Public articles linked to the same research event.
arXiv The work introduces RT-SFT: neural MT roundtrip translation through a pivot language serves as a large-scale, task-training-free style-stripping normalizer, converting a monolingual in-style corpus into a pseudo-parallel corpus on which an instruction-tuned LLM is LoRA-finetuned as the stylizer, with the same normalizer applied to test queries to keep the stylizer in-distribution; across four style domains RT-SFT outperforms state-of-the-art methods such as few-shot in-context learning by considerable margins, and the paper also reports effective retrieval augmentation for expert style domains with strict terminology and naming conventions.
The work introduces RT-SFT: neural MT roundtrip translation through a pivot language serves as a large-scale, task-training-free style-stripping normalizer, converting a monolingual in-style corpus into a pseudo-parallel corpus on which an instruction-tuned LLM is LoRA-finetuned as the stylizer, with the same normalizer applied to test queries to keep the stylizer in-distribution; across four style domains RT-SFT outperforms state-of-the-art methods such as few-shot in-context learning by considerable margins, and the paper also reports effective retrieval augmentation for expert style domains with strict terminology and naming conventions.
The work introduces RT-SFT: neural MT roundtrip translation through a pivot language serves as a large-scale, task-training-free style-stripping normalizer, converting a monolingual in-style corpus into a pseudo-parallel corpus on which an instruction-tuned LLM is LoRA-finetuned as the stylizer, with the same normalizer applied to test queries to keep the stylizer in-distribution; across four style domains RT-SFT outperforms state-of-the-art methods such as few-shot in-context learning by considerable margins, and the paper also reports effective retrieval augmentation for expert style domains with strict terminology and naming conventions.
The work introduces RT-SFT: neural MT roundtrip translation through a pivot language serves as a large-scale, task-training-free style-stripping normalizer, converting a monolingual in-style corpus into a pseudo-parallel corpus on which an instruction-tuned LLM is LoRA-finetuned as the stylizer, with the same normalizer applied to test queries to keep the stylizer in-distribution; across four style domains RT-SFT outperforms state-of-the-art methods such as few-shot in-context learning by considerable margins, and the paper also reports effective retrieval augmentation for expert style domains with strict terminology and naming conventions.