AI BriefingLG AI Research news
The latest AI and developer news from LG AI Research, with short summaries.
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LG AI Research presented research on the model compression technique LeSS and LLM structured pruning at an ICLR 2024 workshop.
The New Hwadamsup: Image Understanding and Generation via LMM
LG AI Research has proposed the EXAONE LMM architecture, which resolves data licensing issues in existing LMMs while improving efficiency.
LG AI Research: Applying Image Understanding and Generation Technology through the Hwadamsup Project
LG AI Research introduces a case where it combined EXAONE's Image Understanding and Generation technologies to realize the Hwadamsup media art.
Multi-Agent Reinforcement Learning for Real-World Deployment
LG AI Research proposed the AOCC architecture, which improves the efficiency of asynchronous multi-agent reinforcement learning (MARL).
[AAMAS 2024] Multi-Agent Reinforcement Learning for Real-World Applications (Part 2)
To overcome differences in observation timing among agents in asynchronous environments, an AOCC structure was proposed to improve MARL learning efficiency.
[AAMAS 2024] Multi-Agent Reinforcement Learning for Real-World Applications (Part 1) - LG AI Research Blog
LG AI Research presented cases of applying Multi-Agent Reinforcement Learning (MARL) to solve real-world problems at AAMAS 2024.
LG AI Research: Multi-Agent Reinforcement Learning (MARL) Research for Solving Real-World Problems
LG AI Research presented multi-agent reinforcement learning research for autonomous driving and industrial site optimization at AAMAS 2024.
LG AI Research Wins NAACL 2024 Social Impact Award for Study on LLMs' Cultural Common Sense Understanding
A joint study by LG AI Research and the University of Michigan won the NAACL 2024 Social Impact Award for analyzing LLMs' understanding of cultural common sense and their biases.
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A joint study by LG AI Research and the University of Michigan analyzing LLMs' understanding of cultural common sense won the NAACL 2024 Social Impact Award.
LG AI Research: ICLR 2024 Research Summary
LG AI Research introduces the latest research trends in foundation models presented at ICLR 2024, focused on interpretability, sustainability, and safety.
[ICLR 2024] Latest Research Trends in the Foundation Model Field - LG AI Research BLOG
This introduces the latest research trends presented at ICLR 2024 regarding the generalization ability, efficiency, and safety of Foundation Models.
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It introduces the 'Generative Image Dynamics' research, selected as Best Paper at CVPR 2024, a technology that generates natural motion from still images.
LG AI Research: EXAONE 3.0 Released
LG AI Research unveiled EXAONE 3.0 7.8B, an open-source model with excellent English and Korean performance.
LG AI Research Unveils EXAONE 3.0 7.8B Instruction Tuned Model
LG AI Research has open-sourced EXAONE 3.0 7.8B, an expert-grade AI model with outstanding English and Korean performance.
Introducing ChatEXAONE: An Enterprise AI Agent Delivering Expert-Level AI Insights to Boost Corporate Efficiency - LG AI Research BLOG
LG AI Research unveiled ChatEXAONE, an enterprise AI agent based on the open-source LLM EXAONE 3.0.
ChatEXAONE: Unveiling an Enterprise AI Agent to Transform Corporate Users' Work Productivity - LG AI Research BLOG
LG AI Research has unveiled ChatEXAONE, an AI agent based on EXAONE 3.0 that supports enterprise work innovation.
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LG AI Research has released EXAONEPath, an open-source model specialized in pathology image analysis.
LG AI Research Open-Sources EXAONEPath, a Model Specialized for Histopathology Images
LG AI Research has unveiled EXAONEPath, an open-source model specialized for histopathology image analysis that achieves both high performance and cost efficiency.
Using Binning as a Pretext Task: Improving Self-Supervised Learning in the Tabular Domain
LG AI Research presented a Binning-based Pretext Task study at ICML 2024 that improves self-supervised learning performance on tabular data.
LG AI Research Presents Tabular Domain Research Results at ICML 2024
LG AI Research presented a new methodology at ICML 2024 that improves learning performance on tabular data.