Enhancing Machine Translation with Large Language Models through Preference-based Learning

Core Concepts

Leveraging preference learning to steer large language models towards generating high-quality translations by aligning their generation probability with human preferences.

Abstract

The content discusses a framework for enhancing the translation capabilities of large language models (LLMs) by incorporating preference learning. The key insights are:

Supervised fine-tuning (SFT) on parallel data can train LLMs to imitate reference translations, but this approach is vulnerable to noise in the data and often reaches a performance plateau.

To overcome this limitation, the authors propose a preference-based approach built upon the Plackett-Luce model. The objective is to steer LLMs towards a more nuanced understanding of translation preferences from a holistic view, while also being more resilient in the absence of gold translations.

The authors construct a dataset called MAPLE, which includes multiple translations of varying quality for each source sentence, with each translation assigned a human preference score by professional translators.

Extensive experiments demonstrate the superiority of the preference learning approach in "breaking the plateau" across diverse LLMs and test settings. The authors' in-depth analysis underscores the pivotal role of diverse translations and accurate preference scores in the success of their approach.

The authors also show that the MAPLE dataset can be reused to improve the translation performance of other LLMs, beyond just the target model.

Stats

"Through supervised fine-tuning (SFT) using a small amount of parallel data, LLMs demonstrate the capability to compete with established commercial translation services such as Google Translate, particularly in high-resource languages."
"The noise can stem not only from the lack of attention by annotators, but also from the inherent challenge of achieving perfect translations due to the intricate interplay of language, culture, and vocabulary."
"Further increasing the volume of parallel translations typically yields minimal additional benefits, and may instead impair the translation capabilities of LLMs."

Quotes

"To alleviate aforementioned limitation of SFT, endeavors have been made to provide LLMs with holistic assessment of contrasting examples rather than token-level imitations."
"We build a dataset, which we refer to as MAPLE, to facilitate preference learning. It equips each source sentence with five translations in diverse quality, scored by professional translators."
"Extensive experiments demonstrate the superiority of our approach in "breaking the plateau" across diverse LLMs and test settings. Our in-depth analysis underscores the pivotal role of diverse translations and accurate preference scores in the success of our approach."

Key Insights Distilled From

A Preference-driven Paradigm for Enhanced Translation with Large Language Models

by Dawei Zhu,So... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11288.pdf

Deeper Inquiries

How can the preference learning framework be extended to low-resource language pairs where LLMs may not exhibit strong baseline performance?

In the context of low-resource language pairs, where LLMs may not have strong baseline performance, the preference learning framework can be extended by incorporating techniques to address the specific challenges faced in such scenarios. Here are some strategies to extend the preference learning framework to low-resource language pairs:

Data Augmentation: In low-resource settings, it may be challenging to obtain a large amount of parallel data for training LLMs. Data augmentation techniques, such as back-translation or synthetic data generation, can be employed to increase the amount of training data available for preference learning. This can help in improving the model's understanding of translation preferences even with limited parallel data.

Transfer Learning: Transfer learning techniques can be utilized to leverage pre-trained models on high-resource languages and fine-tune them on low-resource language pairs. By transferring knowledge from high-resource languages, the model can benefit from the learned representations and improve its performance on low-resource languages.

Active Learning: In low-resource scenarios, active learning can be employed to intelligently select the most informative samples for annotation. By actively selecting samples that are most beneficial for improving the model's performance, the annotation process can be optimized, making the best use of limited resources.

Domain Adaptation: Low-resource language pairs often face domain-specific challenges. Domain adaptation techniques can be applied to adapt the model to the specific domain of the low-resource language pair, improving its performance in domain-specific contexts.

Semi-Supervised Learning: Semi-supervised learning methods can be used to leverage both labeled and unlabeled data in low-resource settings. By incorporating unlabeled data along with preference annotations, the model can learn from a larger pool of data and improve its performance.

How can the potential challenges and limitations in scaling up the human annotation process for the preference dataset be addressed?

Scaling up the human annotation process for the preference dataset can pose several challenges and limitations. Here are some strategies to address these challenges:

Crowdsourcing: Utilizing crowdsourcing platforms can help in scaling up the human annotation process cost-effectively. By distributing the annotation tasks to a larger pool of annotators, the workload can be divided, and annotations can be completed more efficiently.

Quality Control: Implementing quality control measures, such as annotator training, guidelines, and regular checks for inter-annotator agreement, can help maintain the quality of annotations even at scale. Ensuring consistency and accuracy in annotations is crucial for the effectiveness of the preference learning framework.

Automation: Implementing automation tools for certain aspects of the annotation process, such as data preprocessing, can help in speeding up the annotation process. Automated tools can assist annotators in tasks that do not require human judgment, allowing them to focus on more complex annotation tasks.

Iterative Annotation: Adopting an iterative annotation process where annotations are reviewed and refined iteratively can help in improving the quality of annotations over time. Feedback loops and continuous improvement mechanisms can enhance the overall quality of the preference dataset.

Collaboration: Collaborating with domain experts and researchers in the field of machine translation can provide valuable insights and guidance for scaling up the annotation process. Leveraging expertise from multiple sources can lead to more comprehensive and accurate annotations.

How can the insights from this work on preference-driven machine translation be applied to other language generation tasks beyond translation, such as dialogue systems or text summarization?

The insights from preference-driven machine translation can be applied to other language generation tasks beyond translation, such as dialogue systems or text summarization, in the following ways:

Quality Assessment: Preference learning can be used to assess the quality of generated outputs in dialogue systems or text summarization. By collecting human preferences on different generated responses or summaries, the model can be trained to prioritize high-quality outputs based on human judgment.

Diverse Outputs: Similar to translation, generating diverse responses in dialogue systems or summaries can be crucial for capturing different aspects of the input. Preference learning can guide the model to produce diverse and high-quality outputs by learning from human preferences on multiple generated samples.

Fine-tuning Models: Just as in machine translation, preference learning can be used to fine-tune language models for dialogue systems or text summarization tasks. By incorporating human preferences into the training process, the model can learn to generate more accurate and contextually relevant responses or summaries.

Domain-specific Adaptation: Preference learning can help in domain-specific adaptation of language models for dialogue systems or text summarization. By collecting preferences from domain experts or target users, the model can be tailored to generate outputs that align with domain-specific requirements and preferences.

Iterative Improvement: Applying preference learning in an iterative manner can lead to continuous improvement in the quality of generated outputs in dialogue systems or text summarization. By incorporating feedback from users or annotators, the model can adapt and refine its generation capabilities over time.