Livecoding: Getting Started with LLMs, by Jeremy Howard

2023-08-11 · Source: The Full Stack · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, extended

Summary

This content explores a Kaggle competition supported by Google, focusing on evaluating how well Large Language Models (LLMs) can test their own knowledge, particularly in resource-constrained environments. The competition's dataset is generated by GPT-3.5, which creates multiple-choice science questions from Wikipedia snippets. The primary goal is to identify the correct answer among five options, with evaluation based on Mean Average Precision at three (MAP@3), meaning partial credit is given if the correct answer is among the top three guesses. The analysis emphasizes the importance of exploratory data analysis (EDA), including manual review of questions and answers, and using LLMs to automate parts of the EDA process. It also covers establishing baselines with GPT-3.5, discussing prompt engineering techniques like "time to think" to improve model performance, and considering the costs and concurrency challenges of querying external LLM APIs. The content concludes with a live coding session demonstrating data generation for fine-tuning, highlighting the complexities of mimicking the original data generation process.

Key takeaway

For Data Scientists and ML Engineers tackling new problems, prioritize thorough exploratory data analysis (EDA) before diving into complex model development. Your initial baseline should be a reasonable, runnable model like GPT-3.5, and you should leverage LLMs not just for final solutions but also to accelerate data generation and EDA itself. Be mindful of API costs and rate limits when using external LLMs, and consider prompt engineering techniques like "time to think" to improve model accuracy, even if it increases latency or cost.

Key insights

Effective ML problem-solving prioritizes exploratory data analysis and strategic LLM application over immediate complex modeling.

Principles

• Understand data generation processes.
• Establish strong baselines early.
• LLMs can accelerate data tasks.

Method

Explore data manually, then automate EDA with LLMs. Establish a GPT-3.5 baseline, improve with "time to think" prompts, and generate synthetic data for fine-tuning, carefully managing API costs and concurrency.

In practice

• Manually review data to uncover patterns.
• Use LLMs for automated data exploration.
• Generate synthetic data to expand training sets.

Topics

• LLM Evaluation
• Exploratory Data Analysis
• Prompt Engineering
• Synthetic Data Generation
• Mean Average Precision (MAP@3)

Best for: Machine Learning Engineer, Data Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Full Stack.