Bloomberg is bringing to finance what GPT and ChatGPT brought to everyday general-purpose chatbots. Last week, the company announced it had built something called BloombergGPT.
How big is BloombergGPT?
Well, the company says it was trained on a corpus of more than 700 billion tokens (or word fragments). For context, GPT-3, released in 2020, was trained on about 500 billion. (OpenAI has declined to reveal any equivalent number for GPT-4, the successor released last month, citing “the competitive landscape.”)
Of the 700 million-plus tokens, 363 billion are taken from Bloomberg’s own financial data, the sort of information that powers its terminals — “the largest domain-specific dataset yet” constructed, it says. Another 345 billion tokens come from “general purpose datasets” obtained from elsewhere.
Read more: Salesforce latest tech powerhouse to join ChatGPT mania
What can BloombergGPT do?
Because it shares a training base with other LLMs (large language models), BloombergGPT can do the sorts of things that we’ve come to expect from ChatGPT and similar models. But it can also perform tasks more tightly connected to Bloomberg’s needs. It can translate natural language requests (“Apple and IBM market cap and eps or earnings per share”) into the Bloomberg Query Language terminal.
It’s also better tuned, they say, to answer specific business-related questions, whether they be sentiment analysis, categorization, data extraction, or something else entirely. (“For example, it performs well at identifying the CEO of a company.”)
Impacting fintech
Although it is yet to be seen how BloombergGPT will impact the fintech industry, some of the potential uses of BloombergGPT might include:
- Generate an SEC filing using data and ChatGPT, potentially reducing cost.
- Providing a company chart and executive-level structure.
- Automation of generation of draft routine market reports and summaries for clients
- Retrieval of specific elements of financial statements for specific periods via a single prompt
The use of BloombergGPT to assess market sentiment, decipher news headlines, create financial papers, and even make trading judgments, may soon become commonplace.
Wonderful, but at what cost?
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