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
Most improvements to AI systems happen before deployment: better training data, better fine-tuning, better RLHF. Once the model is out in the world, you generally get the performance you trained for and nothing better. A paper from early 2026 takes a different approach. Instead of trying to bake everything into
