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EXAONE 3.0: An Expert AI for Everyone (with Hyeongu Yun)

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内容由Zeta Alpha提供。所有播客内容(包括剧集、图形和播客描述)均由 Zeta Alpha 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

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

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Manage episode 451395743 series 3446693
内容由Zeta Alpha提供。所有播客内容(包括剧集、图形和播客描述)均由 Zeta Alpha 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

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

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