DSPy: Transforming Language Model Calls into Smart Pipelines // Omar Khattab // #194
MLOps.community
Rethinking Prompting Strategies and Fine-tuning in ML Pipelines
This chapter explores the drawbacks of the prompt first approach in language models and introduces DSP as a solution. The speaker also discusses the concept of fine-tuning modules in DSP and its impact on data pipelines and ML pipelines.
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 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Omar on Twitter: https://twitter.com/lateinteraction 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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