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EXAONE 3.0: An Expert AI for Everyone (with Hyeongu Yun)
In this episode of Neural Search Talks, we welcome Hyeongu Yun from LG AI Research to discuss the newest addition to the EXAONE Universe: EXAONE 3.0. The model demonstrates strong capabilities in both English and Korean, excelling not only in real-world instruction-following scenarios but also achieving impressive results in math and coding benchmarks. Hyeongu shares the team's approach to the development of this model, revealing key training factors that contributed to its success while also highlighting the challenges they faced along the way. We close this episode off with a look at EXAONE's future, as well as Hyeongu's perspective on the evolving role of AI systems.
Check out the Zeta Alpha Neural Discovery platform.
Subscribe to the Zeta Alpha calendar to not miss out on any of our events!
Sources:
- https://lgresearch.ai/blog/view?seq=460
- https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
- https://arxiv.org/abs/2408.03541
Timestamps:
0:00 Intro by Jakub Zavrel
1:37 The journey of the EXAONE project
4:34 The main challenges in the development of EXAONE 3.0
6:37 The secret to achieving great bilingual performance in English & Korean
7:51 How EXAONE 3.0 stacks against other open-source models
9:20 The trade-off between instruction-following and reasoning skills
12:32 How will retrieval and generative models evolve in the future
16:36 Open sourcing and user feedback on EXAONE
19:20 The role of synthetic data in model training
20:57 The role of LLMs as evaluators
23:16 Outro
View all episodes
By Zeta AlphaIn this episode of Neural Search Talks, we welcome Hyeongu Yun from LG AI Research to discuss the newest addition to the EXAONE Universe: EXAONE 3.0. The model demonstrates strong capabilities in both English and Korean, excelling not only in real-world instruction-following scenarios but also achieving impressive results in math and coding benchmarks. Hyeongu shares the team's approach to the development of this model, revealing key training factors that contributed to its success while also highlighting the challenges they faced along the way. We close this episode off with a look at EXAONE's future, as well as Hyeongu's perspective on the evolving role of AI systems.
Check out the Zeta Alpha Neural Discovery platform.
Subscribe to the Zeta Alpha calendar to not miss out on any of our events!
Sources:
- https://lgresearch.ai/blog/view?seq=460
- https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
- https://arxiv.org/abs/2408.03541
Timestamps:
0:00 Intro by Jakub Zavrel
1:37 The journey of the EXAONE project
4:34 The main challenges in the development of EXAONE 3.0
6:37 The secret to achieving great bilingual performance in English & Korean
7:51 How EXAONE 3.0 stacks against other open-source models
9:20 The trade-off between instruction-following and reasoning skills
12:32 How will retrieval and generative models evolve in the future
16:36 Open sourcing and user feedback on EXAONE
19:20 The role of synthetic data in model training
20:57 The role of LLMs as evaluators
23:16 Outro