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Wait... judging by their own press release, this thing looks terrible. They didn't benchmark it on finance-specific tasks against GPT-4, or even GPT-3.5, just a
by Michelangelo11 4y ago
Wait... judging by their own press release, this thing looks terrible. They didn't benchmark it on finance-specific tasks against GPT-4, or even GPT-3.5, just a bunch of more-or-less random also-ran models. It beat those models, but that's not saying much at all, and the fact that they didn't benchmark it against any OpenAI model in finance-specific tasks speaks volumes.
(They did benchmark it against GPT-3 in general-purpose tasks and, unsurprisingly, GPT-3 came out on top.)
Press release link: https://www.bloomberg.com/company/press/bloomberggpt-50-billion-parameter-llm-tuned-finance/ https://www.bloomberg.com/company/press/bloomberggpt-50-bill...
- mdredze 4y agoHi! I'm a co-author on the paper (Mark Dredze). We didn't compare to GPT-4, or any instruction tuned model. We're comparing a causal LM pre-trained only (BloombergGPT) to other causal LM pre-trained only models. You need to compare like to like. Would an instruction tuned model work better? Sure! I would hope so. That seems like a good next step to explore. It's also difficult to make scientific comparisons to models that we know nothing about. I don't know what data GPT-4 or GPT-3-5-turbo used for training, how many parameters they have, etc. We can't make any scientific conclusions when comparing two systems if we don't know anything about one of the systems. We compared to GPT-3 results when they were available. Does GPT-3 do better on general purpose datasets? Yes, as we expected it would. It's a much larger model (~3.5 times the size). No surprise there. If anything, the surprise was how well bloombergGPT did against these larger models, beating them on some datasets.