TIM: Teaching Large Language Models to Translate with Comparison (2307.04408v3)

Abstract: Open-sourced LLMs have demonstrated remarkable efficacy in various tasks with instruction tuning. However, these models can sometimes struggle with tasks that require more specialized knowledge such as translation. One possible reason for such deficiency is that instruction tuning aims to generate fluent and coherent text that continues from a given instruction without being constrained by any task-specific requirements. Moreover, it can be more challenging for tuning smaller LLMs with lower-quality training data. To address this issue, we propose a novel framework using examples in comparison to teach LLMs to learn translation. Our approach involves presenting the model with examples of correct and incorrect translations and using a preference loss to guide the model's learning. We evaluate our method on WMT2022 test sets and show that it outperforms existing methods. Our findings offer a new perspective on fine-tuning LLMs for translation tasks and provide a promising solution for generating high-quality translations. Please refer to Github for more details: https://github.com/lemon0830/TIM.

Teaching LLMs to Translate with Comparison

The paper "Teaching LLMs to Translate with Comparison" presents an innovative framework designed to augment the translation capabilities of LLMs. Authored by Jiali Zeng, Fandong Meng, Yongjing Yin, and Jie Zhou, the research addresses the challenges faced by LLMs in handling specialized tasks like translation, which requires precise alignment to specific task requirements. The authors introduce a novel framework, TIM, which fine-tunes LLMs using output and preference comparisons.

The proposed method exploits examples that juxtapose correct and incorrect translations, incorporating an additional preference loss term to strengthen model regularization. Evaluation conducted on benchmarks, including WMT2022 and FLORES-200, reveals that TIM surpasses existing methodologies in translation tasks. TIM is particularly beneficial for fine-tuning smaller LLMs with high-quality training data, making a notable contribution to the field of machine translation.

Methodology

The methodology centers on two components of comparison: output comparison and preference comparison.

1. Output Comparison: The framework presents translation examples with deliberate variations, such as sequence order alterations (order-guided), use of bilingual dictionaries (dictionary-guided), and translation errors with annotations (error-guided). These variations aim to enrich the model's training data, enabling it to comprehend different possible translations for the same input and thus enhancing its understanding of context and task requirements.
2. Preference Comparison: Incorporating samples denoted as "bad output," generated via noise introduction or using a smaller LM, the model is further trained with a preference loss function. This loss function is crucial as it acts to discriminate between high-quality translations and their flawed counterparts, sharpening the model's proclivity towards generating preferable translations.

The final loss function used in training incorporates both the LLMing loss and the preference learning loss, emphasizing the preference for correct translations through comparison.

Experimental Results

The research presents empirical results derived from testing on four language pairs: English-German and Chinese-English, in both directions, using datasets from WMT2022 and FLORES-200. TIM displays exceptional performance, particularly in zero-shot translation scenarios, as it can easily generalize its translation capabilities to language pairs not encountered during training. Moreover, when implementing TIM with different LLM backbones like BLOOMZ and LLaMA, it consistently outperforms established baselines and even competes closely with state-of-the-art systems like NLLB-3.3b.

Implications and Future Directions

The implications of this paper are twofold. Practically, TIM provides a framework for significantly enhancing the translation abilities of LLMs without substantial increases in data or computational resources. Theoretically, it proposes an approach to mitigate common issues like hallucination in machine translation by emphasizing task-specific learning through comparative examples. The preference loss mechanism can potentially inform the development of reward-based tuning frameworks in natural language understanding tasks beyond translation.

The results suggest potential future research directions, including exploring advanced preference learning objectives and integrating more diverse reference materials for output comparisons. These could serve to further minimize inaccuracies and inefficiencies in translation tasks conducted by LLMs.

Overall, the paper provides a research pathway that others in the AI and linguistic fields can follow and expand upon to enhance the efficacy of machine translation systems, contributing meaningfully to the broader application of LLMs in specialized language processing tasks.