AI Agents for Data Analysis with Shreya Shankar - #703
Digest
This podcast features Shreya Shankar, a PhD student at UC Berkeley, discussing her work on Doc ETL, a declarative framework for building and optimizing LLM-powered data processing pipelines. The conversation begins with an overview of Shreya's background and her interest in interactive and intelligent data processing. She then delves into the rise of generative AI and LLMs, highlighting the challenges of designing intuitive interfaces for interacting with these powerful technologies. Shreya introduces Doc ETL, showcasing its use case in analyzing police misconduct data at Berkeley. Doc ETL simplifies LLM programming by providing high-level prompts and automating pipeline optimization. The importance of interactivity and human-in-the-loop evaluation is emphasized, as humans often need to see initial outputs to refine their prompts and understand how to best utilize LLMs. The conversation explores the evolving nature of human intent and the challenges of fully automated prompt engineering. Shreya discusses the complexities of evaluation in the context of LLM-powered data processing, highlighting the need for task-specific validation prompts and the challenges of handling subjective or ambiguous human intent. The podcast delves into the challenges of building agentic systems, particularly in the context of Doc ETL. Shreya highlights the complexity of handling agent failures and the need for fault tolerance mechanisms. She emphasizes the importance of constraining the domain of agents and designing them to produce structured outputs. The need for data processing benchmarks specifically designed for LLM-powered tasks is discussed. Shreya argues that traditional benchmarks don't adequately capture the challenges of working with large, unstructured datasets. She proposes key characteristics for data processing benchmarks, including flexibility in data decomposition, support for subjective correct answers, and a focus on LLM orchestration. The podcast concludes with a discussion of future directions for Doc ETL, including the development of a benchmark for data processing, continued work on interface design, and improvements to optimizer reliability. Shreya discusses the challenges of using chain-of-thought decomposition and the potential of future models like O1 for addressing these issues.
Outlines
Introduction and Doc ETL: A Declarative Framework for LLM-Powered Data Processing
This chapter introduces Shreya Shankar, a PhD student at UC Berkeley, and her work on Doc ETL, a declarative framework for building and optimizing LLM-powered data processing pipelines. Shreya discusses her background, the rise of generative AI and LLMs, and the challenges of designing intuitive interfaces for interacting with these technologies. She then introduces Doc ETL and its use case in analyzing police misconduct data at Berkeley.
The Importance of Interactivity and Human-in-the-Loop Evaluation
This chapter emphasizes the importance of interactivity in LLM-powered data processing. Shreya explains that humans often need to see initial outputs to refine their prompts and understand how to best utilize LLMs for specific tasks. This highlights the need for human-in-the-loop evaluation and the challenges of designing interfaces that facilitate rapid iteration.
The Evolving Nature of Human Intent and Prompt Engineering
This chapter discusses the surprising observation that human intent can change as they interact with LLMs and see intermediate outputs. This challenges the idea of fully automated prompt engineering and highlights the need for human guidance in the loop. Shreya provides examples of how humans might adjust their expectations or refine their understanding of a task based on LLM outputs.
Evaluation in the Context of LLM-Powered Data Processing
This chapter delves into the complexities of evaluation in the context of LLM-powered data processing. Shreya explains how Doc ETL optimizes pipelines by generating candidate plans and evaluating their performance on sample data. She discusses the importance of task-specific validation prompts and the challenges of handling subjective or ambiguous human intent.
The Challenges of Building Agentic Systems
This chapter discusses the challenges of building agentic systems, particularly in the context of Doc ETL. Shreya highlights the complexity of handling agent failures and the need for fault tolerance mechanisms. She emphasizes the importance of constraining the domain of agents and designing them to produce structured outputs.
The Need for Data Processing Benchmarks and Future Directions for Doc ETL
This chapter argues for the need for benchmarks specifically designed for LLM-powered data processing tasks. Shreya explains that traditional benchmarks, focused on reasoning or coding problems, don't adequately capture the challenges of working with large, unstructured datasets. She proposes key characteristics for data processing benchmarks, including flexibility in data decomposition, support for subjective correct answers, and a focus on LLM orchestration. The chapter concludes with a discussion of future directions for Doc ETL, including the development of a benchmark for data processing, continued work on interface design, and improvements to optimizer reliability. Shreya discusses the challenges of using chain-of-thought decomposition and the potential of future models like O1 for addressing these issues.
Keywords
Doc ETL
A declarative framework for building and optimizing LLM-powered data processing pipelines. It simplifies the process of programming LLMs for data processing tasks by providing high-level prompts and automating the optimization of pipelines.
LLM-powered data processing
The use of large language models (LLMs) to analyze and process unstructured data, such as text documents, images, or audio files. This involves tasks like text extraction, classification, summarization, and data transformation.
Human-in-the-loop evaluation
A process where humans are involved in the evaluation of LLM outputs, providing feedback and guidance to refine prompts and improve the accuracy and reliability of LLM-powered data processing pipelines.
Agentic systems
Systems that utilize multiple agents, often LLMs, to perform complex tasks. These systems require careful orchestration, fault tolerance mechanisms, and robust communication protocols to ensure reliable operation.
Data processing benchmarks
Standardized datasets and tasks used to evaluate the performance of LLM-powered data processing systems. These benchmarks should capture the unique challenges of working with large, unstructured datasets and allow for flexibility in data decomposition and LLM orchestration.
Chain-of-thought decomposition
A technique used to break down complex tasks into a series of smaller, more manageable steps. LLMs can be used to generate these decompositions, but it's important to ensure that the resulting chain of thought is both effective and efficient.
Interactive data processing
A process where humans can interact with LLMs during data processing, providing feedback, refining prompts, and iteratively improving the results. This approach emphasizes the importance of human-in-the-loop evaluation and the need for intuitive interfaces.
Criteria drift
A phenomenon observed in human-in-the-loop evaluation where human criteria for evaluating LLM outputs can change over time as they see more examples and gain a better understanding of the task. This highlights the need for flexible evaluation frameworks that can adapt to evolving human intent.
Q&A
What is Doc ETL and how does it work?
Doc ETL is a declarative framework for building and optimizing LLM-powered data processing pipelines. It allows users to specify high-level prompts for data processing operations, and then automatically rewrites those prompts, chunks up the data, and executes the tasks in smaller units that LLMs can handle accurately.
Why is interactivity important in LLM-powered data processing?
Humans often need to see initial outputs from LLMs to refine their prompts and understand how to best utilize LLMs for specific tasks. This highlights the need for human-in-the-loop evaluation and the challenges of designing interfaces that facilitate rapid iteration.
What are the challenges of building agentic systems for LLM-powered data processing?
Building agentic systems requires careful orchestration, fault tolerance mechanisms, and robust communication protocols to ensure reliable operation. It's also important to constrain the domain of agents and design them to produce structured outputs.
Why are data processing benchmarks important and how should they be designed?
Data processing benchmarks are crucial for evaluating the performance of LLM-powered data processing systems. They should capture the unique challenges of working with large, unstructured datasets and allow for flexibility in data decomposition and LLM orchestration. They should also support subjective correct answers and allow for different flavors of the same task.
What are some future directions for Doc ETL?
Future directions for Doc ETL include the development of a benchmark for data processing, continued work on interface design, and improvements to optimizer reliability. The team is also exploring the use of more advanced models like O1 to address challenges related to chain-of-thought decomposition.
Show Notes
Today, we're joined by Shreya Shankar, a PhD student at UC Berkeley to discuss DocETL, a declarative system for building and optimizing LLM-powered data processing pipelines for large-scale and complex document analysis tasks. We explore how DocETL's optimizer architecture works, the intricacies of building agentic systems for data processing, the current landscape of benchmarks for data processing tasks, how these differ from reasoning-based benchmarks, and the need for robust evaluation methods for human-in-the-loop LLM workflows. Additionally, Shreya shares real-world applications of DocETL, the importance of effective validation prompts, and building robust and fault-tolerant agentic systems. Lastly, we cover the need for benchmarks tailored to LLM-powered data processing tasks and the future directions for DocETL.
The complete show notes for this episode can be found at https://twimlai.com/go/703.
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