Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?

Problem
This work addresses the challenge of applying grammatical information effectively in low-resource machine translation (MT) using large language models (LLMs). Despite the potential of LLMs to leverage linguistic resources through in-context learning, they often fail to utilize grammatical structures adequately. The authors propose a novel approach to enhance MT for extremely low-resource languages, specifically Xibe and Chintang, by generating structured linguistic reasoning traces. This paper is a preprint and has not undergone peer review.

Method
The authors introduce a pipeline for automatically generating step-by-step linguistic reasoning traces derived from Universal Dependencies treebanks, dictionaries, and grammar-rule banks. They evaluate the effectiveness of these traces in three distinct settings: in-context learning (ICL), supervised fine-tuning (SFT), and reinforcement fine-tuning (RFT). The traces serve as inference-time guidance in ICL, while in SFT and RFT, they are used as training data. The experiments focus on the translation performance of various models when provided with these structured traces, assessing their impact on translation quality.

Results
The results indicate that linguistic reasoning traces significantly enhance translation performance, particularly in the ICL setting. The authors report that reliable sentence-specific traces lead to substantial improvements across most models and evaluation metrics. For instance, the use of reasoning traces in ICL resulted in a performance increase of up to 15% BLEU score compared to baseline models without traces. In contrast, when used as training data in SFT and RFT, the gains were smaller and less consistent, with some models generating erroneous content, indicating that while the traces are beneficial, the models struggle to learn the correct format and content during training.

Limitations
The authors acknowledge that the primary limitation is the models’ difficulty in learning to generate accurate linguistic analyses from the traces, which hampers the effectiveness of SFT and RFT. Additionally, the reliance on high-quality linguistic resources may not be feasible for all low-resource languages, potentially limiting the generalizability of the approach. The paper does not address the computational costs associated with generating and utilizing these traces, which could impact scalability in practical applications.

Why it matters
This research highlights the potential of structured linguistic reasoning to improve low-resource MT, suggesting that LLMs can benefit from enhanced grammatical information when provided with reliable analyses. The findings underscore the importance of developing better methods for generating and utilizing linguistic traces, which could inform future work in low-resource language processing. This work contributes to the ongoing discourse on integrating linguistic knowledge into neural models, as published in arXiv.