Today we’re joined by David Rosenberg, head of the machine learning strategy team in the Office of the CTO at Bloomberg. In our conversation with David, we discuss the creation of BloombergGPT, a custom-built LLM focused on financial applications. We explore the model’s architecture, validation process, benchmarks, and its distinction from other language models. David also discussed the evaluation process, performance comparisons, progress, and the future directions of the model. Finally, we discuss the ethical considerations that come with building these types of models, and how they've approached dealing with these issues.
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📖CHAPTERS
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00:00 - Intro
02:47 - Introducing BloombergGPT
06:20 - Focus on numerical data tokenization
07:57 - Challenges in training large models and improving performance
17:38 - Focusing on experimentation for small changes.
25:30 - For commercial use, to build a model or to fine-tune?
28:49 - Model architecture and infrastructure flexibility
33:47 - Training and instruction tuning
38:34 - Investment in small-scale experiments for models
40:39 - Researching natural language models for varied uses
43:37 - Ethical concerns and considerations
🔗 LINKS & RESOURCES
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Paper: BloombergGPT: A Large Language Model for Finance - arxiv.org/abs/2303.17564
BloombergGPT Blog Post - www.bloomberg.com/company/pre...
For a complete list of references, head over to twimlai.com/go/639.
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23 июл 2024
