Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Shah, Ankit Parag"'
Autor:
Wang, Yaxuan, Wei, Jiaheng, Liu, Chris Yuhao, Pang, Jinlong, Liu, Quan, Shah, Ankit Parag, Bao, Yujia, Liu, Yang, Wei, Wei
Unlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations. Existing approaches to LLM unlearning often rely on retain data or a r
Externí odkaz:
http://arxiv.org/abs/2410.11143
Autor:
Pang, Jinlong, Wei, Jiaheng, Shah, Ankit Parag, Zhu, Zhaowei, Wang, Yaxuan, Qian, Chen, Liu, Yang, Bao, Yujia, Wei, Wei
Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws. Whi
Externí odkaz:
http://arxiv.org/abs/2410.10877
Autor:
Bao, Yujia, Shah, Ankit Parag, Narang, Neeru, Rivers, Jonathan, Maksey, Rajeev, Guan, Lan, Barrere, Louise N., Evenson, Shelley, Basole, Rahul, Miao, Connie, Mehta, Ankit, Boulay, Fabien, Park, Su Min, Pearson, Natalie E., Joy, Eldhose, He, Tiger, Thakur, Sumiran, Ghosal, Koustav, On, Josh, Morrison, Phoebe, Major, Tim, Wang, Eva Siqi, Escobar, Gina, Wei, Jiaheng, Weerasooriya, Tharindu Cyril, Song, Queena, Lashkevich, Daria, Chen, Clare, Kim, Gyuhak, Yin, Dengpan, Hejna, Don, Nomeli, Mo, Wei, Wei
This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performance metrics, and expert insights. Unlike generic LLMs, FALM leverages a
Externí odkaz:
http://arxiv.org/abs/2406.06559
Audio-visual automatic speech recognition (AV-ASR) models are very effective at reducing word error rates on noisy speech, but require large amounts of transcribed AV training data. Recently, audio-visual self-supervised learning (SSL) approaches hav
Externí odkaz:
http://arxiv.org/abs/2312.09369
Autor:
Larionov, George, Kaden, Zachary, Dureddy, Hima Varsha, Kalejaiye, Gabriel Bayomi T., Kale, Mihir, Potharaju, Srividya Pranavi, Shah, Ankit Parag, Rudnicky, Alexander I
This paper describes the Tartan conversational agent built for the 2018 Alexa Prize Competition. Tartan is a non-goal-oriented socialbot focused around providing users with an engaging and fluent casual conversation. Tartan's key features include an
Externí odkaz:
http://arxiv.org/abs/1812.01260
This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries). The target of the systems is to provide not only the event class but also the event t
Externí odkaz:
http://arxiv.org/abs/1807.10501
Publikováno v:
2015 International Conference on Computing & Network Communications (CoCoNet); 1/1/2015, p333-342, 10p