Betting on DSPy for Systems of LLMs
Isaac Miller's blog discusses DSPy, an open-source framework for integrating LLM calls, emphasizing verifiable feedback and real-world metrics, while acknowledging its reliability issues and potential for future improvements.
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Isaac Miller's blog post discusses the open-source framework DSPy, which facilitates the integration of multiple large language model (LLM) calls to address real-world problems. He contrasts traditional machine learning, which requires a defined problem and data for training, with LLMs, which can generate unstructured data but still need to be anchored to specific problems. Miller emphasizes that LLMs should not be viewed as reasoning systems but rather as creative engines that excel in pattern matching and idea generation. DSPy enhances this process by enforcing verifiable feedback mechanisms, ensuring that LLM outputs are evaluated against real-world metrics. Despite its potential, Miller acknowledges that DSPy has reliability issues and is not beginner-friendly, which could hinder its adoption. He believes that the framework's commitment to evolving with new research and its ability to solve complex problems make it a valuable tool in the AI landscape. Miller concludes by recognizing the ongoing challenges within the AI ecosystem and DSPy, while expressing optimism about the framework's future improvements and the supportive open-source community surrounding it.
- DSPy is an open-source framework designed to integrate multiple LLM calls for problem-solving.
- The framework emphasizes the need for verifiable feedback and real-world metrics to evaluate LLM outputs.
- Miller highlights the limitations of LLMs, stating they should not be relied upon for reasoning tasks.
- DSPy faces challenges related to reliability and approachability for beginners.
- The framework is committed to evolving with new research and improving its functionality over time.
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6 commentsIt 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.
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?
* Multi-hop reasoning rarely works with real data in my case. * Impossible to define advanced metrics over the whole dataset. * No async support
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.