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1. Apparenté Pas pertinent

   Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca

   Yiming Cui, Ziqing Yang, Xin Yao · 2023 · Computation and Language · preprint, non relu par les pairs · cité 447 fois · Pas encore résumé

   Résumé scientifique

   Large Language Models (LLMs), such as ChatGPT and GPT-4, have dramatically transformed natural language processing research and shown promising strides towards Artificial General Intelligence (AGI). Nonetheless, the high costs associated with training and deploying LLMs present substantial obstacles to transparent, accessible academic research. While several large language models, such as LLaMA, have been open-sourced by the community, these predominantly focus on English corpora, limiting their usefulness for other languages. In this paper, we propose a method to augment LLaMA with capabilities for understanding and generating Chinese text and its ability to follow instructions. We achieve …

2. Apparenté Pas pertinent

   PP-OCR: A Practical Ultra Lightweight OCR System

   Yuning Du, Chenxia Li, Ruoyu Guo et al. · 2020 · Computer Vision and Pattern Recognition · preprint, non relu par les pairs · cité 379 fois · Résumé IA disponible

   Résumé scientifique

   The Optical Character Recognition (OCR) systems have been widely used in various of application scenarios, such as office automation (OA) systems, factory automations, online educations, map productions etc. However, OCR is still a challenging task due to the various of text appearances and the demand of computational efficiency. In this paper, we propose a practical ultra lightweight OCR system, i.e., PP-OCR. The overall model size of the PP-OCR is only 3.5M for recognizing 6622 Chinese characters and 2.8M for recognizing 63 alphanumeric symbols, respectively. We introduce a bag of strategies to either enhance the model ability or reduce the model size. The corresponding ablation experiment…

3. Apparenté Pas pertinent

   OLMo: Accelerating the Science of Language Models

   Dirk Groeneveld, Iz Beltagy, Pete Walsh et al. · 2024 · Computation and Language · cité 786 fois · Résumé IA disponible

   Résumé scientifique

   Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, this technical report details the first release of OLMo, a state-of-the-art, truly Open Language Model and its framework to build and study the…

4. Apparenté Pas pertinent

   CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

   Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau et al. · 2019 · Computation and Language · cité 909 fois · Résumé IA disponible

   Résumé scientifique

   Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.

5. Apparenté Pas pertinent

   LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding

   Jiapeng Wang, Lianwen Jin, Kai Ding · 2022 · Computation and Language · cité 205 fois · Résumé IA disponible

   Résumé scientifique

   Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained t…

6. Apparenté Pas pertinent

   DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond

   Cong Yao · 2023 · Computer Vision and Pattern Recognition · preprint, non relu par les pairs · cité 14 fois · Résumé IA disponible

   Résumé scientifique

   In this report, we introduce DocXChain, a powerful open-source toolchain for document parsing, which is designed and developed to automatically convert the rich information embodied in unstructured documents, such as text, tables and charts, into structured representations that are readable and manipulable by machines. Specifically, basic capabilities, including text detection, text recognition, table structure recognition and layout analysis, are provided. Upon these basic capabilities, we also build a set of fully functional pipelines for document parsing, i.e., general text reading, table parsing, and document structurization, to drive various applications related to documents in real-wor…

7. Apparenté Pas pertinent

   LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction

   Abdullatif Köksal, Timo Schick, Anna Korhonen et al. · 2023 · Computation and Language · cité 64 fois · Pas encore résumé

   Résumé scientifique

   Instruction tuning enables language models to generalize more effectively and better follow user intent. However, obtaining instruction data can be costly and challenging. Prior works employ methods such as expensive human annotation, crowd-sourced datasets with alignment issues, or generating noisy examples via LLMs. We introduce the LongForm dataset, which is created by leveraging English corpus examples with augmented instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. This approach provides a cheaper and cleaner instruction-tuning dataset and one suitable for long text …

8. Apparenté Pas pertinent

   MinerU: An Open-Source Solution for Precise Document Content Extraction

   Bin Wang, Chao Xu, Xiaomeng Zhao et al. · 2024 · Computer Vision and Pattern Recognition · preprint, non relu par les pairs · cité 370 fois · Résumé IA disponible

   Résumé scientifique

   Document content analysis has been a crucial research area in computer vision. Despite significant advancements in methods such as OCR, layout detection, and formula recognition, existing open-source solutions struggle to consistently deliver high-quality content extraction due to the diversity in document types and content. To address these challenges, we present MinerU, an open-source solution for high-precision document content extraction. MinerU leverages the sophisticated PDF-Extract-Kit models to extract content from diverse documents effectively and employs finely-tuned preprocessing and postprocessing rules to ensure the accuracy of the final results. Experimental results demonstrate…