From LIMA to DeepLIMA: following a new path of interoperability

• URL: http://arxiv.org/abs/2409.06550v1
• Date: Tue, 10 Sep 2024 14:26:12 GMT
• Title: From LIMA to DeepLIMA: following a new path of interoperability
• Authors: Victor Bocharov, Romaric Besançon, Gaël de Chalendar, Olivier Ferret, Nasredine Semmar,
• Abstract summary: We describe the architecture of the LIMA framework and its recent evolution with the addition of new text analysis modules based on deep neural networks. Models were trained for more than 60 languages on the Universal Dependencies 2.5 corpora, WikiNer corpora, and CoNLL-03 dataset. This integration of ubiquitous Deep Learning Natural Language Processing models and the use of standard annotated collections can be viewed as a new path of interoperability.
• Score: 2.5764171991553795
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this article, we describe the architecture of the LIMA (Libre Multilingual Analyzer) framework and its recent evolution with the addition of new text analysis modules based on deep neural networks. We extended the functionality of LIMA in terms of the number of supported languages while preserving existing configurable architecture and the availability of previously developed rule-based and statistical analysis components. Models were trained for more than 60 languages on the Universal Dependencies 2.5 corpora, WikiNer corpora, and CoNLL-03 dataset. Universal Dependencies allowed us to increase the number of supported languages and to generate models that could be integrated into other platforms. This integration of ubiquitous Deep Learning Natural Language Processing models and the use of standard annotated collections using Universal Dependencies can be viewed as a new path of interoperability, through the normalization of models and data, that are complementary to a more standard technical interoperability, implemented in LIMA through services available in Docker containers on Docker Hub.

Related papers

• LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models [89.13128402847943]
  We present LUSIFER, a novel zero-shot approach that adapts LLM-based embedding models for multilingual tasks without requiring multilingual supervision. LUSIFER's architecture combines a multilingual encoder, serving as a language-universal learner, with an LLM-based embedding model optimized for embedding-specific tasks. We introduce a new benchmark encompassing 5 primary embedding tasks, 123 diverse datasets, and coverage across 14 languages.
  arXiv  Detail & Related papers  (2025-01-01T15:43:07Z)
• Chunk-Distilled Language Modeling [25.238256586953487]
  Chunk-Distilled Language Modeling (CD-LM) is an approach to text generation that addresses two challenges in current large language models (LLMs) Our method combines deep network-based LLMs with a straightforward retrieval module, which allows the generation of multi-token text chunks at a single decoding step.
  arXiv  Detail & Related papers  (2024-12-31T08:32:15Z)
• OmniEvalKit: A Modular, Lightweight Toolbox for Evaluating Large Language Model and its Omni-Extensions [58.46747176834132]
  We present OmniEvalKit, a novel benchmarking toolbox designed to evaluate Large Language Models (LLMs) Unlike existing benchmarks that often focus on a single aspect, OmniEvalKit provides a modular, lightweight, and automated evaluation system. It is structured with a modular architecture comprising a Static Builder and Dynamic Data Flow, promoting the seamless integration of new models and datasets.
  arXiv  Detail & Related papers  (2024-12-09T17:39:43Z)
• IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities [4.269326314400742]
  We introduce the Inner-Adaptor Architecture for multimodal large language models (MLLMs) The architecture incorporates multiple multimodal adaptors at varying depths within the large language model to facilitate direct interaction with the inherently text-oriented transformer layers. Unlike previous approaches of freezing language models that require large-scale aligned data, our proposed architecture is able to achieve superior performance on small-scale datasets.
  arXiv  Detail & Related papers  (2024-08-23T08:10:13Z)
• Language Portability Strategies for Open-domain Dialogue with Pre-trained Language Models from High to Low Resource Languages [1.7436854281619139]
  We propose a study of linguistic portability strategies of large pre-trained language models (PLMs) used for open-domain dialogue systems. In particular the target low-resource language (L_T) will be simulated with French, as it lacks of task-specific resources.
  arXiv  Detail & Related papers  (2024-07-01T14:20:54Z)
• CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
  This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
  arXiv  Detail & Related papers  (2024-04-03T02:21:46Z)
• SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models [97.40590590880144]
  We develop an extensive Multimodality Large Language Model (MLLM) series. We assemble a comprehensive dataset covering publicly available resources in language, vision, and vision-language tasks. We obtain a spectrum of MLLMs that vary in parameter size and multilingual capabilities.
  arXiv  Detail & Related papers  (2024-02-08T18:59:48Z)
• MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue [115.32009638844059]
  We extend the English only NLU++ dataset to include manual translations into a range of high, medium, and low resource languages. Because of its multi-intent property, MULTI3NLU++ represents complex and natural user goals. We use MULTI3NLU++ to benchmark state-of-the-art multilingual models for the Natural Language Understanding tasks of intent detection and slot labelling.
  arXiv  Detail & Related papers  (2022-12-20T17:34:25Z)
• Multi-lingual Evaluation of Code Generation Models [82.7357812992118]
  We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages. We are able to assess the performance of code generation models in a multi-lingual fashion.
  arXiv  Detail & Related papers  (2022-10-26T17:17:06Z)

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.