TransClean: A Benchmark for Detecting and Extracting Clean Translations from Large Language Model Outputs

2026-09-10

AI Summary

本文针对大型语言模型翻译输出中的额外文本问题，通过分析噪声模式构建了TransClean基准，并评估了两种提取清洁翻译的方法。

Abstract

Large language models (LLMs) are increasingly used for machine translation, yet their outputs often contain additional text beyond the translation itself, such as language labels, explanations or bilingual repetitions, which we term translation noise. Despite its prevalence, this problem lacks dedicated benchmarks and systematic study. We analyze over 790,000 translation outputs from 12 LLMs across 22 language pairs (LPs) and identify 12 recurring noise patterns, which we group into formatting and content noise. Building on the observed patterns, we construct TransClean, a controlled benchmark of 9,900 pairs of noisy and clean translation outputs, comprising 8,800 synthetically generated instances and 1,100 manually curated authentic instances. We evaluate two extraction approaches on the TransClean benchmark: 1) a span-based extraction method leveraging translation quality estimation models for span detection, and 2) an LLM-based extraction method that prompts an LLM to isolate the translation. Our benchmark and analysis provide the first systematic framework to evaluate and improve the cleanliness of LLM translation outputs.

Problem

• Large Language Models
• Translation Noise
• Machine Translation

Innovation

• TransClean
• large language models
• translation noise
• benchmark
• extraction methods