MT-PATCHER: Enhancing Machine Translation with Selective Knowledge Distillation from Large Language Models

Core Concepts

MT-PATCHER enhances machine translation by selectively distilling knowledge from large language models to improve translation performance.

Abstract

MT-PATCHER proposes a framework for transferring translation knowledge from large language models (LLMs) to existing machine translation (MT) models in a selective, comprehensive, and proactive manner. The method involves identifying and correcting translation errors in student MT models instead of distilling the entire translation from the teacher model. By leveraging the strong language abilities of LLMs, diverse contexts are synthesized to improve translation performance on unseen contexts and words. Experimental results demonstrate that finetuning the student MT model on a small percentage of examples can achieve comparable results to traditional knowledge distillation methods. Additionally, synthesizing potential errors and diverse contexts further enhances translation performance.

Stats

Finetuning the student model on about 10% examples can achieve comparable results to traditional knowledge distillation methods.
Synthesized potential errors and diverse contexts further improve translation performances on unseen contexts and words.
The backbone LLMs used for building MT-PATCHER are LLaMA2-13B and Baichuan-2-13B.

Quotes

"Considering the current translation ability of student MT models, we only identify and correct their translation errors."
"Leveraging the strong language abilities of LLMs, we instruct LLM teachers to synthesize diverse contexts and anticipate more potential errors for the student."
"Experiment results show that finetuning the student model on only 10% examples selected by MT-PATCHER is equivalent to finetuning on all examples as in KD."

Key Insights Distilled From

MT-PATCHER

by Jiahuan Li,S... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09522.pdf

Deeper Inquiries

How can MT-PATCHER address other types of translation errors beyond those related to vocabulary?

MT-PATCHER can address various types of translation errors beyond vocabulary-related issues by leveraging the feedback mechanism and context synthesis capabilities. The framework can identify errors in sentence structure, grammar, coherence, and style by analyzing the discrepancies between the student model's translations and the corrections provided by the feedbacker (LLM). By synthesizing diverse contexts and generating parallel data with error-correction pairs, MT-PATCHER can help improve the student model's understanding of different linguistic nuances. Additionally, incorporating a word analoger component allows for proactive prediction of potential errors on unseen words or concepts, further enhancing the student model's translation accuracy across various language phenomena.

What are the implications of using GPT-4 as an evaluator in experiments?

Using GPT-4 as an evaluator in experiments has several implications:

Consistency: GPT-4 provides consistent evaluations across multiple examples without human bias or fatigue.
Efficiency: It speeds up evaluation processes significantly compared to manual assessments.
Scalability: Enables scalability for large-scale experiments due to its automated evaluation capabilities.
Reliability: While not perfect, GPT-4 evaluations have been shown to correlate well with human judgments in many NLP tasks.
Standardization: Provides a standardized evaluation metric that ensures uniformity in assessing translation quality.

However, it is essential to acknowledge that while GPT-4 offers valuable insights and efficiency benefits, it may still have limitations such as occasional inaccuracies or biases inherent in any AI system.

How scalable is MT-PATCHER across different languages or domains beyond Chinese-to-English translations?

MT-PATCHER shows promise for scalability across different languages and domains beyond Chinese-to-English translations due to its underlying principles rather than specific language dependencies:

Transferable Framework: The methodology behind MT-PATCHER - selective knowledge distillation from LLMs - is applicable irrespective of language pairs.
Adaptability: The pipeline involving feedback analysis, context synthesis, and error anticipation can be tailored to suit diverse linguistic structures and vocabularies.
Generalizability: By focusing on improving general translation abilities through targeted error correction strategies rather than language-specific rules, MT-PATCHER can be adapted effectively to new languages or domains.
Performance Consistency: While performance may vary based on language complexity or dataset availability, the core approach remains robust for knowledge transfer.

Overall, with proper adjustments and fine-tuning based on specific linguistic characteristics per language pair or domain requirements,
MT-PATCHER holds significant potential for scalability across diverse languages outside Chinese-to-English translations as well as varied text genres within those languages.