Augmenting Large Language Model Translators via Translation Memories

• URL: http://arxiv.org/abs/2305.17367v1
• Date: Sat, 27 May 2023 04:47:09 GMT
• Title: Augmenting Large Language Model Translators via Translation Memories
• Authors: Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, Jingbo Zhu
• Abstract summary: Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. We take a step towards prompting large language models (LLMs) with TMs and making them better translators.
• Score: 32.28138249566329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large language models (LLMs) with TMs and making them better translators. We find that the ability of LLMs to ``understand'' prompts is indeed helpful for making better use of TMs. Experiments show that the results of a pre-trained LLM translator can be greatly improved by using high-quality TM-based prompts. These results are even comparable to those of the state-of-the-art NMT systems which have access to large-scale in-domain bilingual data and are well tuned on the downstream tasks.

Related papers

• Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages [2.53740603524637]
  Machine translation models (MT) produce excellent multilingual representations, resulting in strong translation performance even for low-resource languages. In this work, we get the best both worlds by integrating MT encoders directly into language backbones via sample-efficient self-distillation. The resulting MT-LLMs preserve the inherent multilingual representational alignment from the MT encoder, allowing lower-resource languages to tap into the rich knowledge embedded in English-centric LLMs.
  arXiv  Detail & Related papers  (2024-06-18T16:00:20Z)
• TasTe: Teaching Large Language Models to Translate through Self-Reflection [82.83958470745381]
  Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. We propose the TasTe framework, which stands for translating through self-reflection. The evaluation results in four language directions on the WMT22 benchmark reveal the effectiveness of our approach compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-12T17:21:21Z)
• Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning [57.323716555996114]
  Off-target translation remains an unsolved problem, especially for low-resource languages. Recent works have either designed advanced prompting strategies to highlight the functionality of translation instructions or exploited the in-context learning ability of LLMs. In this work, we design a two-stage fine-tuning algorithm to improve the instruction-following ability (especially the translation direction) of LLMs.
  arXiv  Detail & Related papers  (2024-03-21T13:47:40Z)
• Speech Translation with Large Language Models: An Industrial Practice [64.5419534101104]
  We introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained large language model (LLM) By integrating the large language model (LLM) with a speech encoder and employing multi-task instruction tuning, LLM-ST can produce accurate timestamped transcriptions and translations. Through rigorous experimentation on English and Chinese datasets, we showcase the exceptional performance of LLM-ST.
  arXiv  Detail & Related papers  (2023-12-21T05:32:49Z)
• On-the-Fly Fusion of Large Language Models and Machine Translation [3.718665608549311]
  We propose the on-the-fly ensembling of a machine translation model with an LLM prompted on the same task and input. We find that a slightly weaker-at-translation LLM can improve translations of a NMT model, and ensembling with an LLM can produce better translations than ensembling two stronger MT models.
  arXiv  Detail & Related papers  (2023-11-14T16:49:33Z)
• TIM: Teaching Large Language Models to Translate with Comparison [78.66926087162672]
  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. Our findings offer a new perspective on fine-tuning LLMs for translation tasks and provide a promising solution for generating high-quality translations.
  arXiv  Detail & Related papers  (2023-07-10T08:15:40Z)
• Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions [68.01449013641532]
  Large-scale Pretrained Language Models (LLMs) have shown strong abilities in multilingual translations. We present a detailed analysis by finetuning a multilingual pretrained language model, XGLM-7B, to perform multilingual translation.
  arXiv  Detail & Related papers  (2023-05-24T12:00:24Z)
• Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis [103.89753784762445]
  Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT) This paper systematically investigates the advantages and challenges of LLMs for MMT. We thoroughly evaluate eight popular LLMs, including ChatGPT and GPT-4.
  arXiv  Detail & Related papers  (2023-04-10T15:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.