Exploring Pre-training & Post-training Strategies for LLMs: Importance of High-Quality, Efficiently Formatted Data

Join Noah and Chun as they discuss the importance of data infrastructure in developing AI models, sharing their experiences with Lance TV, a database for multimodal AI that enables fast scans, random access, and indexing extensions.

1. Chunsa is the CEO and co-founder of Lance TV, and has been creating data tools for data science and machine learning for two decades.
2. Noah leads the AI data platform at Character.ai, a leading personalized AI platform.
3. Both companies focus on training their own foundation models and care about what they train on, giving it a nice format that does many useful things for it.
4. Pre-training involves thinking wider about domains (books or chat data), while post-training focuses on specific tasks and how difficult they are.
5. Favorite current research areas include data efficient learning, sampling from data, measuring diversity, clean data, evaluations, data set management, and analytics.
6. Data efficient learning aims to reduce the amount of data needed for good results.
7. Sampling metrics and diversity measurement are important in industry and academic papers.
8. Clean data is always the starting point, followed by evaluations and dataset management.
9. Analytics help understand data collections, including token counts, length, and complexity of code data.
10. Reading data has been the biggest win for understanding performance and what's going on.
11. Using language models to improve language models includes synthetic data, quality scoring, data set selection, and data set augmentation.
12. Data set selection matches distributions between desired behavior and available data through retrieval or clustering.
13. Synthetic data generation can be done quickly using existing Big Data tools and GPU backends for prompting, embedding, and classifying things.
14. Human labeling is used to improve classifiers and rewrite synthetic data with issues.
15. Land's platform tooling aims to make research acceleration easier by separating concerns in data materialization and training jobs.
16. LanCE format allows for quick random access, shuffling references rather than rows, and saves time and iteration speed.
17. AI workloads require fast scans, fast random access, and dealing with large binary data to stream directly into GPUs.
18. Existing data formats and infrastructure are good for at most two of the three properties required in AI workloads.
19. Lance format is a columnar file format optimized for AI, giving fast scans like Parquet and supporting fast lookups.
20. LanCE format is also a lightweight table format that automatically versions data, making it easy to add experimental features and roll them back later.
21. Indexing extensions in Lance format allow for billion-scale Vector search directly from S3, reducing the need for Elasticsearch clusters.
22. LanCE format enables a single table for many different workloads, including metadata columns, time series columns, large blobs, and tensors.
23. Lance DB is a vector database that provides distributed Vector search with billions of vectors at low latency and very high QPS.
24. Lance DB is designed to handle multimodal data needs for features, audio waveforms, images, vectors, metadata columns, time series columns, and other workloads.

Source: AI Engineer via YouTube

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