Read a random paper about LLMs and look at what it cites. Read those papers and look at what those cites. And so on. You'll soon figure out what the academic community consider the seminal papers in that field.
When I find a paper I'm interested in I usually follow the cites.
The last time I was interested in a topic (tree segmentation) I used elicit.org * and I found it really nice to find new papers.
* From the FAQ:
If you ask a question, Elicit will show relevant papers and summaries of key information about those papers in an easy-to-use table.
There's paperswithcode which has a ranking of sorts.
This is assuming you have access behind the research paper paywalls. Not everyone does and sci-hub doesn't always have access to recent papers.
I am working on a smart RSS reader that collects about 1000 articles on a good day from various sources including CS papers from arXiv. It selects about 300 articles (summary only) most days that i browse through a TikTok like interface (i judge one article at a time so I get valid negatives unlike the typing “learning to rank” problem). I can favorite an article to retrieve it later, say i like it to see more like it in the future but not save, or say i dislike it.
It is powered by transformer models and sbert.net, these are used to assign articles to 20 clusters generated daily, i see the top 15 from each cluster. This does a reasonable job of handling a diverse feed that includes CS abstracts, trade publication article, sports news, etc. I have high satisfaction in days that the system gets a lot of articles (peaks on Thorsday) but less on the weekends, sometimes I backfill high-scoring articles from last week then.
I tried using fine-tuned BERT-like models for classification and got them to equal the performance of the embedding-based system after a huge amount of work and a much longer training time. My problem is pretty noisy and there is some limit to how high i can get the AUC.