EXAONE 4.5 Technical Report

• URL: http://arxiv.org/abs/2604.08644v1
• Date: Thu, 09 Apr 2026 17:51:11 GMT
• Title: EXAONE 4.5 Technical Report
• Abstract summary: This report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research.<n> EXAONE 4.5 is architected by integrating a dedicated visual encoder into the existing EXAONE 4.0 framework.<n>The model is trained on large-scale data with careful curation, particularly emphasizing document-centric corpora.
• Score: 74.37330398684594
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, enabling native multimodal pretraining over both visual and textual modalities. The model is trained on large-scale data with careful curation, particularly emphasizing document-centric corpora that align with LG's strategic application domains. This targeted data design enables substantial performance gains in document understanding and related tasks, while also delivering broad improvements across general language capabilities. EXAONE 4.5 extends context length up to 256K tokens, facilitating long-context reasoning and enterprise-scale use cases. Comparative evaluations demonstrate that EXAONE 4.5 achieves competitive performance in general benchmarks while outperforming state-of-the-art models of similar scale in document understanding and Korean contextual reasoning. As part of LG's ongoing effort toward practical industrial deployment, EXAONE 4.5 is designed to be continuously extended with additional domains and application scenarios to advance AI for a better life.

Related papers

• From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding [15.376371030121094]
  ReceiptBench is a large-scale, human-annotated benchmark consisting of 10k diverse receipts.<n>We introduce ReceiptBench, a large-scale, human-annotated benchmark consisting of 10k diverse receipts.
  arXiv  Detail & Related papers  (2026-05-21T12:37:03Z)
• The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes [584.7162994485712]
  This document consolidates publicly reported technical details about Metas Llama 4 model family.<n>It summarizes (i) released variants (Scout and Maverick) and the broader herd context.<n>The goal is to provide a compact technical reference for researchers and practitioners who need precise, source-backed facts.
  arXiv  Detail & Related papers  (2026-01-15T21:06:38Z)
• Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry [6.231880333264743]
  This paper explores how SciNCL can be applied to the process industry domain.<n>Our experiments demonstrate that language models fine-tuned with triplets derived from graph embeddings (GE) outperform a state-of-the-art mE5-large text encoder.
  arXiv  Detail & Related papers  (2025-10-06T09:36:20Z)
• EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes [42.31740630042654]
  EXAONE 4.0 integrates a Non-reasoning mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep.<n>The EXAONE 4.0 model series consists of two sizes: a mid-size 32B model optimized for high performance, and a small-size 1.2B model designed for on-device applications.
  arXiv  Detail & Related papers  (2025-07-15T15:24:51Z)
• Eka-Eval : A Comprehensive Evaluation Framework for Large Language Models in Indian Languages [1.1957520154275776]
  EKA-eval is a unified evaluation framework that integrates over 35+ benchmarks across nine major evaluation categories.<n>It provides 11 core capabilities through a modular architecture, seamless integration with Hugging Face and proprietary models, and plug-and-play usability.<n>The framework is open-source and publicly available at: https://github.com/lingo-iitgn/eka-eval.
  arXiv  Detail & Related papers  (2025-07-02T16:07:54Z)
• From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models [54.44375226381814]
  Long-context capabilities are essential for a wide range of applications, including document and video understanding, in-context learning, and inference-time scaling.<n>We introduce a efficient training recipe for building ultra-long context LLMs from aligned instruct model, pushing the boundaries of context lengths from 128K to 1M, 2M, and 4M tokens.<n>Our approach achieves state-of-the-art performance across a diverse set of long-context benchmarks.
  arXiv  Detail & Related papers  (2025-04-08T16:58:58Z)
• LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework synergizes open-world knowledge with collaborative knowledge.<n>We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• The All-Seeing Project V2: Towards General Relation Comprehension of the Open World [58.40101895719467]
  We present the All-Seeing Project V2, a new model and dataset designed for understanding object relations in images. We propose the All-Seeing Model V2 that integrates the formulation of text generation, object localization, and relation comprehension into a relation conversation task. Our model excels not only in perceiving and recognizing all objects within the image but also in grasping the intricate relation graph between them.
  arXiv  Detail & Related papers  (2024-02-29T18:59:17Z)
• Improving Language Models via Plug-and-Play Retrieval Feedback [42.786225163763376]
  Large language models (LLMs) exhibit remarkable performance across various NLP tasks. They often generate incorrect or hallucinated information, which hinders their practical applicability in real-world scenarios. We introduce ReFeed, a novel pipeline designed to enhance LLMs by providing automatic retrieval feedback in a plug-and-play framework.
  arXiv  Detail & Related papers  (2023-05-23T12:29:44Z)
• Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction [18.926993352330797]
  We propose TransFusion, a framework in which models are fine-tuned to use English translations of low-resource language data. GoLLIE-TF, a cross-lingual instruction-tuned LLM for IE tasks, is designed to close the performance gap between high and low-resource languages.
  arXiv  Detail & Related papers  (2023-05-23T01:23:22Z)
• Text Embeddings by Weakly-Supervised Contrastive Pre-training [98.31785569325402]
  E5 is a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. E5 can be readily used as a general-purpose embedding model for any tasks requiring a single-vector representation of texts.
  arXiv  Detail & Related papers  (2022-12-07T09:25:54Z)

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.