In long-form QA, LLM-based evaluators correlate with human judgments better than ROUGE or BERTScore, but they are biased by answer length, question type, self-reinforcement, and rare-word usage, and fine-grained prompting reduces some of these problems.
On The Evaluation of Machine Translation Systems Trained With Back-Translation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese. This is believed to be due to translationese inputs better matching the back-translated training data. In this work, we show that this conjecture is not empirically supported and that back-translation improves translation quality of both naturally occurring text as well as translationese according to professional human translators. We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs. BLEU cannot capture human preferences because references are translationese when source sentences are natural text. We recommend complementing BLEU with a language model score to measure fluency.
fields
cs.IR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
An Empirical Study of Evaluating Long-form Question Answering
In long-form QA, LLM-based evaluators correlate with human judgments better than ROUGE or BERTScore, but they are biased by answer length, question type, self-reinforcement, and rare-word usage, and fine-grained prompting reduces some of these problems.