Leveraging Concatenated Large Language Models for Efficient Machine Translation Across Languages

Core Concepts

By concatenating two distinct large language models specialized in the source and target languages, respectively, the proposed Relay Decoding (RD) method can effectively achieve superior machine translation performance without the high costs associated with continuous learning approaches.

Abstract

The paper presents an innovative approach called Relay Decoding (RD) to address the challenge of finding a single large language model (LLM) that can effectively handle both the source and target languages in machine translation tasks. The key idea is to concatenate two distinct LLMs, each specialized in one of the languages involved in the translation task, and utilize a simple mapping layer to facilitate the connection between them.
The authors first provide a task description, explaining the scenario where it is difficult to find a single LLM that can support both the source and target languages simultaneously. They then detail the RD approach, which involves:

Using the source language LLM to generate the hidden representation of the input sentence.
Projecting the hidden representation to the input space of the target language LLM using a mapping layer.
Concatenating the mapped representation with a prompt in the target language and feeding it into the target language LLM for decoding and generation.

The authors also explore the impact of finetuning the LLMs during the training process, finding that simultaneously finetuning the target language LLM yields better results.
Experiments conducted on the Multi30k and WikiMatrix datasets, using the LLaMA and Aquila2 models, demonstrate the effectiveness of the proposed RD method. The results show that RD outperforms the approach of finetuning a single LLM, achieving significant improvements of over 3 BLEU points in certain language pairs.
The paper also analyzes the amount of parallel data required to train the mapping layer, finding that approximately 60,000 data points are sufficient on the WikiMatrix dataset, which is considerably smaller compared to the dataset size typically needed by traditional bilingual methods.

Stats

The authors report the following key metrics:

On the Multi30k dataset, the RD method achieves BLEU scores of 27.36 for Zh-Fr, 17.87 for Zh-De, and 13.44 for Zh-Cs, outperforming the fine-tuning of single LLMs.
On the WikiMatrix dataset, the RD method achieves a BLEU score of 15.52 for Zh-Fr using a training set of 70,000 data points.

Quotes

"By incorporating a simple mapping layer to facilitate the connection between these two models and utilizing a limited amount of parallel data for training, we successfully achieve superior results in the machine translation task."
"Our concatenation method also surpasses the performance of fine-tuning with a single large model, demonstrating the need for pretraining large models on both the source and target languages to achieve better translation performance and this further validates the effectiveness of our proposed concatenation method."

Key Insights Distilled From

Relay Decoding: Concatenating Large Language Models for Machine Translation

by Chengpeng Fu... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02933.pdf

Deeper Inquiries

How can the proposed RD method be extended to handle more than two languages, allowing for multilingual translation

To extend the RD method for multilingual translation involving more than two languages, a systematic approach can be adopted. One way is to create a cascading model where multiple LLMs are concatenated in a sequential manner. For instance, if we have languages A, B, and C, we can first concatenate LLMs specialized in A and B using the RD method to create a hybrid model AB. Then, we can concatenate the AB model with an LLM specialized in language C. This cascading approach allows for the incorporation of multiple languages in the translation process. Additionally, techniques like hierarchical concatenation can be explored, where multiple LLMs are connected in a tree-like structure to handle a broader range of languages efficiently.

What techniques could be explored to further reduce the reliance on parallel data for training the mapping layer, such as leveraging monolingual data and back-translation

To reduce the reliance on parallel data for training the mapping layer in the RD method, several techniques can be considered:

Monolingual Data Augmentation: By leveraging monolingual data in both the source and target languages, data augmentation techniques like back-translation can be employed. This involves translating monolingual data from one language to another and then back to the original language, creating synthetic parallel data for training the mapping layer.
Unsupervised Learning: Exploring unsupervised learning methods such as adversarial training or self-training can help in training the mapping layer without the need for parallel data. These techniques focus on aligning the representations of the source and target languages in an unsupervised manner.
Multitask Learning: Incorporating multitask learning where the mapping layer is trained not only for translation but also for related tasks like language modeling or cross-lingual understanding can enhance its performance with limited parallel data.

Given the potential limitations of the LLMs used in the experiments, how might the RD method perform with more advanced or specialized LLMs for the source and target languages

With more advanced or specialized LLMs for the source and target languages, the RD method is likely to exhibit improved performance. Advanced LLMs with enhanced language understanding and translation capabilities can lead to more accurate and fluent translations. Specialized LLMs tailored specifically for certain language pairs can provide better alignment and context understanding, resulting in higher translation quality. Additionally, incorporating domain-specific LLMs trained on relevant corpora can further enhance the translation accuracy and fluency for specific domains or industries. Overall, utilizing state-of-the-art LLMs in the RD method is expected to yield superior translation results, especially when dealing with complex or specialized language pairs.