Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Ye, Seonghyeon"'
Autor:
Longpre, Shayne, Mahari, Robert, Lee, Ariel, Lund, Campbell, Oderinwale, Hamidah, Brannon, William, Saxena, Nayan, Obeng-Marnu, Naana, South, Tobin, Hunter, Cole, Klyman, Kevin, Klamm, Christopher, Schoelkopf, Hailey, Singh, Nikhil, Cherep, Manuel, Anis, Ahmad, Dinh, An, Chitongo, Caroline, Yin, Da, Sileo, Damien, Mataciunas, Deividas, Misra, Diganta, Alghamdi, Emad, Shippole, Enrico, Zhang, Jianguo, Materzynska, Joanna, Qian, Kun, Tiwary, Kush, Miranda, Lester, Dey, Manan, Liang, Minnie, Hamdy, Mohammed, Muennighoff, Niklas, Ye, Seonghyeon, Kim, Seungone, Mohanty, Shrestha, Gupta, Vipul, Sharma, Vivek, Chien, Vu Minh, Zhou, Xuhui, Li, Yizhi, Xiong, Caiming, Villa, Luis, Biderman, Stella, Li, Hanlin, Ippolito, Daphne, Hooker, Sara, Kabbara, Jad, Pentland, Sandy
General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent pro
Externí odkaz:
http://arxiv.org/abs/2407.14933
Autor:
Chang, Hoyeon, Park, Jinho, Ye, Seonghyeon, Yang, Sohee, Seo, Youngkyung, Chang, Du-Seong, Seo, Minjoon
Despite the recent observation that large language models (LLMs) can store substantial factual knowledge, there is a limited understanding of the mechanisms of how they acquire factual knowledge through pretraining. This work addresses this gap by st
Externí odkaz:
http://arxiv.org/abs/2406.11813
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
Autor:
Lee, Changho, Han, Janghoon, Ye, Seonghyeon, Choi, Stanley Jungkyu, Lee, Honglak, Bae, Kyunghoon
Instruction tuning has shown its ability to not only enhance zero-shot generalization across various tasks but also its effectiveness in improving the performance of specific tasks. A crucial aspect in instruction tuning for a particular task is a st
Externí odkaz:
http://arxiv.org/abs/2404.16418
Training on large amounts of rationales (i.e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs). However, acquiring human-authored rationales or augmenting rationales from proprietary models is cos
Externí odkaz:
http://arxiv.org/abs/2404.10346
Autor:
Oh, Hanseok, Lee, Hyunji, Ye, Seonghyeon, Shin, Haebin, Jang, Hansol, Jun, Changwook, Seo, Minjoon
Despite the critical need to align search targets with users' intention, retrievers often only prioritize query information without delving into the users' intended search context. Enhancing the capability of retrievers to understand intentions and p
Externí odkaz:
http://arxiv.org/abs/2402.14334
Autor:
Kim, Yujin, Yoon, Jaehong, Ye, Seonghyeon, Bae, Sangmin, Ho, Namgyu, Hwang, Sung Ju, Yun, Se-young
The dynamic nature of knowledge in an ever-changing world presents challenges for language models trained on static data; the model in the real world often requires not only acquiring new knowledge but also overwriting outdated information into updat
Externí odkaz:
http://arxiv.org/abs/2311.08106
Autor:
Ye, Seonghyeon, Kim, Doyoung, Kim, Sungdong, Hwang, Hyeonbin, Kim, Seungone, Jo, Yongrae, Thorne, James, Kim, Juho, Seo, Minjoon
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction. However, previous studies have mainly focused on coar
Externí odkaz:
http://arxiv.org/abs/2307.10928
Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal prompt among the candidates for a given task but have failed to provide a
Externí odkaz:
http://arxiv.org/abs/2305.14877
Autor:
Kim, Seungone, Joo, Se June, Kim, Doyoung, Jang, Joel, Ye, Seonghyeon, Shin, Jamin, Seo, Minjoon
Language models (LMs) with less than 100B parameters are known to perform poorly on chain-of-thought (CoT) reasoning in contrast to large LMs when solving unseen tasks. In this work, we aim to equip smaller LMs with the step-by-step reasoning capabil
Externí odkaz:
http://arxiv.org/abs/2305.14045