Booth 21-25 | AI Data Management Zone | Tokyo Big Sight
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
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 space, it’s worth bookmarking. What’s in it: The reason it’s worth following — beyond the obvious “saves you a few
