A multi-step prompting method using keyword extraction, dictionary retrieval, and iterative self-checking yields modest and inconsistent BLEU gains for LLM translation.
Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation
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abstract
Recent studies have demonstrated a perceivable improvement on the performance of neural machine translation by applying cross-lingual language model pretraining (Lample and Conneau, 2019), especially the Translation Language Modeling (TLM). To alleviate the need for expensive parallel corpora by TLM, in this work, we incorporate the translation information from dictionaries into the pretraining process and propose a novel Bilingual Dictionary-based Language Model (BDLM). We evaluate our BDLM in Chinese, English, and Romanian. For Chinese-English, we obtained a 55.0 BLEU on WMT-News19 (Tiedemann, 2012) and a 24.3 BLEU on WMT20 news-commentary, outperforming the Vanilla Transformer (Vaswani et al., 2017) by more than 8.4 BLEU and 2.3 BLEU, respectively. According to our results, the BDLM also has advantages on convergence speed and predicting rare words. The increase in BLEU for WMT16 Romanian-English also shows its effectiveness in low-resources language translation.
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cs.CL 1years
2024 1verdicts
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Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach
A multi-step prompting method using keyword extraction, dictionary retrieval, and iterative self-checking yields modest and inconsistent BLEU gains for LLM translation.