Qwen researcher: human annotation may be noisier than auto-labeling
antoine_chaffin · x · 2026-09-20
Antoine Chaffin, a multimodal researcher on the Qwen team, argues that recent "human annotated dataset" papers reveal human labels are often noisier and more error-prone than advanced automatic labeling. What works, he says, is iteration: let agents iterate repeatedly with human-readable summaries, potentially paired with human annotators.
Related event: Qwen researcher says human-labeled data may be noisier than auto-labeling(2 posts)→
More from Research
- Textbook author: 99.9% accuracy can mean zero scientific discoveries — bravo_abad · 2026-09-20
- rasbt: Jev's Secret Sauce Is Data, Not the Algorithm — Laya Rival Falls Far Short — RichmanRonald · 2026-09-20
- Sebastian Raschka open-sources an end-to-end 'AI text detector from scratch' project — rasbt · 2026-09-20
- Sebastian Raschka: restricting LLM outputs to an action space is just a classic encoder classifier — rasbt · 2026-09-20
- Follow-up: Link to Larry Wasserman's 2012 Solution for Navigating the Paper Flood — maksym_andr · 2026-09-20
- ICLR's 50k+ Submissions Spark Debate: Researcher Says More AI Research Is Worth Celebrating — maksym_andr · 2026-09-20