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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.03541 |
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| _version_ | 1866917179029454848 |
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| author | An, Soyoung Bae, Kyunghoon Choi, Eunbi Choi, Stanley Jungkyu Choi, Yemuk Hong, Seokhee Hong, Yeonjung Hwang, Junwon Jeon, Hyojin Jo, Gerrard Jeongwon Jo, Hyunjik Jung, Jiyeon Jung, Yountae Kim, Euisoon Kim, Hyosang Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Youchul Lee, Edward Hwayoung Lee, Haeju Lee, Honglak Lee, Jinsik Lee, Kyungmin Lee, Moontae Lee, Seungjun Lim, Woohyung Park, Sangha Park, Sooyoun Park, Yongmin Seo, Boseong Yang, Sihoon Yeen, Heuiyeen Yoo, Kyungjae Yun, Hyeongu |
| author_facet | An, Soyoung Bae, Kyunghoon Choi, Eunbi Choi, Stanley Jungkyu Choi, Yemuk Hong, Seokhee Hong, Yeonjung Hwang, Junwon Jeon, Hyojin Jo, Gerrard Jeongwon Jo, Hyunjik Jung, Jiyeon Jung, Yountae Kim, Euisoon Kim, Hyosang Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Youchul Lee, Edward Hwayoung Lee, Haeju Lee, Honglak Lee, Jinsik Lee, Kyungmin Lee, Moontae Lee, Seungjun Lim, Woohyung Park, Sangha Park, Sooyoun Park, Yongmin Seo, Boseong Yang, Sihoon Yeen, Heuiyeen Yoo, Kyungjae Yun, Hyeongu |
| contents | We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly competitive real-world performance with instruction-following capability against other state-of-the-art open models of similar size. Our comparative analysis shows that EXAONE 3.0 excels particularly in Korean, while achieving compelling performance across general tasks and complex reasoning. With its strong real-world effectiveness and bilingual proficiency, we hope that EXAONE keeps contributing to advancements in Expert AI. Our EXAONE 3.0 instruction-tuned model is available at https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03541 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EXAONE 3.0 7.8B Instruction Tuned Language Model An, Soyoung Bae, Kyunghoon Choi, Eunbi Choi, Stanley Jungkyu Choi, Yemuk Hong, Seokhee Hong, Yeonjung Hwang, Junwon Jeon, Hyojin Jo, Gerrard Jeongwon Jo, Hyunjik Jung, Jiyeon Jung, Yountae Kim, Euisoon Kim, Hyosang Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Youchul Lee, Edward Hwayoung Lee, Haeju Lee, Honglak Lee, Jinsik Lee, Kyungmin Lee, Moontae Lee, Seungjun Lim, Woohyung Park, Sangha Park, Sooyoun Park, Yongmin Seo, Boseong Yang, Sihoon Yeen, Heuiyeen Yoo, Kyungjae Yun, Hyeongu Computation and Language Artificial Intelligence We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly competitive real-world performance with instruction-following capability against other state-of-the-art open models of similar size. Our comparative analysis shows that EXAONE 3.0 excels particularly in Korean, while achieving compelling performance across general tasks and complex reasoning. With its strong real-world effectiveness and bilingual proficiency, we hope that EXAONE keeps contributing to advancements in Expert AI. Our EXAONE 3.0 instruction-tuned model is available at https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct. |
| title | EXAONE 3.0 7.8B Instruction Tuned Language Model |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.03541 |