
A reading list worth keeping: LLM4Annotation
A practical post. The GitHub repo LLM4Annotation (Zhen-Tan-dmml/LLM4Annotation) is a curated, updated list of papers and tools at the intersection of LLMs and data annotation. If you work in this
Booth 21-25 | AI Data Management Zone | Tokyo Big Sight

A practical post. The GitHub repo LLM4Annotation (Zhen-Tan-dmml/LLM4Annotation) is a curated, updated list of papers and tools at the intersection of LLMs and data annotation. If you work in this

There was a point, not long ago, when the dominant strategy for improving large language models was simple: feed them more. More tokens, more compute, more parameters. The scaling laws made this feel almost like a law of physics — just add more and the model gets better. That era
