Omar Khattab, PhD Candidate at Stanford, discusses DSPy, a programming model that optimizes language model pipelines. Topics include the drawbacks of prompt-based approaches, fine-tuning modules, retrieval-based NLP systems, BERT in pipelines, and the concept of fine tuning in language models.
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Quick takeaways
Late interaction in retrieval improves the quality by representing documents as matrices.
DSPY allows customizable language model pipelines, enabling task-adaptive and efficient optimization.
DSPY compiler automates the optimization process by simulating the pipeline and exploring different options.
Deep dives
The power of late interaction and matrix representation in retrieval
In the podcast episode, the guest discusses the concept of late interaction in retrieval and emphasizes the benefits of representing documents as matrices rather than single vectors. Late interaction allows for more contextual representation of documents and improves the quality of retrieval. By using matrix representation, the quality of retrieval can be significantly improved, especially in domains with limited data. The guest highlights the importance of optimizing the encoding of documents to enhance the quality of retrieval, while keeping the process as fast and efficient as possible.
Modular programming approach with customizable modules
The podcast episode introduces the concept of DSPY, a programming framework that enables modular and customizable language model pipelines. Unlike other libraries that provide predefined modules, DSPY allows users to define their own modules with specific input-output behavior. These modules act as reusable building blocks that can be combined to create complex language model pipelines. The guest explains that the focus is on task-adaptive modules, where the same module can be trained differently for different tasks. DSPY enables users to write code for their desired workflow, interact with language models at specific points, and compile the code to optimize the pipeline's performance. This approach provides flexibility and allows for efficient optimization of language model pipelines.
Automated optimization through compiler and exploration of diverse models
The podcast episode introduces the concept of compiling language model pipelines in DSPY. The compiler simulates the pipeline on various examples and explores different options, including fine-tuning and prompting. By simulating the pipeline and tracking input-output behavior, the compiler automatically optimizes the pipeline's performance. This automated optimization allows users to explore different approaches and architectures, such as fine-tuning larger models or using shorter prompts. The guest draws an analogy to neural networks, where users specify the architecture, loss function, and optimizer, leaving the weight initialization and optimization to the neural network framework. Similarly, in DSPY, users specify the workflow and metric, while the compiler handles the optimization process.
Shifting from handcrafted prompts to structured prompting techniques
The podcast discusses the shift from using handcrafted prompts to structured prompting techniques in the development of language models. Traditionally, fine-tuning was used to adapt pre-trained language models, but this process was data-intensive, fragile, and required a lot of expertise. However, prompting techniques have emerged as a more effective alternative. Instead of relying solely on prompts, researchers are now focusing on how to prompt models in the right way and how to stack different models together. This shift allows for more resilient systems and better optimization, as prompts can be replaced with well-defined modules and parameters that can be automatically optimized.
The benefits of building modular pipelines with language models
The podcast highlights the advantages of building modular pipelines with language models. Rather than relying on hard-coded prompts and fine-tuning, modular pipelines allow for more flexibility and optimization. Instead of using a single language model for the entire pipeline, smaller models can be used for individual steps, keeping the system more efficient and reducing complexity. By breaking down the pipeline into smaller modules, the language model only needs to be called when necessary, and the rest of the steps can be handled by simpler components. This approach not only reduces the cost of fine-tuning but also enables easier debugging and iteration in the development process.
MLOps podcast #194 with Omar Khattab, PhD Candidate at Stanford, DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.
// Abstract
The ML community is rapidly exploring techniques for prompting language models (LMs) and for stacking them into pipelines that solve complex tasks. Unfortunately, existing LM pipelines are typically implemented using hard-coded "prompt templates", i.e. lengthy strings discovered via trial and error. Toward a more systematic approach for developing and optimizing LM pipelines, we introduce DSPy, a programming model that abstracts LM pipelines as text transformation graphs, i.e. imperative computational graphs where LMs are invoked through declarative modules. DSPy modules are parameterized, meaning they can learn (by creating and collecting demonstrations) how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. We design a compiler that will optimize any DSPy pipeline to maximize a given metric. We conduct two case studies, showing that succinct DSPy programs can express and optimize sophisticated LM pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot prompting and pipelines with expert-created demonstrations. On top of that, DSPy programs compiled to open and relatively small LMs like 770M-parameter T5 and llama2-13b-chat are competitive with approaches that rely on expert-written prompt chains for proprietary GPT-3.5. DSPy is available as open source at https://github.com/stanfordnlp/dspy
// Bio
Omar Khattab is a PhD candidate at Stanford and an Apple PhD Scholar in AI/ML. He builds retrieval models as well as retrieval-based NLP systems, which can leverage large text collections to craft knowledgeable responses efficiently and transparently. Omar is the author of the ColBERT retrieval model, which has been central to the development of the field of neural retrieval, and author of several of its derivate NLP systems like ColBERT-QA and Baleen. His recent work includes the DSPy framework for solving advanced tasks with language models (LMs) and retrieval models (RMs).
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Website: https://omarkhattab.com/
DSPy: https://github.com/stanfordnlp/dspy
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Timestamps:
[00:00] Omar's preferred coffee
[00:26] Takeaways
[06:40] Weight & Biases Ad
[09:00] Omar's tech background
[13:35] Evolution of RAG
[16:33] Complex retrievals
[21:32] Vector Encoding for Databases
[23:50] BERT vs New Models
[28:00] Resilient Pipelines: Design Principles
[33:37] MLOps Workflow Challenges
[36:15] Guiding LLMs for Tasks
[37:40] Large Language Models: Usage and Costs
[41:32] DSPy Breakdown
[51:05] AI Compliance Roundtable
[55:40] Fine-Tuning Frustrations and Solutions
[57:27] Fine-Tuning Challenges in ML
[1:00:55] Versatile GPT-3 in Agents
[1:03:53] AI Focus: DSP and Retrieval
[1:04:55] Commercialization plans
[1:05:27] Wrap up
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