Multi-dimensional data refining strategy for effective fine-tuning LLMs

• URL: http://arxiv.org/abs/2311.01049v1
• Date: Thu, 2 Nov 2023 07:50:43 GMT
• Title: Multi-dimensional data refining strategy for effective fine-tuning LLMs
• Authors: Thanh Nguyen Ngoc, Quang Nhat Tran, Arthur Tang, Bao Nguyen, Thuy Nguyen, Thanh Pham
• Abstract summary: This paper presents lessons learned while crawling and refining data tailored for fine-tuning Vietnamese language models. Our paper presents a multidimensional strategy including leveraging existing datasets in the English language and developing customized data-crawling scripts with the assistance of generative AI tools.
• Score: 2.67766280323297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data is a cornerstone for fine-tuning large language models, yet acquiring suitable data remains challenging. Challenges encompassed data scarcity, linguistic diversity, and domain-specific content. This paper presents lessons learned while crawling and refining data tailored for fine-tuning Vietnamese language models. Crafting such a dataset, while accounting for linguistic intricacies and striking a balance between inclusivity and accuracy, demands meticulous planning. Our paper presents a multidimensional strategy including leveraging existing datasets in the English language and developing customized data-crawling scripts with the assistance of generative AI tools. A fine-tuned LLM model for the Vietnamese language, which was produced using resultant datasets, demonstrated good performance while generating Vietnamese news articles from prompts. The study offers practical solutions and guidance for future fine-tuning models in languages like Vietnamese.

Related papers

• Unsupervised Data Validation Methods for Efficient Model Training [0.0]
  State-of-the-art models in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT) and vision-language models (VLM) rely heavily on large datasets. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training.
  arXiv  Detail & Related papers  (2024-10-10T13:00:53Z)
• Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation [2.9921619703037274]
  We propose a retrieval augmented generation (RAG) framework backed by a large language model (LLM) to correct the output of a smaller model for the linguistic task of morphological glossing. We leverage linguistic information to make up for the lack of data and trainable parameters, while allowing for inputs from written descriptive grammars interpreted and distilled through an LLM. We show that a compact, RAG-supported model is highly effective in data-scarce settings, achieving a new state-of-the-art for this task and our target languages.
  arXiv  Detail & Related papers  (2024-10-01T04:20:14Z)
• Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution [7.681258910515419]
  Tabular data presents unique challenges due to its heterogeneous nature and complex structural relationships. High predictive performance and robustness in tabular data analysis holds significant promise for numerous applications. The recent advent of large language models, such as GPT and LLaMA, has further revolutionized the field, facilitating more advanced and diverse applications with minimal fine-tuning.
  arXiv  Detail & Related papers  (2024-08-20T04:59:19Z)
• ViANLI: Adversarial Natural Language Inference for Vietnamese [1.907126872483548]
  We introduce the adversarial NLI dataset to the NLP research community with the name ViANLI. This data set contains more than 10K premise-hypothesis pairs. The accuracy of the most powerful model on the test set only reached 48.4%.
  arXiv  Detail & Related papers  (2024-06-25T16:58:19Z)
• CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages [86.90220551111096]
  Training datasets for large language models (LLMs) are often not fully disclosed. We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages.
  arXiv  Detail & Related papers  (2023-09-17T23:49:10Z)
• Improving Domain-Specific Retrieval by NLI Fine-Tuning [64.79760042717822]
  This article investigates the fine-tuning potential of natural language inference (NLI) data to improve information retrieval and ranking. We employ both monolingual and multilingual sentence encoders fine-tuned by a supervised method utilizing contrastive loss and NLI data. Our results point to the fact that NLI fine-tuning increases the performance of the models in both tasks and both languages, with the potential to improve mono- and multilingual models.
  arXiv  Detail & Related papers  (2023-08-06T12:40:58Z)
• 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)
• Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP [0.5482532589225552]
  In our work we suggest to leverage pretrained language models for training data acquisition. We create a custom dataset which we use to train a medical NER model for German texts, GPTNERMED.
  arXiv  Detail & Related papers  (2022-08-30T18:42:55Z)
• Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation [70.81596088969378]
  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding. COD enables dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages.
  arXiv  Detail & Related papers  (2022-01-31T18:11:21Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• Data Augmentation for Spoken Language Understanding via Pretrained Language Models [113.56329266325902]
  Training of spoken language understanding (SLU) models often faces the problem of data scarcity. We put forward a data augmentation method using pretrained language models to boost the variability and accuracy of generated utterances.
  arXiv  Detail & Related papers  (2020-04-29T04:07:12Z)

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.