Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation

ICLR 2025 Conference Submission13856 Authors

Keywords: Large Language Model, Continual Instruction Tuning, Machine Translation

TL;DR: This paper propose to RaDis, which distilles self-generated rationales to improve LLMs' translation proficiency without inducing forgetting of general capabilities.

Abstract: Large Language Models (LLMs) have achieved impressive results across numerous NLP tasks but still encounter difficulties in machine translation. Traditional methods to improve translation have typically involved fine-tuning LLMs using parallel corpora. However, vanilla fine-tuning often leads to catastrophic forgetting of the instruction-following capabilities and alignment with human preferences, compromising their broad general abilities and introducing potential security risks. These abilities, which are developed using proprietary and unavailable training data, make existing continual instruction tuning methods ineffective. To overcome this issue, we propose a novel approach called $\textbf{RaDis}$ ($\textbf{Ra}$tionale $\textbf{Dis}$tillation). RaDis harnesses the strong generative capabilities of LLMs to create rationales for training data, which are then “replayed” to prevent forgetting. These rationales $\textit{connect prior knowledge with new tasks}$, acting as $\textit{self-distillation targets}$ to regulate the training process. By jointly training on reference translations and self-generated rationales, the model can learn new translation skills while preserving its general abilities. Extensive experiments demonstrate that our method enhances machine translation performance while maintaining the broader capabilities of LLMs across other tasks. This work presents a pathway for creating more versatile LLMs that excel in specialized tasks without compromising generality or safety and provides a fresh angle for utilizing rationales in the CL field.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 13856