others all seem very promising. DSPy is, yes, ultimately a fancy prompt chain. Even once we integrate some of the other approaches, I don't think it becomes a single-lever problem where we can only change one thing(e.g., fine-tune a model) and that solves all of our problems. It will likely always be a combination of the few most powerful levers to pull.
- Der_Einzige 2y agoCorrect, when I say "ChatGPT et al", I mean closed source paywalled LLMs, open access LLM personalization is an extreme gamechanger. All of what you mentioned is important, and I'm particularly excited about PyReft. https://github.com/stanfordnlp/pyreft https://github.com/stanfordnlp/pyreft Anything Christopher Manning touches turns to gold.
- revskill 2y agoWhenever i see "ChainOfThought" for AI, it's an annoying and misleading term. Machine never never thinks at all.
- fsndz 2y agoI tried it recently and it is kinda fun: https://www.lycee.ai/courses/a5b7d115-c794-410d-92f2-15d8f2932130/chapters/bc227f31-3c8b-425b-99dd-c5e597ac943e https://www.lycee.ai/courses/a5b7d115-c794-410d-92f2-15d8f29...
- gunalx 2y agoNot to say anything about dspy, but I really liked the take on hvat we should use llms for. We need to stop doing useless reasoning stuff, and find acttual fitting problems for the llms to solve. Current llms are not your db manager(if they could be you don't have a db size in the real world). They are not a developer. We have people for that. Llms prove to be decent creative tools, classificators, and qna answer generators.
- isaacfung 2y agoWe have translators. Doesn't mean we can't replace them with a cheaper, more accessible tool. That's the whole point of automation. Reasoning stuff is not useless. They provably(according to benchmarks) improve the performance of coding and math related tasks.
- thatsadude 2y agoI had a few problems with DSPy: * Multi-hop reasoning rarely works with real data in my case. * Impossible to define advanced metrics over the whole dataset. * No async support