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  1. Book ; Online: Approach Intelligent Writing Assistants Usability with Seven Stages of Action

    Bhat, Avinash / Shrivastava, Disha / Guo, Jin L. C.

    2023  

    Abstract: Despite the potential of Large Language Models (LLMs) as writing assistants, they are plagued by issues like coherence and fluency of the model output, trustworthiness, ownership of the generated content, and predictability of model performance, thereby ... ...

    Abstract Despite the potential of Large Language Models (LLMs) as writing assistants, they are plagued by issues like coherence and fluency of the model output, trustworthiness, ownership of the generated content, and predictability of model performance, thereby limiting their usability. In this position paper, we propose to adopt Norman's seven stages of action as a framework to approach the interaction design of intelligent writing assistants. We illustrate the framework's applicability to writing tasks by providing an example of software tutorial authoring. The paper also discusses the framework as a tool to synthesize research on the interaction design of LLM-based tools and presents examples of tools that support the stages of action. Finally, we briefly outline the potential of a framework for human-LLM interaction research.

    Comment: The Second Workshop on Intelligent and Interactive Writing Assistants co-located with The ACM CHI Conference on Human Factors in Computing Systems (CHI 2023)
    Keywords Computer Science - Human-Computer Interaction ; Computer Science - Computation and Language ; Computer Science - Machine Learning
    Subject code 420
    Publishing date 2023-04-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Repository-Level Prompt Generation for Large Language Models of Code

    Shrivastava, Disha / Larochelle, Hugo / Tarlow, Daniel

    2022  

    Abstract: With the success of large language models (LLMs) of code and their use as code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge in the prompt design process become important. In this work, we propose a ... ...

    Abstract With the success of large language models (LLMs) of code and their use as code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge in the prompt design process become important. In this work, we propose a framework called Repo-Level Prompt Generator that learns to generate example-specific prompts using prompt proposals. The prompt proposals take context from the entire repository, thereby incorporating both the structure of the repository and the context from other relevant files (e.g. imports, parent class files). Our technique doesn't require any access to the weights of the LLM, making it applicable in cases where we only have black-box access to the LLM. We conduct experiments on the task of single-line code-autocompletion using code repositories taken from Google Code archives. We demonstrate that an oracle constructed from our prompt proposals gives a remarkably high relative improvement of 36% over Codex, showing the quality of these proposals. Further, we show that when we train a model to predict a prompt proposal, we can achieve significant performance gains over Codex and other baselines. We release our code, data, and trained checkpoints at: \url{https://github.com/shrivastavadisha/repo_level_prompt_generation}.

    Comment: ICML 2023 (Camera-Ready version)
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Programming Languages ; Computer Science - Software Engineering
    Subject code 005
    Publishing date 2022-06-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Learning to Combine Per-Example Solutions for Neural Program Synthesis

    Shrivastava, Disha / Larochelle, Hugo / Tarlow, Daniel

    2021  

    Abstract: The goal of program synthesis from examples is to find a computer program that is consistent with a given set of input-output examples. Most learning-based approaches try to find a program that satisfies all examples at once. Our work, by contrast, ... ...

    Abstract The goal of program synthesis from examples is to find a computer program that is consistent with a given set of input-output examples. Most learning-based approaches try to find a program that satisfies all examples at once. Our work, by contrast, considers an approach that breaks the problem into two stages: (a) find programs that satisfy only one example, and (b) leverage these per-example solutions to yield a program that satisfies all examples. We introduce the Cross Aggregator neural network module based on a multi-head attention mechanism that learns to combine the cues present in these per-example solutions to synthesize a global solution. Evaluation across programs of different lengths and under two different experimental settings reveal that when given the same time budget, our technique significantly improves the success rate over PCCoder [Zohar et. al 2018] and other ablation baselines. The code, data and trained models for our work can be found at https://github.com/shrivastavadisha/N-PEPS.

    Comment: NeurIPS 2021 (camera-ready version)
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Programming Languages ; Computer Science - Software Engineering
    Subject code 006
    Publishing date 2021-06-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: RepoFusion

    Shrivastava, Disha / Kocetkov, Denis / de Vries, Harm / Bahdanau, Dzmitry / Scholak, Torsten

    Training Code Models to Understand Your Repository

    2023  

    Abstract: Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the context present in the repository (e.g., imports, parent classes, files with similar names, etc.), thereby ... ...

    Abstract Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the context present in the repository (e.g., imports, parent classes, files with similar names, etc.), thereby producing inaccurate code completions. This effect is more pronounced when using these assistants for repositories that the model has not seen during training, such as proprietary software or work-in-progress code projects. Recent work has shown the promise of using context from the repository during inference. In this work, we extend this idea and propose RepoFusion, a framework to train models to incorporate relevant repository context. Experiments on single-line code completion show that our models trained with repository context significantly outperform much larger code models as CodeGen-16B-multi ($\sim73\times$ larger) and closely match the performance of the $\sim 70\times$ larger StarCoderBase model that was trained with the Fill-in-the-Middle objective. We find these results to be a novel and compelling demonstration of the gains that training with repository context can bring. We carry out extensive ablation studies to investigate the impact of design choices such as context type, number of contexts, context length, and initialization within our framework. Lastly, we release Stack-Repo, a dataset of 200 Java repositories with permissive licenses and near-deduplicated files that are augmented with three types of repository contexts. Additionally, we are making available the code and trained checkpoints for our work. Our released resources can be found at \url{https://huggingface.co/RepoFusion}.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Programming Languages ; Computer Science - Software Engineering
    Subject code 005
    Publishing date 2023-06-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: On-the-Fly Adaptation of Source Code Models using Meta-Learning

    Shrivastava, Disha / Larochelle, Hugo / Tarlow, Daniel

    2020  

    Abstract: The ability to adapt to unseen, local contexts is an important challenge that successful models of source code must overcome. One of the most popular approaches for the adaptation of such models is dynamic evaluation. With dynamic evaluation, when ... ...

    Abstract The ability to adapt to unseen, local contexts is an important challenge that successful models of source code must overcome. One of the most popular approaches for the adaptation of such models is dynamic evaluation. With dynamic evaluation, when running a model on an unseen file, the model is updated immediately after having observed each token in that file. In this work, we propose instead to frame the problem of context adaptation as a meta-learning problem. We aim to train a base source code model that is best able to learn from information in a file to deliver improved predictions of missing tokens. Unlike dynamic evaluation, this formulation allows us to select more targeted information (support tokens) for adaptation, that is both before and after a target hole in a file. We consider an evaluation setting that we call line-level maintenance, designed to reflect the downstream task of code auto-completion in an IDE. Leveraging recent developments in meta-learning such as first-order MAML and Reptile, we demonstrate improved performance in experiments on a large scale Java GitHub corpus, compared to other adaptation baselines including dynamic evaluation. Moreover, our analysis shows that, compared to a non-adaptive baseline, our approach improves performance on identifiers and literals by 44\% and 15\%, respectively.

    Comment: This paper has been withdrawn because we found a bug in the FOMAML implementation that invalidates some of the key claims in the paper
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Software Engineering ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-03-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Transfer Learning by Modeling a Distribution over Policies

    Shrivastava, Disha / Dhekane, Eeshan Gunesh / Islam, Riashat

    2019  

    Abstract: Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning. In this work, we build on the idea of modeling a distribution over policies in a Bayesian deep reinforcement learning setup to propose ... ...

    Abstract Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning. In this work, we build on the idea of modeling a distribution over policies in a Bayesian deep reinforcement learning setup to propose a transfer strategy. Recent works have shown to induce diversity in the learned policies by maximizing the entropy of a distribution of policies (Bachman et al., 2018; Garnelo et al., 2018) and thus, we postulate that our proposed approach leads to faster exploration resulting in improved transfer learning. We support our hypothesis by demonstrating favorable experimental results on a variety of settings on fully-observable GridWorld and partially observable MiniGrid (Chevalier-Boisvert et al., 2018) environments.

    Comment: Accepted at the ICML 2019 workshop on Multi-Task and Lifelong Reinforcement Learning
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2019-06-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Gemini

    Gemini Team / Anil, Rohan / Borgeaud, Sebastian / Wu, Yonghui / Alayrac, Jean-Baptiste / Yu, Jiahui / Soricut, Radu / Schalkwyk, Johan / Dai, Andrew M. / Hauth, Anja / Millican, Katie / Silver, David / Petrov, Slav / Johnson, Melvin / Antonoglou, Ioannis / Schrittwieser, Julian / Glaese, Amelia / Chen, Jilin / Pitler, Emily /
    Lillicrap, Timothy / Lazaridou, Angeliki / Firat, Orhan / Molloy, James / Isard, Michael / Barham, Paul R. / Hennigan, Tom / Lee, Benjamin / Viola, Fabio / Reynolds, Malcolm / Xu, Yuanzhong / Doherty, Ryan / Collins, Eli / Meyer, Clemens / Rutherford, Eliza / Moreira, Erica / Ayoub, Kareem / Goel, Megha / Tucker, George / Piqueras, Enrique / Krikun, Maxim / Barr, Iain / Savinov, Nikolay / Danihelka, Ivo / Roelofs, Becca / White, Anaïs / Andreassen, Anders / von Glehn, Tamara / Yagati, Lakshman / Kazemi, Mehran / Gonzalez, Lucas / Khalman, Misha / Sygnowski, Jakub / Frechette, Alexandre / Smith, Charlotte / Culp, Laura / Proleev, Lev / Luan, Yi / Chen, Xi / Lottes, James / Schucher, Nathan / Lebron, Federico / Rrustemi, Alban / Clay, Natalie / Crone, Phil / Kocisky, Tomas / Zhao, Jeffrey / Perz, Bartek / Yu, Dian / Howard, Heidi / Bloniarz, Adam / Rae, Jack W. / Lu, Han / Sifre, Laurent / Maggioni, Marcello / Alcober, Fred / Garrette, Dan / Barnes, Megan / Thakoor, Shantanu / Austin, Jacob / Barth-Maron, Gabriel / Wong, William / Joshi, Rishabh / Chaabouni, Rahma / Fatiha, Deeni / Ahuja, Arun / Liu, Ruibo / Li, Yunxuan / Cogan, Sarah / Chen, Jeremy / Jia, Chao / Gu, Chenjie / Zhang, Qiao / Grimstad, Jordan / Hartman, Ale Jakse / Chadwick, Martin / Tomar, Gaurav Singh / Garcia, Xavier / Senter, Evan / Taropa, Emanuel / Pillai, Thanumalayan Sankaranarayana / Devlin, Jacob / Laskin, Michael / Casas, Diego de Las / Valter, Dasha / Tao, Connie / Blanco, Lorenzo / Badia, Adrià Puigdomènech / Reitter, David / Chen, Mianna / Brennan, Jenny / Rivera, Clara / Brin, Sergey / Iqbal, Shariq / Surita, Gabriela / Labanowski, Jane / Rao, Abhi / Winkler, Stephanie / Parisotto, Emilio / Gu, Yiming / Olszewska, Kate / Zhang, Yujing / Addanki, Ravi / Miech, Antoine / Louis, Annie / Shafey, Laurent El / Teplyashin, Denis / Brown, Geoff / Catt, Elliot / Attaluri, Nithya / Balaguer, Jan / Xiang, Jackie / Wang, Pidong / Ashwood, Zoe / Briukhov, Anton / Webson, Albert / Ganapathy, Sanjay / Sanghavi, Smit / Kannan, Ajay / Chang, Ming-Wei / Stjerngren, Axel / Djolonga, Josip / Sun, Yuting / Bapna, Ankur / Aitchison, Matthew / Pejman, Pedram / Michalewski, Henryk / Yu, Tianhe / Wang, Cindy / Love, Juliette / Ahn, Junwhan / Bloxwich, Dawn / Han, Kehang / Humphreys, Peter / Sellam, Thibault / Bradbury, James / Godbole, Varun / Samangooei, Sina / Damoc, Bogdan / Kaskasoli, Alex / Arnold, Sébastien M. R. / Vasudevan, Vijay / Agrawal, Shubham / Riesa, Jason / Lepikhin, Dmitry / Tanburn, Richard / Srinivasan, Srivatsan / Lim, Hyeontaek / Hodkinson, Sarah / Shyam, Pranav / Ferret, Johan / Hand, Steven / Garg, Ankush / Paine, Tom Le / Li, Jian / Li, Yujia / Giang, Minh / Neitz, Alexander / Abbas, Zaheer / York, Sarah / Reid, Machel / Cole, Elizabeth / Chowdhery, Aakanksha / Das, Dipanjan / Rogozińska, Dominika / Nikolaev, Vitaly / Sprechmann, Pablo / Nado, Zachary / Zilka, Lukas / Prost, Flavien / He, Luheng / Monteiro, Marianne / Mishra, Gaurav / Welty, Chris / Newlan, Josh / Jia, Dawei / Allamanis, Miltiadis / Hu, Clara Huiyi / de Liedekerke, Raoul / Gilmer, Justin / Saroufim, Carl / Rijhwani, Shruti / Hou, Shaobo / Shrivastava, Disha / Baddepudi, Anirudh / Goldin, Alex / Ozturel, Adnan / Cassirer, Albin / Xu, Yunhan / Sohn, Daniel / Sachan, Devendra / Amplayo, Reinald Kim / Swanson, Craig / Petrova, Dessie / Narayan, Shashi / Guez, Arthur / Brahma, Siddhartha / Landon, Jessica / Patel, Miteyan / Zhao, Ruizhe / Villela, Kevin / Wang, Luyu / Jia, Wenhao / Rahtz, Matthew / Giménez, Mai / Yeung, Legg / Lin, Hanzhao / Keeling, James / Georgiev, Petko / Mincu, Diana / Wu, Boxi / Haykal, Salem / Saputro, Rachel / Vodrahalli, Kiran / Qin, James / Cankara, Zeynep / Sharma, Abhanshu / Fernando, Nick / Hawkins, Will / Neyshabur, Behnam / Kim, Solomon / Hutter, Adrian / Agrawal, Priyanka / Castro-Ros, Alex / Driessche, George van den / Wang, Tao / Yang, Fan / Chang, Shuo-yiin / Komarek, Paul / McIlroy, Ross / Lučić, Mario / Zhang, Guodong / Farhan, Wael / Sharman, Michael / Natsev, Paul / Michel, Paul / Cheng, Yong / Bansal, Yamini / Qiao, Siyuan / Cao, Kris / Shakeri, Siamak / Butterfield, Christina / Chung, Justin / Rubenstein, Paul Kishan / Agrawal, Shivani / Mensch, Arthur / Soparkar, Kedar / Lenc, Karel / Chung, Timothy / Pope, Aedan / Maggiore, Loren / Kay, Jackie / Jhakra, Priya / Wang, Shibo / Maynez, Joshua / Phuong, Mary / Tobin, Taylor / Tacchetti, Andrea / Trebacz, Maja / Robinson, Kevin / Katariya, Yash / Riedel, Sebastian / Bailey, Paige / Xiao, Kefan / Ghelani, Nimesh / Aroyo, Lora / Slone, Ambrose / Houlsby, Neil / Xiong, Xuehan / Yang, Zhen / Gribovskaya, Elena / Adler, Jonas / Wirth, Mateo / Lee, Lisa / Li, Music / Kagohara, Thais / Pavagadhi, Jay / Bridgers, Sophie / Bortsova, Anna / Ghemawat, Sanjay / Ahmed, Zafarali / Liu, Tianqi / Powell, Richard / Bolina, Vijay / Iinuma, Mariko / Zablotskaia, Polina / Besley, James / Chung, Da-Woon / Dozat, Timothy / Comanescu, Ramona / Si, Xiance / Greer, Jeremy / Su, Guolong / Polacek, Martin / Kaufman, Raphaël Lopez / Tokumine, Simon / Hu, Hexiang / Buchatskaya, Elena / Miao, Yingjie / Elhawaty, Mohamed / Siddhant, Aditya / Tomasev, Nenad / Xing, Jinwei / Greer, Christina / Miller, Helen / Ashraf, Shereen / Roy, Aurko / Zhang, Zizhao / Ma, Ada / Filos, Angelos / Besta, Milos / Blevins, Rory / Klimenko, Ted / Yeh, Chih-Kuan / Changpinyo, Soravit / Mu, Jiaqi / Chang, Oscar / Pajarskas, Mantas / Muir, Carrie / Cohen, Vered / Lan, Charline Le / Haridasan, Krishna / Marathe, Amit / Hansen, Steven / Douglas, Sholto / Samuel, Rajkumar / Wang, Mingqiu / Austin, Sophia / Lan, Chang / Jiang, Jiepu / Chiu, Justin / Lorenzo, Jaime Alonso / Sjösund, Lars Lowe / Cevey, Sébastien / Gleicher, Zach / Avrahami, Thi / Boral, Anudhyan / Srinivasan, Hansa / Selo, Vittorio / May, Rhys / Aisopos, Konstantinos / Hussenot, Léonard / Soares, Livio Baldini / Baumli, Kate / Chang, Michael B. / Recasens, Adrià / Caine, Ben / Pritzel, Alexander / Pavetic, Filip / Pardo, Fabio / Gergely, Anita / Frye, Justin / Ramasesh, Vinay / Horgan, Dan / Badola, Kartikeya / Kassner, Nora / Roy, Subhrajit / Dyer, Ethan / Campos, Víctor / Tomala, Alex / Tang, Yunhao / Badawy, Dalia El / White, Elspeth / Mustafa, Basil / Lang, Oran / Jindal, Abhishek / Vikram, Sharad / Gong, Zhitao / Caelles, Sergi / Hemsley, Ross / Thornton, Gregory / Feng, Fangxiaoyu / Stokowiec, Wojciech / Zheng, Ce / Thacker, Phoebe / Ünlü, Çağlar / Zhang, Zhishuai / Saleh, Mohammad / Svensson, James / Bileschi, Max / Patil, Piyush / Anand, Ankesh / Ring, Roman / Tsihlas, Katerina / Vezer, Arpi / Selvi, Marco / Shevlane, Toby / Rodriguez, Mikel / Kwiatkowski, Tom / Daruki, Samira / Rong, Keran / Dafoe, Allan / FitzGerald, Nicholas / Gu-Lemberg, Keren / Khan, Mina / Hendricks, Lisa Anne / Pellat, Marie / Feinberg, Vladimir / Cobon-Kerr, James / Sainath, Tara / Rauh, Maribeth / Hashemi, Sayed Hadi / Ives, Richard / Hasson, Yana / Li, YaGuang / Noland, Eric / Cao, Yuan / Byrd, Nathan / Hou, Le / Wang, Qingze / Sottiaux, Thibault / Paganini, Michela / Lespiau, Jean-Baptiste / Moufarek, Alexandre / Hassan, Samer / Shivakumar, Kaushik / van Amersfoort, Joost / Mandhane, Amol / Joshi, Pratik / Goyal, Anirudh / Tung, Matthew / Brock, Andrew / Sheahan, Hannah / Misra, Vedant / Li, Cheng / Rakićević, Nemanja / Dehghani, Mostafa / Liu, Fangyu / Mittal, Sid / Oh, Junhyuk / Noury, Seb / Sezener, Eren / Huot, Fantine / Lamm, Matthew / De Cao, Nicola / Chen, Charlie / Elsayed, Gamaleldin / Chi, Ed / Mahdieh, Mahdis / Tenney, Ian / Hua, Nan / Petrychenko, Ivan / Kane, Patrick / Scandinaro, Dylan / Jain, Rishub / Uesato, Jonathan / Datta, Romina / Sadovsky, Adam / Bunyan, Oskar / Rabiej, Dominik / Wu, Shimu / Zhang, John / Vasudevan, Gautam / Leurent, Edouard / Alnahlawi, Mahmoud / Georgescu, Ionut / Wei, Nan / Zheng, Ivy / Chan, Betty / Rabinovitch, Pam G / Stanczyk, Piotr / Zhang, Ye / Steiner, David / Naskar, Subhajit / Azzam, Michael / Johnson, Matthew / Paszke, Adam / Chiu, Chung-Cheng / Elias, Jaume Sanchez / Mohiuddin, Afroz / Muhammad, Faizan / Miao, Jin / Lee, Andrew / Vieillard, Nino / Potluri, Sahitya / Park, Jane / Davoodi, Elnaz / Zhang, Jiageng / Stanway, Jeff / Garmon, Drew / Karmarkar, Abhijit / Dong, Zhe / Lee, Jong / Kumar, Aviral / Zhou, Luowei / Evens, Jonathan / Isaac, William / Chen, Zhe / Jia, Johnson / Levskaya, Anselm / Zhu, Zhenkai / Gorgolewski, Chris / Grabowski, Peter / Mao, Yu / Magni, Alberto / Yao, Kaisheng / Snaider, Javier / Casagrande, Norman / Suganthan, Paul / Palmer, Evan / Irving, Geoffrey / Loper, Edward / Faruqui, Manaal / Arkatkar, Isha / Chen, Nanxin / Shafran, Izhak / Fink, Michael / Castaño, Alfonso / Giannoumis, Irene / Kim, Wooyeol / Rybiński, Mikołaj / Sreevatsa, Ashwin / Prendki, Jennifer / Soergel, David / Goedeckemeyer, Adrian / Gierke, Willi / Jafari, Mohsen / Gaba, Meenu / Wiesner, Jeremy / Wright, Diana Gage / Wei, Yawen / Vashisht, Harsha / Kulizhskaya, Yana / Hoover, Jay / Le, Maigo / Li, Lu / Iwuanyanwu, Chimezie / Liu, Lu / Ramirez, Kevin / Khorlin, Andrey / Cui, Albert / LIN, Tian / Georgiev, Marin / Wu, Marcus / Aguilar, Ricardo / Pallo, Keith / Chakladar, Abhishek / Repina, Alena / Wu, Xihui / van der Weide, Tom / Ponnapalli, Priya / Kaplan, Caroline / Simsa, Jiri / Li, Shuangfeng / Dousse, Olivier / Piper, Jeff / Ie, Nathan / Lui, Minnie / Pasumarthi, Rama / Lintz, Nathan / Vijayakumar, Anitha / Thiet, Lam Nguyen / Andor, Daniel / Valenzuela, Pedro / Paduraru, Cosmin / Peng, Daiyi / Lee, Katherine / Zhang, Shuyuan / Greene, Somer / Nguyen, Duc Dung / Kurylowicz, Paula / Velury, Sarmishta / Krause, Sebastian / Hardin, Cassidy / Dixon, Lucas / Janzer, Lili / Choo, Kiam / Feng, Ziqiang / Zhang, Biao / Singhal, Achintya / Latkar, Tejasi / Zhang, Mingyang / Le, Quoc / Abellan, Elena Allica / Du, Dayou / McKinnon, Dan / Antropova, Natasha / Bolukbasi, Tolga / Keller, Orgad / Reid, David / Finchelstein, Daniel / Raad, Maria Abi / Crocker, Remi / Hawkins, Peter / Dadashi, Robert / Gaffney, Colin / Lall, Sid / Franko, Ken / Filonov, Egor / Bulanova, Anna / Leblond, Rémi / Yadav, Vikas / Chung, Shirley / Askham, Harry / Cobo, Luis C. / Xu, Kelvin / Fischer, Felix / Xu, Jun / Sorokin, Christina / Alberti, Chris / Lin, Chu-Cheng / Evans, Colin / Zhou, Hao / Dimitriev, Alek / Forbes, Hannah / Banarse, Dylan / Tung, Zora / Liu, Jeremiah / Omernick, Mark / Bishop, Colton / Kumar, Chintu / Sterneck, Rachel / Foley, Ryan / Jain, Rohan / Mishra, Swaroop / Xia, Jiawei / Bos, Taylor / Cideron, Geoffrey / Amid, Ehsan / Piccinno, Francesco / Wang, Xingyu / Banzal, Praseem / Gurita, Petru / Noga, Hila / Shah, Premal / Mankowitz, Daniel J. / Polozov, Alex / Kushman, Nate / Krakovna, Victoria / Brown, Sasha / Bateni, MohammadHossein / Duan, Dennis / Firoiu, Vlad / Thotakuri, Meghana / Natan, Tom / Mohananey, Anhad / Geist, Matthieu / Mudgal, Sidharth / Girgin, Sertan / Li, Hui / Ye, Jiayu / Roval, Ofir / Tojo, Reiko / Kwong, Michael / Lee-Thorp, James / Yew, Christopher / Yuan, Quan / Bagri, Sumit / Sinopalnikov, Danila / Ramos, Sabela / Mellor, John / Sharma, Abhishek / Severyn, Aliaksei / Lai, Jonathan / Wu, Kathy / Cheng, Heng-Tze / Miller, David / Sonnerat, Nicolas / Vnukov, Denis / Greig, Rory / Beattie, Jennifer / Caveness, Emily / Bai, Libin / Eisenschlos, Julian / Korchemniy, Alex / Tsai, Tomy / Jasarevic, Mimi / Kong, Weize / Dao, Phuong / Zheng, Zeyu / Liu, Frederick / Zhu, Rui / Geller, Mark / Teh, Tian Huey / Sanmiya, Jason / Gladchenko, Evgeny / Trdin, Nejc / Sozanschi, Andrei / Toyama, Daniel / Rosen, Evan / Tavakkol, Sasan / Xue, Linting / Elkind, Chen / Woodman, Oliver / Carpenter, John / Papamakarios, George / Kemp, Rupert / Kafle, Sushant / Grunina, Tanya / Sinha, Rishika / Talbert, Alice / Goyal, Abhimanyu / Wu, Diane / Owusu-Afriyie, Denese / Du, Cosmo / Thornton, Chloe / Pont-Tuset, Jordi / Narayana, Pradyumna / Li, Jing / Fatehi, Sabaer / Wieting, John / Ajmeri, Omar / Uria, Benigno / Zhu, Tao / Ko, Yeongil / Knight, Laura / Héliou, Amélie / Niu, Ning / Gu, Shane / Pang, Chenxi / Tran, Dustin / Li, Yeqing / Levine, Nir / Stolovich, Ariel / Kalb, Norbert / Santamaria-Fernandez, Rebeca / Goenka, Sonam / Yustalim, Wenny / Strudel, Robin / Elqursh, Ali / Lakshminarayanan, Balaji / Deck, Charlie / Upadhyay, Shyam / Lee, Hyo / Dusenberry, Mike / Li, Zonglin / Wang, Xuezhi / Levin, Kyle / Hoffmann, Raphael / Holtmann-Rice, Dan / Bachem, Olivier / Yue, Summer / Arora, Sho / Malmi, Eric / Mirylenka, Daniil / Tan, Qijun / Koh, Christy / Yeganeh, Soheil Hassas / Põder, Siim / Zheng, Steven / Pongetti, Francesco / Tariq, Mukarram / Sun, Yanhua / Ionita, Lucian / Seyedhosseini, Mojtaba / Tafti, Pouya / Kotikalapudi, Ragha / Liu, Zhiyu / Gulati, Anmol / Liu, Jasmine / Ye, Xinyu / Chrzaszcz, Bart / Wang, Lily / Sethi, Nikhil / Li, Tianrun / Brown, Ben / Singh, Shreya / Fan, Wei / Parisi, Aaron / Stanton, Joe / Kuang, Chenkai / Koverkathu, Vinod / Choquette-Choo, Christopher A. / Li, Yunjie / Lu, TJ / Ittycheriah, Abe / Shroff, Prakash / Sun, Pei / Varadarajan, Mani / Bahargam, Sanaz / Willoughby, Rob / Gaddy, David / Dasgupta, Ishita / Desjardins, Guillaume / Cornero, Marco / Robenek, Brona / Mittal, Bhavishya / Albrecht, Ben / Shenoy, Ashish / Moiseev, Fedor / Jacobsson, Henrik / Ghaffarkhah, Alireza / Rivière, Morgane / Walton, Alanna / Crepy, Clément / Parrish, Alicia / Liu, Yuan / Zhou, Zongwei / Farabet, Clement / Radebaugh, Carey / Srinivasan, Praveen / van der Salm, 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Chen, Mia / Milan, Kieran / Mikulik, Vladimir / Strohman, Trevor / Franco, Juliana / Green, Tim / Hassabis, Demis / Kavukcuoglu, Koray / Dean, Jeffrey / Vinyals, Oriol

    A Family of Highly Capable Multimodal Models

    2023  

    Abstract: This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from ... ...

    Abstract This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of Gemini models in cross-modal reasoning and language understanding will enable a wide variety of use cases and we discuss our approach toward deploying them responsibly to users.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-12-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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