Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?
- URL: http://arxiv.org/abs/2410.17145v1
- Date: Tue, 22 Oct 2024 16:26:03 GMT
- Title: Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?
- Authors: Jirat Chiaranaipanich, Naiyarat Hanmatheekuna, Jitkapat Sawatphol, Krittamate Tiankanon, Jiramet Kinchagawat, Amrest Chinkamol, Parinthapat Pengpun, Piyalitt Ittichaiwong, Peerat Limkonchotiwat,
- Abstract summary: Large language models (LLMs) perform well on common tasks but struggle with generalization in low-resource and low-computation settings.
We examine this limitation by testing various LLMs and specialized translation models on English-Thai machine translation and code-switching datasets.
- Score: 2.1969983462375318
- License:
- Abstract: Large language models (LLMs) perform well on common tasks but struggle with generalization in low-resource and low-computation settings. We examine this limitation by testing various LLMs and specialized translation models on English-Thai machine translation and code-switching datasets. Our findings reveal that under more strict computational constraints, such as 4-bit quantization, LLMs fail to translate effectively. In contrast, specialized models, with comparable or lower computational requirements, consistently outperform LLMs. This underscores the importance of specialized models for maintaining performance under resource constraints.
Related papers
- What do Large Language Models Need for Machine Translation Evaluation? [12.42394213466485]
Large language models (LLMs) can achieve results comparable to fine-tuned multilingual pre-trained language models.
This paper explores what translation information, such as the source, reference, translation errors and annotation guidelines, is needed for LLMs to evaluate machine translation quality.
arXiv Detail & Related papers (2024-10-04T09:50:45Z) - Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation [62.202893186343935]
We explore what it would take to adapt Large Language Models for low-resource languages.
We show that parallel data is critical during both pre-training andSupervised Fine-Tuning (SFT)
Our experiments with three LLMs across two low-resourced language groups reveal consistent trends, underscoring the generalizability of our findings.
arXiv Detail & Related papers (2024-08-23T00:59:38Z) - 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) - 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) - TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement [26.26493253161022]
Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT)
We introduce a systematic LLM-based self-refinement translation framework, named textbfTEaR.
arXiv Detail & Related papers (2024-02-26T07:58:12Z) - Adapting Large Language Models for Document-Level Machine Translation [46.370862171452444]
Large language models (LLMs) have significantly advanced various natural language processing (NLP) tasks.
Recent research indicates that moderately-sized LLMs often outperform larger ones after task-specific fine-tuning.
This study focuses on adapting LLMs for document-level machine translation (DocMT) for specific language pairs.
arXiv Detail & Related papers (2024-01-12T09:29:13Z) - SCALE: Synergized Collaboration of Asymmetric Language Translation
Engines [105.8983433641208]
We introduce a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine.
By introducing translation from STM into the triplet in-context demonstrations, SCALE unlocks refinement and pivoting ability of LLM.
Our experiments show that SCALE significantly outperforms both few-shot LLMs (GPT-4) and specialized models (NLLB) in challenging low-resource settings.
arXiv Detail & Related papers (2023-09-29T08:46:38Z) - Simultaneous Machine Translation with Large Language Models [51.470478122113356]
We investigate the possibility of applying Large Language Models to SimulMT tasks.
We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset.
The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
arXiv Detail & Related papers (2023-09-13T04:06:47Z) - 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) - 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.