Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis

• URL: http://arxiv.org/abs/2405.14277v2
• Date: Wed, 7 Aug 2024 08:21:58 GMT
• Title: Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis
• Authors: Sabri Boughorbel, MD Rizwan Parvez, Majd Hawasly,
• Abstract summary: We investigate the role of translation and synthetic data in training language models. We translate TinyStories, a dataset of 2.2M short stories for 3-4 year old children, from English to Arabic using the open NLLB-3B MT model. To rectify these issues, we pre-train the models with a small dataset of synthesized high-quality Arabic stories.
• Score: 3.16714407449467
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Training LLMs for low-resource languages usually utilizes data augmentation from English using machine translation (MT). This, however, brings a number of challenges to LLM training: there are large costs attached to translating and curating huge amounts of content with high-end machine translation solutions; the translated content carries over cultural biases; and if the translation is not faithful and accurate, data quality degrades causing issues in the trained model. In this work, we investigate the role of translation and synthetic data in training language models. We translate TinyStories, a dataset of 2.2M short stories for 3-4 year old children, from English to Arabic using the open NLLB-3B MT model. We train a number of story generation models of size 1M-33M parameters using this data. We identify a number of quality and task-specific issues in the resulting models. To rectify these issues, we further pre-train the models with a small dataset of synthesized high-quality Arabic stories generated by a capable LLM, representing 1% of the original training data. We show, using GPT-4 as a judge and Dictionary Learning Analysis from mechanistic interpretability, that the suggested approach is a practical means to resolve some of the machine translation pitfalls. We illustrate the improvements through case studies of linguistic and cultural bias issues.

Related papers

• How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes [2.0109318570325847]
  We investigate the impact of fine-tuning the Llama 3 model using TMs from a specific organisation in the software sector. We fine-tune separate models for each training set and evaluate their performance based on automatic metrics, BLEU, chrF++, TER, and COMET. Our findings reveal improvement in translation performance with larger datasets across all metrics.
  arXiv  Detail & Related papers  (2024-09-05T12:06:38Z)
• Machine Translation for Ge'ez Language [0.0]
  Machine translation for low-resource languages such as Ge'ez faces challenges such as out-of-vocabulary words, domain mismatches, and lack of labeled training data. We develop a multilingual neural machine translation (MNMT) model based on languages relatedness. We also experiment with using GPT-3.5, a state-of-the-art LLM, for few-shot translation with fuzzy matches.
  arXiv  Detail & Related papers  (2023-11-24T14:55:23Z)
• 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)
• Unified Model Learning for Various Neural Machine Translation [63.320005222549646]
  Existing machine translation (NMT) studies mainly focus on developing dataset-specific models. We propose a versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks. OurNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs.
  arXiv  Detail & Related papers  (2023-05-04T12:21:52Z)
• MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation [0.0]
  Large pre-trained language models have brought remarkable progress in NLP. We empirically demonstrate the effectiveness of self-supervised pre-training and data augmentation for zero-shot multi-lingual machine translation.
  arXiv  Detail & Related papers  (2022-10-01T17:01:30Z)
• ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback [70.5469946314539]
  ChrEnTranslate is an online machine translation demonstration system for translation between English and an endangered language Cherokee. It supports both statistical and neural translation models as well as provides quality estimation to inform users of reliability.
  arXiv  Detail & Related papers  (2021-07-30T17:58:54Z)
• Multilingual Neural Semantic Parsing for Low-Resourced Languages [1.6244541005112747]
  We introduce a new multilingual semantic parsing dataset in English, Italian and Japanese. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset. We find that a semantic trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.
  arXiv  Detail & Related papers  (2021-06-07T09:53:02Z)
• Beyond English-Centric Multilingual Machine Translation [74.21727842163068]
  We create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT.
  arXiv  Detail & Related papers  (2020-10-21T17:01:23Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)

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.