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40 resources
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Matyáš Boháček, Steven Moore, John Stamp...|Jul 7th, 2023|conferencePaperMatyáš Boháček, Steven Moore, John Stamp...Jul 7th, 2023
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Ziwei Ji, Nayeon Lee, Rita Frieske|Mar 3rd, 2023|journalArticleZiwei Ji, Nayeon Lee, Rita FrieskeMar 3rd, 2023
Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as ve summarization, dialogue generation, and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which...
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Yejin Bang, Samuel Cahyawijaya, Nayeon L...|Feb 28th, 2023|preprintYejin Bang, Samuel Cahyawijaya, Nayeon L...Feb 28th, 2023
This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms...
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Shashank Sonkar, Richard G. Baraniuk, St...|Jul 7th, 2023|conferencePaperShashank Sonkar, Richard G. Baraniuk, St...Jul 7th, 2023
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Md Rayhan Kabir, Fuhua Lin, Steven Moore...|Jul 7th, 2023|conferencePaperMd Rayhan Kabir, Fuhua Lin, Steven Moore...Jul 7th, 2023
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Gautam Yadav, Ying-Jui Tseng, Xiaolin Ni...|Jul 7th, 2023|conferencePaperGautam Yadav, Ying-Jui Tseng, Xiaolin Ni...Jul 7th, 2023
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Qianou Christina Ma, Sherry Tongshuang W...|Jul 7th, 2023|conferencePaperQianou Christina Ma, Sherry Tongshuang W...Jul 7th, 2023
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Benjamin D. Nye, Dillon Mee, Mark G. Cor...|Jul 7th, 2023|conferencePaperBenjamin D. Nye, Dillon Mee, Mark G. Cor...Jul 7th, 2023
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Shouvik Ahmed Antu, Haiyan Chen, Cindy K...|Jul 7th, 2023|conferencePaperShouvik Ahmed Antu, Haiyan Chen, Cindy K...Jul 7th, 2023
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Bor-Chen Kuo, Frederic T. Y. Chang, Zong...|Jul 7th, 2023|conferencePaperBor-Chen Kuo, Frederic T. Y. Chang, Zong...Jul 7th, 2023
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Daniel Leiker, Sara Finnigan, Ashley Ric...|Jul 7th, 2023|conferencePaperDaniel Leiker, Sara Finnigan, Ashley Ric...Jul 7th, 2023
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Alex Goslen, Yeo Jin Kim, Jonathan Rowe,...|Jul 7th, 2023|conferencePaperAlex Goslen, Yeo Jin Kim, Jonathan Rowe,...Jul 7th, 2023
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Pragnya Sridhar, Aidan Doyle, Arav Agarw...|Jul 7th, 2023|conferencePaperPragnya Sridhar, Aidan Doyle, Arav Agarw...Jul 7th, 2023
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Muntasir Hoq, Yang Shi, Juho Leinonen|Jul 7th, 2023|conferencePaperMuntasir Hoq, Yang Shi, Juho LeinonenJul 7th, 2023
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Sai Gattupalli, Will Lee, Danielle Alles...|Jul 7th, 2023|conferencePaperSai Gattupalli, Will Lee, Danielle Alles...Jul 7th, 2023
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Katie Bainbridge, Candace Walkington, Ar...|Jul 7th, 2023|conferencePaperKatie Bainbridge, Candace Walkington, Ar...Jul 7th, 2023
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Kole Norberg, Husni Almoubayyed, Stephen...|Jul 7th, 2023|conferencePaperKole Norberg, Husni Almoubayyed, Stephen...Jul 7th, 2023
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Andrew Caines, Luca Benedetto, Shiva Tas...|Jul 7th, 2023|conferencePaperAndrew Caines, Luca Benedetto, Shiva Tas...Jul 7th, 2023
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Rohan Anil, Andrew M. Dai, Orhan Firat|May 17th, 2023|preprintRohan Anil, Andrew M. Dai, Orhan FiratMay 17th, 2023
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more...
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav...|Aug 15th, 2024|preprintAbhimanyu Dubey, Abhinav Jauhri, Abhinav...Aug 15th, 2024
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language...