AI BriefingLG AI Research 566
Key point
Covers the evolution of LLMs, foundation models, and technical advances through efficient MoE architecture.
Details
Recently, LLM(Large Language Models) have grown rapidly beyond simple question-answering, gaining the ability to solve problems at the level of math competitions and to make use of complex tools. LG AI Research has contributed to these technical advances through its EXAONE 3.0, 3.5, EXAONE Deep models.
The Foundation Model is the core of every LLM application, and architectural decisions made at the initial design stage have a decisive impact on the subsequent post-training and inference stages. Therefore, building an efficient training methodology is extremely important.
For efficient large-scale model training, the MoE(Mixture-of-Experts) architecture is drawing attention. Unlike Dense models that use all parameters, MoE is a Sparse model approach that activates only specific Experts depending on the input token, dramatically reducing computational cost.
The main characteristics of MoE are as follows:
- Efficiency: Enables training of larger-scale models with lower computational cost
- Speed: Fast training and inference speed with high computational efficiency
- Technical challenges: Balanced token routing among experts and ensuring training stability are key
Recently, research such as DeepSeekMoE has been underway, using fine-grained expert segmentation and shared experts to maximize training efficiency.
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