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
- minpts-check: one-run predictor tells if your HDBSCAN clusters survive min_samples changes — leland_mcinnes · 2026-11-12
- Architecture study sparks monitoring concerns over a multi-hop planning spike — ChowdhuryNeil · 2026-11-12
- SGLang Megakernel Pushes Muse Spark to 1,109 tok/s, 2.1x Over Baseline — hsu_byron · 2026-11-12
- ArcGrav: a cross-model Agent Control Language to cut token overhead — ArchitecturalGravity · 2026-11-12
- Open-source dual-arm robot solves tangram puzzles with 90 demos, ~60% success — DanGrover · 2026-11-12
- FutureHouse's deslopper trains Kosmos to write indistinguishably from human experts — proteinrosh · 2026-11-12