Translation-tailored Large language models (LLMs) exhibit remarkable
translation capabilities, even competing with supervised-trained commercial
translation systems. However, off-target translation remains an unsolved
problem, especially for low-resource languages, hindering us from developing
accurate LLMs-based translation models. To mitigate the off-target translation
problem and enhance the performance of LLMs on translation, recent works have
either designed advanced prompting strategies to highlight the functionality of
translation instructions or exploited the in-context learning ability of LLMs
by feeding few-shot demonstrations. However, these methods essentially do not
improve LLM's ability to follow translation instructions, especially the
language direction information. In this work, we design a two-stage fine-tuning
algorithm to improve the instruction-following ability (especially the
translation direction) of LLMs. Specifically, we first tune LLMs with the
maximum likelihood estimation loss on the translation dataset to elicit the
basic translation capabilities. In the second stage, we construct
instruction-conflicting samples by randomly replacing the translation
directions with a wrong one within the instruction, and then introduce an extra
unlikelihood loss to learn those samples. Experiments on IWSLT and WMT
benchmarks upon the LLaMA model spanning 16 zero-shot directions show that,
compared to the competitive baseline -- translation-finetuned LLama, our method
could effectively reduce the off-target translation ratio (averagely -53.3\%),
thus improving translation quality with average +5.7 SacreBLEU and +16.4
BLEURT. Analysis shows that our method could preserve the model's general task
performance on AlpacaEval. Code and models will be released at
https://github.com/alphadl/LanguageAware_Tuning.

通过两阶段微调算法，改进了大型语言模型（LLMs）遵循翻译指示的能力，特别是翻译方向信息，从而有效降低了错位翻译比率（平均降低 53.3%），提高了翻译质量（平均增加 5.7 SacreBLEU 和 16.4 BLEURT）。

构建准确的基于翻译的语言感知 LLMS 的指导调优

Building Accurate Translation-Tailored LLMs with Language Aware  Instruction Tuning

Hallucinations and off-target translation remain unsolved problems in machine
translation, especially for low-resource languages and massively multilingual
models. In this paper, we introduce methods to mitigate both failure cases with
a modified decoding objective, without requiring retraining or external models.
In source-contrastive decoding, we search for a translation that is probable
given the correct input, but improbable given a random input segment,
hypothesising that hallucinations will be similarly probable given either. In
language-contrastive decoding, we search for a translation that is probable,
but improbable given the wrong language indicator token. In experiments on
M2M-100 (418M) and SMaLL-100, we find that these methods effectively suppress
hallucinations and off-target translations, improving chrF2 by 1.7 and 1.4
points on average across 57 tested translation directions. In a proof of
concept on English--German, we also show that we can suppress off-target
translations with the Llama 2 chat models, demonstrating the applicability of
the method to machine translation with LLMs. We release our source code at
this https URL

机器翻译中的幻觉和目标脱靶翻译一直是未解决的问题，特别是对于低资源语言和大规模多语种模型。本文介绍了一种修改解码目标的方法，用于缓解这两种失败情况，而无需重新训练或使用外部模型。在实验证明，这些方法能有效抑制幻觉和目标脱靶翻译，在 57 个测试的翻译方向上平均提高了 1.7 和 1.4 个 chrF2 分数。在英 - 德的概念验证中，我们还展示了我们可以使用 Llama 2 聊天模型来抑制目标脱靶翻译，证明了该方法在 LLM 机器翻译中的可应用性。

使用源对比和语言对比解码减轻幻觉和目标外机器翻译

Mitigating Hallucinations and Off-target Machine Translation with  Source-Contrastive and Language-Contrastive Decoding

Multilingual Neural Machine Translation (NMT) enables one model to serve all
translation directions, including ones that are unseen during training, i.e.
zero-shot translation. Despite being theoretically attractive, current models
often produce low quality translations -- commonly failing to even produce
outputs in the right target language. In this work, we observe that off-target
translation is dominant even in strong multilingual systems, trained on massive
multilingual corpora. To address this issue, we propose a joint approach to
regularize NMT models at both representation-level and gradient-level. At the
representation level, we leverage an auxiliary target language prediction task
to regularize decoder outputs to retain information about the target language.
At the gradient level, we leverage a small amount of direct data (in thousands
of sentence pairs) to regularize model gradients. Our results demonstrate that
our approach is highly effective in both reducing off-target translation
occurrences and improving zero-shot translation performance by +5.59 and +10.38
BLEU on WMT and OPUS datasets respectively. Moreover, experiments show that our
method also works well when the small amount of direct data is not available.

本文提出了一种同时在表示层和梯度层面上正则化神经机器翻译（NMT）模型的方法，以解决多语言 NMT 在零 - shot 翻译中出现偏离目标语言及低质量翻译的问题，并在 WMT 和 OPUS 数据集上实现了 5.59 和 10.38 BLEU 的性能提升。