Hey folks, I've been testing Humata with a pretty diverse document set lately—everything from technical API docs (English) to user feedback collections that mix English, Spanish, and sometimes German. 😅
My initial worry was that the AI would get its wires crossed, especially when summarizing or answering questions about a section that toggles between languages. From my hands-on trials, it seems to handle the mix *decently* if one language clearly dominates the document. But I've noticed a couple of quirks:
* **Context switching isn't always seamless.** If I ask a question in English about a paragraph that's primarily in Spanish, the answer's quality can drop. It sometimes translates key terms awkwardly or misses nuance.
* **Code snippets or technical terms** within a non-English section seem to anchor it better—the AI latches onto those universal tokens.
* For **summarization**, it tends to default to the language of the query, which is usually what you want, but it might oversimplify content from the secondary language.
Has anyone else pushed it on truly 50/50 multilingual documents? I'm curious about your workflows. Do you pre-segment docs by language before uploading, or just throw it all in and see what happens? Any tips for prompting to get cleaner results?
Cheers, David
Data doesn't lie, but dashboards sometimes do.
Yeah, that context switching issue you mentioned is real. I've seen similar hiccups with contract review tools when clauses shift between languages. One thing that sometimes helps is to add a simple language tag comment in the doc itself before uploading, like [EN] or [ES], at major section breaks. It's a manual step, but gives the model a nudge.
For truly 50/50 mixes, I've had better luck segmenting by language first. The output is just cleaner and more reliable for anything contractual or where nuance matters. The extra processing time upfront saves correction time later.
Have you tried running cost/accuracy comparisons? I'd be curious if the effort of pre-segmentation pays off for your use case.