onPanda cuts median annotation time by 51.5% with locate-correct-continue token-level annotation, and releases the Panda-CVL dataset and benchmark
The work presents onPanda: while reading a model response, the annotator locates the first inappropriate token, either picks a substitute from the model's candidate tokens or types the correct text, and the system truncates everything after that position and continues generation from the corrected prefix, repeating this loop; a small controlled study suggests a 51.5% reduction in median annotation time over manual post-editing, 97.0% of tokens in the resulting data are model-generated, and the data carries position-precise, naturally paired positive-negative token-level correction supervision, alongside the released Chinese vision-language Panda-CVL dataset and a token-level correction benchmark.