Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Lee, Moontae"'
Autor:
Research, LG AI, 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
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 ope
Externí odkaz:
http://arxiv.org/abs/2408.03541
In embodied instruction-following (EIF), the integration of pretrained language models (LMs) as task planners emerges as a significant branch, where tasks are planned at the skill level by prompting LMs with pretrained skills and user instructions. H
Externí odkaz:
http://arxiv.org/abs/2408.01024
Music generation introduces challenging complexities to large language models. Symbolic structures of music often include vertical harmonization as well as horizontal counterpoint, urging various adaptations and enhancements for large-scale Transform
Externí odkaz:
http://arxiv.org/abs/2407.19900
Autor:
Kim, Takyoung, Lee, Kyungjae, Jang, Young Rok, Cho, Ji Yong, Kim, Gangwoo, Cho, Minseok, Lee, Moontae
Interactions with billion-scale large language models typically yield long-form responses due to their extensive parametric capacities, along with retrieval-augmented features. While detailed responses provide insightful viewpoint of a specific subje
Externí odkaz:
http://arxiv.org/abs/2407.01158
Autor:
Kim, Seungone, Suk, Juyoung, Cho, Ji Yong, Longpre, Shayne, Kim, Chaeeun, Yoon, Dongkeun, Son, Guijin, Cho, Yejin, Shafayat, Sheikh, Baek, Jinheon, Park, Sue Hyun, Hwang, Hyeonbin, Jo, Jinkyung, Cho, Hyowon, Shin, Haebin, Lee, Seongyun, Oh, Hanseok, Lee, Noah, Ho, Namgyu, Joo, Se June, Ko, Miyoung, Lee, Yoonjoo, Chae, Hyungjoo, Shin, Jamin, Jang, Joel, Ye, Seonghyeon, Lin, Bill Yuchen, Welleck, Sean, Neubig, Graham, Lee, Moontae, Lee, Kyungjae, Seo, Minjoon
As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently assess LMs using abstract evaluation criteria like helpfulness and harml
Externí odkaz:
http://arxiv.org/abs/2406.05761
Text-to-SQL models are pivotal for making Electronic Health Records (EHRs) accessible to healthcare professionals without SQL knowledge. With the advancements in large language models, these systems have become more adept at translating complex quest
Externí odkaz:
http://arxiv.org/abs/2405.11162
Autor:
Shen, Siqi, Logeswaran, Lajanugen, Lee, Moontae, Lee, Honglak, Poria, Soujanya, Mihalcea, Rada
Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations. However, their understanding of cultural commonsense remains largely unexamined. In this paper, we conduct a comprehensive ex
Externí odkaz:
http://arxiv.org/abs/2405.04655
Autor:
Kim, Seungone, Suk, Juyoung, Longpre, Shayne, Lin, Bill Yuchen, Shin, Jamin, Welleck, Sean, Neubig, Graham, Lee, Moontae, Lee, Kyungjae, Seo, Minjoon
Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from various LMs. However, concerns including transparency, controllability, and affordability strongly motivate the development of open-source LMs specialized in eva
Externí odkaz:
http://arxiv.org/abs/2405.01535
Autor:
Zhang, Yunxiang, Khalifa, Muhammad, Logeswaran, Lajanugen, Kim, Jaekyeom, Lee, Moontae, Lee, Honglak, Wang, Lu
Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated critiques that pinpoint the errors. This work explores whether small (<= 13
Externí odkaz:
http://arxiv.org/abs/2404.17140
Despite the promise of RLHF in aligning LLMs with human preferences, it often leads to superficial alignment, prioritizing stylistic changes over improving downstream performance of LLMs. Underspecified preferences could obscure directions to align t
Externí odkaz:
http://arxiv.org/abs/2403.14238