Contemporary translation engines built upon the encoder-decoder framework
have reached a high level of development, while the emergence of Large Language
Models (LLMs) has disrupted their position by offering the potential for
achieving superior translation quality. Therefore, it is crucial to understand
in which scenarios LLMs outperform traditional NMT systems and how to leverage
their strengths. In this paper, we first conduct a comprehensive analysis to
assess the strengths and limitations of various commercial NMT systems and
MT-oriented LLMs. Our findings indicate that neither NMT nor MT-oriented LLMs
alone can effectively address all the translation issues, but MT-oriented LLMs
can serve as a promising complement to the NMT systems. Building upon these
insights, we explore hybrid methods and propose Cooperative Decoding (CoDec),
which treats NMT systems as a pretranslation model and MT-oriented LLMs as a
supplemental solution to handle complex scenarios beyond the capability of NMT
alone. The results on the WMT22 test sets and a newly collected test set
WebCrawl demonstrate the effectiveness and efficiency of CoDec, highlighting
its potential as a robust solution for combining NMT systems with MT-oriented
LLMs in machine translation.

基于编码器 - 解码器框架的当代翻译引擎发展迅猛，而大型语言模型的出现破坏了它们的地位，提供了实现更优翻译质量的潜力。因此，了解 LLMs 是如何在何种场景下胜过传统 NMT 系统，以及如何发挥其优势，变得至关重要。本文首先进行了全面分析，评估了各种商业 NMT 系统和面向机器翻译的 LLMs 的优势和局限性。发现 NMT 系统和面向机器翻译的 LLMs 都不能有效解决所有翻译问题，但面向机器翻译的 LLMs 可以作为 NMT 系统的有希望的补充。基于这些发现，我们探索了混合方法，并提出了合作解码（CoDec）将 NMT 系统视为预翻译模型，将面向机器翻译的 LLMs 视为处理 NMT 系统无法覆盖的复杂场景的补充解决方案。在 WMT22 测试集和新收集的 WebCrawl 测试集上的结果表明，CoDec 的有效性和效率，突出了将 NMT 系统与面向机器翻译的 LLMs 相结合作为强大解决方案的潜力。

利用大语言模型提升机器翻译：基于合作解码的初步研究

Improving Machine Translation with Large Language Models: A Preliminary  Study with Cooperative Decoding

Language models generate texts by successively predicting probability
distributions for next tokens given past ones. A growing field of interest
tries to leverage external information in the decoding process so that the
generated texts have desired properties, such as being more natural, non toxic,
faithful, or having a specific writing style. A solution is to use a classifier
at each generation step, resulting in a cooperative environment where the
classifier guides the decoding of the language model distribution towards
relevant texts for the task at hand. In this paper, we examine three families
of (transformer-based) discriminators for this specific task of cooperative
decoding: bidirectional, left-to-right and generative ones. We evaluate the
pros and cons of these different types of discriminators for cooperative
generation, exploring respective accuracy on classification tasks along with
their impact on the resulting sample quality and computational performances. We
also provide the code of a batched implementation of the powerful cooperative
decoding strategy used for our experiments, the Monte Carlo Tree Search,
working with each discriminator for Natural Language Generation.

本文研究语言模型在生成自然文本时采用协同解码策略，使用三种基于 Transformer 的分类器，并提供用于实验的基于 Monte Carlo Tree Search 的批量实现代码，比较它们在分类准确性、样本质量和计算性能等方面的优缺点。