Improving Neural Machine Translation by Ensembling Large Language Models and Dedicated Translation Models

Core Concepts

Ensembling a neural machine translation (NMT) model with a prompted large language model (LLM) can improve translation quality, even when the LLM is weaker at translation than the NMT model.

Abstract

The authors propose an on-the-fly ensembling method that combines a neural machine translation (NMT) model with a large language model (LLM) prompted for translation. Through experiments on four language pairs with varying data amounts, they find that:

A slightly weaker-at-translation LLM can improve translations of an NMT model.
Such an ensemble can produce better translations than ensembling two stronger NMT models.
The ensemble method can be combined with various LLM prompting techniques, such as in-context learning and translation context.

The authors demonstrate that the ensemble approach leverages the complementary strengths of the NMT and LLM models. While NMT models are trained specifically for the translation task, LLMs are exposed to more diverse data and can be prompted with auxiliary information. The ensemble allows the models to complement each other's capabilities.
The authors explore the performance of the ensemble method in high and low resource settings, and compare it to ensembling two NMT models. They find that the LLM-NMT ensemble outperforms the NMT-NMT ensemble, even when the LLM is slightly weaker at translation than the NMT model. This suggests that simply choosing the two highest quality models is not sufficient for optimal ensembling, and that the diversity of the models' training and capabilities is an important factor.
The authors also investigate the impact of prompting the LLM with domain-specific information and document-level context. They find that prompting can improve the LLM's translation quality, and that the ensemble with document-level context outperforms all other variants, including the standalone NMT model.

Stats

The parallel training data for German and Russian is from the WMT22 shared task, and the Hausa data is from WMT21.
The Turkish training data includes additional data from OPUS, excluding Paracrawl.
The authors use domain-specific test sets such as TED-100 and ParaPat, as well as the CTXPro test suite for document-level evaluation.

Quotes

"We propose on-the-fly ensembling of a neural machine translation (NMT) model with a large language model (LLM), prompted on the same task and input."
"We demonstrate that our ensemble method can be combined with various techniques from LLM prompting, such as in context learning and translation context."
"We find that a slightly weaker-at-translation LLM can improve translations of a NMT model, and such an ensemble can produce better translations than ensembling two stronger NMT models."

Key Insights Distilled From

On-the-Fly Fusion of Large Language Models and Machine Translation

by Hieu Hoang,H... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2311.08306.pdf

Deeper Inquiries

How can the ensemble method be extended to other NLP tasks beyond machine translation?

In the context of NLP tasks beyond machine translation, the ensemble method can be applied by combining the strengths of different models to improve overall performance. For tasks like sentiment analysis, text summarization, named entity recognition, or question answering, ensembling can involve combining multiple models such as large language models (LLMs), neural networks, or even traditional machine learning algorithms. Each model may excel in different aspects of the task, such as fluency, accuracy, or domain-specific knowledge. By ensembling these models, the ensemble can benefit from the diverse strengths of each individual model.
Ensembling can be extended to other NLP tasks by following a similar approach as in machine translation. First, identify a set of diverse models that perform well on the specific task. Then, determine the optimal way to combine their outputs, such as through weighted averaging, stacking, or voting mechanisms. Additionally, techniques like prompt engineering, in-context learning, or domain-specific prompting can be utilized to enhance the performance of the ensemble on specific tasks. Regular evaluation and fine-tuning of the ensemble method are crucial to ensure optimal performance across various NLP tasks.

What are the potential drawbacks or limitations of relying on large language models for critical applications, and how can they be addressed?

While large language models (LLMs) have shown remarkable performance across various NLP tasks, there are several drawbacks and limitations associated with relying solely on them for critical applications:

Computational Resources: Training and fine-tuning LLMs require significant computational resources, making them expensive to deploy and maintain.

Data Privacy Concerns: LLMs trained on large datasets may inadvertently memorize sensitive information, posing privacy risks when used in critical applications.

Bias and Fairness: LLMs can inherit biases present in the training data, leading to biased outputs that may impact decision-making in critical applications.

Interpretability: LLMs are often considered black-box models, making it challenging to interpret their decisions, which is crucial for critical applications where transparency is required.

Domain Adaptation: LLMs may struggle with domain-specific tasks or low-resource languages, limiting their effectiveness in certain critical applications.

To address these limitations, several strategies can be implemented:

Regular Auditing: Conduct regular audits to identify and mitigate biases in LLMs.
Data Augmentation: Augment training data to improve model robustness and reduce overfitting.
Model Compression: Implement techniques like distillation to reduce the size and complexity of LLMs while maintaining performance.
Hybrid Models: Combine LLMs with task-specific models to leverage the strengths of both approaches.
Ethical Guidelines: Establish ethical guidelines for the development and deployment of LLMs in critical applications to ensure fairness and transparency.

How might the ensemble approach be adapted to handle low-resource languages or domains where both the NMT and LLM models perform poorly?

Adapting the ensemble approach to handle low-resource languages or domains where both Neural Machine Translation (NMT) and Large Language Models (LLMs) perform poorly requires a tailored strategy to leverage the strengths of each model effectively. Here are some ways to adapt the ensemble approach in such scenarios:

Data Augmentation: Utilize data augmentation techniques to generate synthetic data for low-resource languages, improving the training data quality for both NMT and LLM models.

Transfer Learning: Pre-train models on a resource-rich language and then fine-tune them on the low-resource language data to improve performance in the target domain.

Domain Adaptation: Incorporate domain-specific knowledge or prompts during training to enhance the models' understanding of the target domain, improving translation quality.

Ensemble Diversity: Include a diverse set of models, including task-specific models, traditional machine learning algorithms, or rule-based systems, in the ensemble to capture a broader range of linguistic patterns and domain knowledge.

Active Learning: Implement active learning strategies to selectively label and train on the most informative data points, optimizing the model's performance in low-resource settings.

Hybrid Models: Combine the ensemble approach with hybrid models that integrate both NMT and LLM components, allowing for a more nuanced and context-aware translation in challenging language or domain contexts.

By incorporating these strategies, the ensemble approach can be effectively adapted to address the challenges posed by low-resource languages or domains where individual NMT and LLM models may struggle to perform optimally.