Just saw that MMLU benchmark floating around. DeepSeek Chat scored really high, close to GPT-4. That's impressive for an open model!
But in my job, I'm usually evaluating B2B SaaS for things like project management or support ticketing. How much does a good score on an academic test actually tell me? I care about consistent tone in responses, handling our specific data, and fitting into our workflows.
Has anyone tried using it for actual business tasks yet? Like summarizing meeting notes from a transcript or comparing vendor feature lists? I'm curious if the benchmark performance translates to real use-cases, or if it's more of a research win.
Learning every day
Your skepticism is warranted. MMLU measures broad knowledge retrieval, not the consistent application of logic or adherence to style guidelines you need for business workflows.
I've run a limited test on technical documentation summarization. While DeepSeek performed well on factual extraction from a provided transcript, its default tone was more expository than concise. For vendor comparisons, it can generate a feature matrix from input data, but hallucination rates on specific API details were slightly higher than GPT-4 in my trials, despite similar MMLU scores.
The benchmark indicates raw capability, but the translation to reliable business utility depends heavily on your specific prompt patterns and the need for deterministic output. It's a promising research win, but for B2B integration, you'd still need extensive scaffolding for data validation and output formatting.
data is the product
Totally get where you're coming from. When I'm looking at Jira or Confluence integrations, I need a model that follows my specific formatting rules, not just one that aces a general knowledge test.
That said, a good MMLU score does suggest it can understand complex instructions better, which is a start. I've had some luck using it to draft release notes from GitLab commit logs, but I still have to manually correct the tone to match our internal style. It's more of a powerful first draft tool for me right now.
For your use case on vendor comparisons, I'd be cautious. It might pull a great matrix from a webpage, but I've seen it confidently swap feature names between similar products. You'd need a very tight, fact-checking workflow behind it.