This introduces the latest research methodologies of Foundation models for tackling time series data, classified according to how they utilize LLMs.
LG AI Research and KAIST took first place in the EHRSQL task at NAACL 2024 with a highly reliable Text-to-SQL model for extracting medical data.
LG AI Research, together with KAIST, took 1st place at the NAACL 2024 workshop with a reliable Text-to-SQL model that improves access to medical data.
At ICLR 2024, LG AI Research introduced LeSS, a new model compression technique that combines filter pruning and low-rank decomposition.
LG AI Research presented research on the model compression technique LeSS and LLM structured pruning at an ICLR 2024 workshop.
LG AI Research has proposed the EXAONE LMM architecture, which resolves data licensing issues in existing LMMs while improving efficiency.
LG AI Research introduces a case where it combined EXAONE's Image Understanding and Generation technologies to realize the Hwadamsup media art.
LG AI Research proposed the AOCC architecture, which improves the efficiency of asynchronous multi-agent reinforcement learning (MARL).
To overcome differences in observation timing among agents in asynchronous environments, an AOCC structure was proposed to improve MARL learning efficiency.
LG AI Research presented cases of applying Multi-Agent Reinforcement Learning (MARL) to solve real-world problems at AAMAS 2024.
LG AI Research presented multi-agent reinforcement learning research for autonomous driving and industrial site optimization at AAMAS 2024.
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.
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 introduces the latest research trends in foundation models presented at ICLR 2024, focused on interpretability, sustainability, and safety.
This introduces the latest research trends presented at ICLR 2024 regarding the generalization ability, efficiency, and safety of Foundation Models.
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 unveiled EXAONE 3.0 7.8B, an open-source model with excellent English and Korean performance.
LG AI Research has open-sourced EXAONE 3.0 7.8B, an expert-grade AI model with outstanding English and Korean performance.
LG AI Research unveiled ChatEXAONE, an enterprise AI agent based on the open-source LLM EXAONE 3.0.
LG AI Research has unveiled ChatEXAONE, an AI agent based on EXAONE 3.0 that supports enterprise work innovation.
LG AI Research has released EXAONEPath, an open-source model specialized in pathology image analysis.
LG AI Research has unveiled EXAONEPath, an open-source model specialized for histopathology image analysis that achieves both high performance and cost efficiency.
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 presented a new methodology at ICML 2024 that improves learning performance on tabular data.
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