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DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax and an automatic compiler that teaches LMs how to conduct the

by okhat 3y ago

DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax and an automatic compiler that teaches LMs how to conduct the declarative steps in your program. Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.

simonw 3y ago
"Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs" I'm having trouble understanding the value provided here. A prompt is a string. Why abstract that string away from me like this? My instinct is that this will make it harder, not easier, for me to understand what's going on and make necessary changes.
- pama 3y ago
  Agreed. I don’t understand the trend for abstracting away the best possible human interface.
- okhat 3y ago
  "A neural network layer is just a matrix. Why abstract that matrix and learn it?" Well, because it's not your job to figure out how to hardcode delicate string or floats that work well for a given architecture & backend. We want developers to iterate quickly on system designs: How should we break down the task? Where do we call LMs? What should they do? --- If you can guess the right prompts right away for each LLM, tweak them well for any complex pipeline, and rarely have to change the pipeline (and hence all prompts in it), then you probably won't need this. That said, it turns out that (a) prompts that work well are very specific to particular LMs, large & especially small ones, (b) prompts that work well change significantly when you tweak your pipeline or your data, and (c) prompts that work well may be long and time-consuming to find. Oh, and often the prompt that works well changes for different inputs. Thinking in terms of strings is a glaring anti-pattern.
  - simonw 3y ago
    I agree with you on all of those points - but my conclusion is different: those are the reasons it's so important to me that the prompts are not abstracted away from me! I'm working with Llama 2 a bunch at the moment and much of the challenge is learning how to prompt it differently from how I prompt GPT-4. I'm not yet convinced that an abstraction will solve that problem for me.
    - behnamoh 3y ago
      this is one of the reasons why Langchain sucks. People seem to underestimate and overlook the importance of prompts.
      - verdverm 3y ago
        > People seem to underestimate and overlook the importance of prompts. We do this to each other as well. Being able to communicate clear, concise, and complete requests will produce better results with both humans and LLMs. What is interesting is that we can experiment with prompts against machines at a scale we cannot with other people. I'd really like to see more work towards leveraging this feature to improve our human interactions, kind of like empathy training in VR
  - okhat 3y ago
    @simonw it sounds like we'd agree that: 1] when prototyping, it's useful to not have to tweak each prompt by hand as long as you can inspect them easily 2] when the system design is "final", it's important to be able to tweak any prompts or finetunes with full flexibility But we may or may not agree on: 3] automatic optimization can basically make #2 above only very rarely needed --- Anyway, the entire DSPy project has zero hard-coded prompts for tasks. It's all bootstrapped and validated for your logic. In case you're worried that we're doing some opinionated prompting on your behalf.
    - janekm 3y ago
      It sounds fascinating! Is there anything one could read to figure out more about how this is being done (From reading the docs by the "Teleprompter"s right)?
- okhat 3y ago
  btw read a more official answer here: https://github.com/stanfordnlp/dspy#5a-dspy-vs-thin-wrappers-around-prompts-openai-api-minichain-basic-templating-etc https://github.com/stanfordnlp/dspy#5a-dspy-vs-thin-wrappers...
- fassssst 3y ago
  Because there are magic strings that work considerably better than what you might come up with. Like “think step by step.”

by okhat 3y ago

simonw 3y ago

"Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs" I'm having trouble understanding the value provided here. A prompt is a string. Why abstract that string away from me like this? My instinct is that this will make it harder, not easier, for me to understand what's going on and make necessary changes.

pama 3y ago
Agreed. I don’t understand the trend for abstracting away the best possible human interface.
okhat 3y ago
"A neural network layer is just a matrix. Why abstract that matrix and learn it?" Well, because it's not your job to figure out how to hardcode delicate string or floats that work well for a given architecture & backend. We want developers to iterate quickly on system designs: How should we break down the task? Where do we call LMs? What should they do? --- If you can guess the right prompts right away for each LLM, tweak them well for any complex pipeline, and rarely have to change the pipeline (and hence all prompts in it), then you probably won't need this. That said, it turns out that (a) prompts that work well are very specific to particular LMs, large & especially small ones, (b) prompts that work well change significantly when you tweak your pipeline or your data, and (c) prompts that work well may be long and time-consuming to find. Oh, and often the prompt that works well changes for different inputs. Thinking in terms of strings is a glaring anti-pattern.
- simonw 3y ago
  I agree with you on all of those points - but my conclusion is different: those are the reasons it's so important to me that the prompts are not abstracted away from me! I'm working with Llama 2 a bunch at the moment and much of the challenge is learning how to prompt it differently from how I prompt GPT-4. I'm not yet convinced that an abstraction will solve that problem for me.
  - behnamoh 3y ago
    this is one of the reasons why Langchain sucks. People seem to underestimate and overlook the importance of prompts.
    - verdverm 3y ago
      > People seem to underestimate and overlook the importance of prompts. We do this to each other as well. Being able to communicate clear, concise, and complete requests will produce better results with both humans and LLMs. What is interesting is that we can experiment with prompts against machines at a scale we cannot with other people. I'd really like to see more work towards leveraging this feature to improve our human interactions, kind of like empathy training in VR
- okhat 3y ago
  @simonw it sounds like we'd agree that: 1] when prototyping, it's useful to not have to tweak each prompt by hand as long as you can inspect them easily 2] when the system design is "final", it's important to be able to tweak any prompts or finetunes with full flexibility But we may or may not agree on: 3] automatic optimization can basically make #2 above only very rarely needed --- Anyway, the entire DSPy project has zero hard-coded prompts for tasks. It's all bootstrapped and validated for your logic. In case you're worried that we're doing some opinionated prompting on your behalf.
  - janekm 3y ago
    It sounds fascinating! Is there anything one could read to figure out more about how this is being done (From reading the docs by the "Teleprompter"s right)?
okhat 3y ago
btw read a more official answer here: https://github.com/stanfordnlp/dspy#5a-dspy-vs-thin-wrappers-around-prompts-openai-api-minichain-basic-templating-etc https://github.com/stanfordnlp/dspy#5a-dspy-vs-thin-wrappers...
fassssst 3y ago
Because there are magic strings that work considerably better than what you might come up with. Like “think step by step.”