Does anyone knows how good could be LLM in writing summaries, by connecting some text events with time?
Example:
Assume, I have a huge amount of logs in OpenSearch to train LLM with short summary from operators. At some point, I want to load some piece of logs (e.g Warnings only) and get a short summary:
- “Service has problems with auth for 10 min” or “Service is warming up now”
- Or even just classification “service is Healthy/Unhealthy since 14:23:00”.
It turns out, order and timing in such classification are really important. Do you know any models that could be good with a such kind of tasks?
Question is similar to /llm-for-timeseries-data/713248, but here we talking about mix of time and text.
Edit 1: I can, of course upload logs in batches and write a summary as an aggregation of those batches: e.g batch #5 that have logs since 14:23:00 classified as “unhealthy”, after four “healthy” batches and I make a conclusion that service is not OK since 14:23:00. But I would love to have it done for me automatically, if possible.