Time in a LLM reasoning engine

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:

  1. “Service has problems with auth for 10 min” or “Service is warming up now”
  2. 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.

Did you experiment with Chatgpt?

Yes, I finally did it. I’ve just finished my small investigation of this topic in ChatGPT.
Chat GPT (v4.0) do an amaizing job in writing summary of what is going on in a logs in general(for example, identify if there was a hacking attack attempt), but fail with more precise timing and order

I did a little bit of prompt engineering, asking model to return to me:

  • time range when event did happened
  • how long did it last.
  • did some events happened simultaneously, in parallel.
  • did one event happen before other event (e.g restart had happened before a server failure)

It did fail in most of these task. It could group events by date, but not by time.

Indeed, it can happen. Perhaps if you check our free courses in this site you may find some app that can further help, I haven’t checked them myself to be frank.