RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets

arXiv:2510.06143v2 Announce Type: replace Abstract: LLMs are powerful generators of synthetic data, which are used for training smaller, specific models. This is especially valuable for low-resource languages, where human-labelled data is scarce but LLMs can still produce high-quality text. However, LLMs differ in how useful their outputs are for training. Selecting the best LLM as a generator is challenging because extrinsic evaluation requires costly human annotations (which are often unavailable for low-resource languages), while intrinsic metrics correlate poorly with downstream performanc

Source: arXiv cs.CL — read the full report at the original publisher.

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