GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators (2402.06894v2)

Abstract: Recent advances in LLMs have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inference. These techniques struggle to fully exploit the rich information in the diverse N-best hypotheses, making them less optimal for translation tasks that require a single, high-quality output sequence. In this paper, we propose a new generative paradigm for translation tasks, namely "GenTranslate", which builds upon LLMs to generate better results from the diverse translation versions in N-best list. Leveraging the rich linguistic knowledge and strong reasoning abilities of LLMs, our new paradigm can integrate the rich information in N-best candidates to generate a higher-quality translation result. Furthermore, to support LLM finetuning, we build and release a HypoTranslate dataset that contains over 592K hypotheses-translation pairs in 11 languages. Experiments on various speech and machine translation benchmarks (e.g., FLEURS, CoVoST-2, WMT) demonstrate that our GenTranslate significantly outperforms the state-of-the-art model.

Examining GenTranslate: An Innovative Paradigm for Multilingual Speech and Machine Translation

The paper "GenTranslate: LLMs are Generative Multilingual Speech and Machine Translators" introduces GenTranslate, a novel approach designed to enhance translation quality by leveraging LLMs. Recent improvements in LLMs have facilitated advanced developments in multilingual speech and machine translation, mainly by minimizing representation errors and infusing external knowledge. The traditional reliance on beam search decoding and top-1 hypothesis selection has been determined to be sub-optimal because these techniques often fail to harness the richness contained in $N$-best hypotheses. GenTranslate seeks to address this limitation by integrating the $N$-best candidates through LLMs to generate high-quality translations.

Key Contributions and Dataset

The research introduces a HypoTranslate dataset consisting of over 592,000 hypotheses-translation pairs across 11 languages, which supports LLM fine-tuning. The inclusion of this dataset is a significant contribution, enabling systematic evaluation and enhancement of LLM-based translation models. The GenTranslate model leverages the fine-tuned LLMs on this dataset, significantly surpassing the state-of-the-art models across various benchmarks such as FLEURS, CoVoST-2, and WMT.

Experimental Outcomes

The experimental section asserts GenTranslate's superior performance over existing models, showcasing its capabilities on speech and machine translation tasks. For instance, GenTranslate demonstrates a notable improvement in BLEU scores, with figures surpassing SeamlessM4T and other models in both end-to-end ST and cascaded ASR+MT tasks. These outcomes are backed by a detailed series of experiments that compare different LLMs, training strategies, and the impact of $N$-best list sizes. The findings highlight GenTranslate's efficiency in utilizing the diverse information from $N$-best lists to deliver translations that better align with ground truth.

Practical and Theoretical Implications

From a practical standpoint, GenTranslate provides a promising pathway towards optimizing translation systems, reducing errors, and enhancing the reliability of outputs across multiple languages. This approach can be particularly beneficial in contexts requiring precise and high-quality translation, such as international diplomacy or global business communications. Theoretically, this research emphasizes the potential of LLMs beyond traditional tasks and opens avenues for further explorations into integrating vast hypothesis spaces to improve single-output quality.

Potential Future Directions

The implications of GenTranslate extend to various domains, from language learning applications to automated multimedia subtitling. Future research could explore improvements in LLMs to boost their translational capacity further, potentially through integrating cross-lingual knowledge. Enhancements in model efficiency, data representations, and scalability remain areas ripe for exploration. Additionally, studying the implications of GenTranslate in low-resource languages and environments could broaden its impact and accessibility.

This investigation into GenTranslate reflects a significant stride forward in translational science, marrying the depth of LLM reasoning with the practical requirements of multilingual translation. While rooted in the solid foundation of existing methodologies, it lays the groundwork for translating richer content more effectively, moving towards the ultimate goal of seamless multilingual communication.