DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines

We're excited to welcome Omar Khattab, the author of the ColBERT family of retrieval models. Omar is going to We introduce DSPy, a programming model that streamlines the development of language model (LM) pipelines. Unlike traditional hard-coded prompt templates, DSPy uses text transformation graphs to invoke LMs through declarative, parameterized modules. These modules can learn optimized combinations of prompting, finetuning, augmentation, and reasoning. A specialized compiler optimizes DSPy pipelines for given metrics. Case studies show that DSPy quickly generates and finetunes pipelines that surpass both few-shot prompting and expert setups for GPT-3.5 and Llama2-13b-chat.
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[@w_chadly]

I love the metaphor of compilation here. treating low-level prompting similar to machine byte code. it feels like we are starting to come out of the analogous "punch card era" of computing with this ❤

[@twoplustwo5]

🎯 Key Takeaways for quick navigation:
00:00 📚 *Introduction to Language and Retrieval Models*
- Discusses the active research in language and retrieval models.
- Highlights the importance of adapting these models to new domains.
- Emphasizes the need for reliable systems based on language models.
01:50 🏰 *Example of a Complex Question*
- Presents a complex question as an example to illustrate the limitations of language models.
- Explains how most models either abstain or provide incorrect answers.
- Suggests the need for a more complex pipeline for accurate answers.
04:55 🔄 *Importance of Complex Pipelines*
- Discusses the inherent complexity of tasks and the need for complex pipelines.
- Mentions the rapid expansion of literature on building these pipelines.
- Advises investing in pipeline and system design.
07:29 🧩 *Challenges in Building Effective Pipelines*
- Describes the difficulties in building and optimizing effective pipelines.
- Highlights the issues with fine-tuning and the brittleness of prompts.
- Points out the commonality of large, complex prompts in research papers.
09:36 💡 *Introduction to DSPy Solution*
- Introduces DSPy as a solution to the challenges in building effective pipelines.
- Proposes the concept of programming rather than prompting language models.
- Aims to shift focus from tweaking models to building good system design.
12:08 📝 *Key Concepts of DSPy*
- Explains the three key concepts of DSPy: signatures, signature-based modules, and teleprompters.
- Describes how these concepts help in building and optimizing pipelines.
- Compares DSPy to PyTorch, with the language model as the device.
15:04 🧮 *Example of a Math Word Problem*
- Presents a math word problem as an example to illustrate the use of DSPy.
- Shows how to build a simple one-stage pipeline using DSPy.
- Demonstrates how to expand the pipeline to include a Chain of Thought component.
17:54 📝 *Writing Code with DSPy*
- Discusses how to write code using DSPy.
- Explains the process of defining a class with two methods for the program.
- Highlights the flexibility of DSPy in tweaking signatures to generate different outputs.
19:58 🛠️ *Compiling the Program*
- Describes the process of compiling the program using DSPy.
- Compares the process to training a model in PyTorch.
- Emphasizes the importance of selecting the right teleprompter for the compilation.
22:17 🔄 *Generating Prompts*
- Explains how DSPy generates prompts under the hood.
- Discusses how the process becomes more interesting with larger multi-stage pipelines.
- Highlights the flexibility of DSPy in compiling in different ways.
25:31 📈 *Improving Accuracy with DSPy*
- Shows how DSPy can significantly improve accuracy.
- Demonstrates the improvement in accuracy with different programs and models.
- Emphasizes the effectiveness of the compilation process in DSPy.
30:06 🎯 *Multihop Questions with Hotpot QA*
- Introduces the task of answering multihop questions with Hotpot QA.
- Discusses the process of describing the task in DSPy.
- Highlights the high scores achieved with DSPy.
35:18 🧩 *Building the Program*
- Discusses the process of building the program using DSPy.
- Highlights the flexibility of DSPy in working with any retriever or language model.
- Explains how the program can generate answers based on the context and question.
37:24 🔄 *Compiling the Program*
- Describes the process of compiling the program using DSPy.
- Explains how the compiler works under the hood.
- Discusses the three stages of the compilation process: candidate generation, parameter optimization, and pipeline optimization.
39:39 📈 *Evaluating the Effectiveness of DSPy*
- Discusses the effectiveness of DSPy in asking new systematic questions and delivering large gains.
- Demonstrates the improvement in accuracy with different programs and models.
- Highlights the high scores achieved with DSPy.
43:10 🎯 *Refining the Use of Language Models*
- Discusses the need to refine the use of language models.
- Highlights the importance of making concrete and reproducible claims about the capabilities of language models.
- Emphasizes the need for systematic approaches rather than trial and error.
45:03 💰 *Cost-Effectiveness of DSPy*
- Discusses the cost-effectiveness of using DSPy with proprietary language models.
- Highlights the potential savings in terms of API completion costs.
- Emphasizes the value of investing in pipeline development rather than prompt engineering.
47:32 🎚️ *Defining Metrics in DSPy*
- Discusses the process of defining metrics in DSPy.
- Highlights the importance of writing your own metrics as DSPy programs.
- Emphasizes the need for iterative improvement of programs, metrics, and data size.
49:42 🔄 *Implementing Chain of Thought in DSPy*
- Discusses the implementation of the Chain of Thought module in DSPy.
- Highlights the simplicity of the module and its ability to mutate signatures internally.
- Emphasizes the benefits of using Chain of Thought as a module rather than just an idea applied to prompts.
51:05 🎯 *Key Takeaways from DSPy*
- Summarizes the key takeaways from the use of DSPy.
- Highlights the benefits of thinking about multi-stage pipelines rather than relying on the size of the model.
- Emphasizes the potential of DSPy for collaborative, modular, open research.
Made with HARPA AI

[@mysticaltech]

Absolutely amazing technology.

[@powerspeed1295]

It is not clear to me if with this teleprompter is there a way to let it create a prompt from scratch, without any training set but just with a test set or it is just a function that retrieve a good example.
In the second case it's a nice to have, but I don't see the big advantage .

[@twoplustwo5]

yes video does help to get what DSPy is

[@loritagermana2325]

promo sm 🤤

[@8eck]

The most interesting part about compilation was very short and skipped...