CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart

Autor: Zhao, Bowen, Cheng, Tianhao, Zhang, Yuejie, Cheng, Ying, Feng, Rui, Zhang, Xiaobo

Multimodal Question Answering (MMQA) is crucial as it enables comprehensive understanding and accurate responses by integrating insights from diverse data representations such as tables, charts, and text. Most existing researches in MMQA only focus o

Externí odkaz: http://arxiv.org/abs/2410.21414

GPT-4o System Card

Autor: OpenAI, Hurst, Aaron, Lerer, Adam, Goucher, Adam P., Perelman, Adam, Ramesh, Aditya, Clark, Aidan, Ostrow, AJ, Welihinda, Akila, Hayes, Alan, Radford, Alec, Mądry, Aleksander, Baker-Whitcomb, Alex, Beutel, Alex, Borzunov, Alex, Carney, Alex, Chow, Alex, Kirillov, Alex, Nichol, Alex, Paino, Alex, Renzin, Alex, Passos, Alex Tachard, Kirillov, Alexander, Christakis, Alexi, Conneau, Alexis, Kamali, Ali, Jabri, Allan, Moyer, Allison, Tam, Allison, Crookes, Amadou, Tootoochian, Amin, Tootoonchian, Amin, Kumar, Ananya, Vallone, Andrea, Karpathy, Andrej, Braunstein, Andrew, Cann, Andrew, Codispoti, Andrew, Galu, Andrew, Kondrich, Andrew, Tulloch, Andrew, Mishchenko, Andrey, Baek, Angela, Jiang, Angela, Pelisse, Antoine, Woodford, Antonia, Gosalia, Anuj, Dhar, Arka, Pantuliano, Ashley, Nayak, Avi, Oliver, Avital, Zoph, Barret, Ghorbani, Behrooz, Leimberger, Ben, Rossen, Ben, Sokolowsky, Ben, Wang, Ben, Zweig, Benjamin, Hoover, Beth, Samic, Blake, McGrew, Bob, Spero, Bobby, Giertler, Bogo, Cheng, Bowen, Lightcap, Brad, Walkin, Brandon, Quinn, Brendan, Guarraci, Brian, Hsu, Brian, Kellogg, Bright, Eastman, Brydon, Lugaresi, Camillo, Wainwright, Carroll, Bassin, Cary, Hudson, Cary, Chu, Casey, Nelson, Chad, Li, Chak, Shern, Chan Jun, Conger, Channing, Barette, Charlotte, Voss, Chelsea, Ding, Chen, Lu, Cheng, Zhang, Chong, Beaumont, Chris, Hallacy, Chris, Koch, Chris, Gibson, Christian, Kim, Christina, Choi, Christine, McLeavey, Christine, Hesse, Christopher, Fischer, Claudia, Winter, Clemens, Czarnecki, Coley, Jarvis, Colin, Wei, Colin, Koumouzelis, Constantin, Sherburn, Dane, Kappler, Daniel, Levin, Daniel, Levy, Daniel, Carr, David, Farhi, David, Mely, David, Robinson, David, Sasaki, David, Jin, Denny, Valladares, Dev, Tsipras, Dimitris, Li, Doug, Nguyen, Duc Phong, Findlay, Duncan, Oiwoh, Edede, Wong, Edmund, Asdar, Ehsan, Proehl, Elizabeth, Yang, Elizabeth, Antonow, Eric, Kramer, Eric, Peterson, Eric, Sigler, Eric, Wallace, Eric, Brevdo, Eugene, Mays, Evan, Khorasani, Farzad, Such, Felipe Petroski, Raso, Filippo, Zhang, Francis, von Lohmann, Fred, Sulit, Freddie, Goh, Gabriel, Oden, Gene, Salmon, Geoff, Starace, Giulio, Brockman, Greg, Salman, Hadi, Bao, Haiming, Hu, Haitang, Wong, Hannah, Wang, Haoyu, Schmidt, Heather, Whitney, Heather, Jun, Heewoo, Kirchner, Hendrik, Pinto, Henrique Ponde de Oliveira, Ren, Hongyu, Chang, Huiwen, Chung, Hyung Won, Kivlichan, Ian, O'Connell, Ian, Osband, Ian, Silber, Ian, Sohl, Ian, Okuyucu, Ibrahim, Lan, Ikai, Kostrikov, Ilya, Sutskever, Ilya, Kanitscheider, Ingmar, Gulrajani, Ishaan, Coxon, Jacob, Menick, Jacob, Pachocki, Jakub, Aung, James, Betker, James, Crooks, James, Lennon, James, Kiros, Jamie, Leike, Jan, Park, Jane, Kwon, Jason, Phang, Jason, Teplitz, Jason, Wei, Jason, Wolfe, Jason, Chen, Jay, Harris, Jeff, Varavva, Jenia, Lee, Jessica Gan, Shieh, Jessica, Lin, Ji, Yu, Jiahui, Weng, Jiayi, Tang, Jie, Yu, Jieqi, Jang, Joanne, Candela, Joaquin Quinonero, Beutler, Joe, Landers, Joe, Parish, Joel, Heidecke, Johannes, Schulman, John, Lachman, Jonathan, McKay, Jonathan, Uesato, Jonathan, Ward, Jonathan, Kim, Jong Wook, Huizinga, Joost, Sitkin, Jordan, Kraaijeveld, Jos, Gross, Josh, Kaplan, Josh, Snyder, Josh, Achiam, Joshua, Jiao, Joy, Lee, Joyce, Zhuang, Juntang, Harriman, Justyn, Fricke, Kai, Hayashi, Kai, Singhal, Karan, Shi, Katy, Karthik, Kavin, Wood, Kayla, Rimbach, Kendra, Hsu, Kenny, Nguyen, Kenny, Gu-Lemberg, Keren, Button, Kevin, Liu, Kevin, Howe, Kiel, Muthukumar, Krithika, Luther, Kyle, Ahmad, Lama, Kai, Larry, Itow, Lauren, Workman, Lauren, Pathak, Leher, Chen, Leo, Jing, Li, Guy, Lia, Fedus, Liam, Zhou, Liang, Mamitsuka, Lien, Weng, Lilian, McCallum, Lindsay, Held, Lindsey, Ouyang, Long, Feuvrier, Louis, Zhang, Lu, Kondraciuk, Lukas, Kaiser, Lukasz, Hewitt, Luke, Metz, Luke, Doshi, Lyric, Aflak, Mada, Simens, Maddie, Boyd, Madelaine, Thompson, Madeleine, Dukhan, Marat, Chen, Mark, Gray, Mark, Hudnall, Mark, Zhang, Marvin, Aljubeh, Marwan, Litwin, Mateusz, Zeng, Matthew, Johnson, Max, Shetty, Maya, Gupta, Mayank, Shah, Meghan, Yatbaz, Mehmet, Yang, Meng Jia, Zhong, Mengchao, Glaese, Mia, Chen, Mianna, Janner, Michael, Lampe, Michael, Petrov, Michael, Wu, Michael, Wang, Michele, Fradin, Michelle, Pokrass, Michelle, Castro, Miguel, de Castro, Miguel Oom Temudo, Pavlov, Mikhail, Brundage, Miles, Wang, Miles, Khan, Minal, Murati, Mira, Bavarian, Mo, Lin, Molly, Yesildal, Murat, Soto, Nacho, Gimelshein, Natalia, Cone, Natalie, Staudacher, Natalie, Summers, Natalie, LaFontaine, Natan, Chowdhury, Neil, Ryder, Nick, Stathas, Nick, Turley, Nick, Tezak, Nik, Felix, Niko, Kudige, Nithanth, Keskar, Nitish, Deutsch, Noah, Bundick, Noel, Puckett, Nora, Nachum, Ofir, Okelola, Ola, Boiko, Oleg, Murk, Oleg, Jaffe, Oliver, Watkins, Olivia, Godement, Olivier, Campbell-Moore, Owen, Chao, Patrick, McMillan, Paul, Belov, Pavel, Su, Peng, Bak, Peter, Bakkum, Peter, Deng, Peter, Dolan, Peter, Hoeschele, Peter, Welinder, Peter, Tillet, Phil, Pronin, Philip, Tillet, Philippe, Dhariwal, Prafulla, Yuan, Qiming, Dias, Rachel, Lim, Rachel, Arora, Rahul, Troll, Rajan, Lin, Randall, Lopes, Rapha Gontijo, Puri, Raul, Miyara, Reah, Leike, Reimar, Gaubert, Renaud, Zamani, Reza, Wang, Ricky, Donnelly, Rob, Honsby, Rob, Smith, Rocky, Sahai, Rohan, Ramchandani, Rohit, Huet, Romain, Carmichael, Rory, Zellers, Rowan, Chen, Roy, Chen, Ruby, Nigmatullin, Ruslan, Cheu, Ryan, Jain, Saachi, Altman, Sam, Schoenholz, Sam, Toizer, Sam, Miserendino, Samuel, Agarwal, Sandhini, Culver, Sara, Ethersmith, Scott, Gray, Scott, Grove, Sean, Metzger, Sean, Hermani, Shamez, Jain, Shantanu, Zhao, Shengjia, Wu, Sherwin, Jomoto, Shino, Wu, Shirong, Shuaiqi, Xia, Phene, Sonia, Papay, Spencer, Narayanan, Srinivas, Coffey, Steve, Lee, Steve, Hall, Stewart, Balaji, Suchir, Broda, Tal, Stramer, Tal, Xu, Tao, Gogineni, Tarun, Christianson, Taya, Sanders, Ted, Patwardhan, Tejal, Cunninghman, Thomas, Degry, Thomas, Dimson, Thomas, Raoux, Thomas, Shadwell, Thomas, Zheng, Tianhao, Underwood, Todd, Markov, Todor, Sherbakov, Toki, Rubin, Tom, Stasi, Tom, Kaftan, Tomer, Heywood, Tristan, Peterson, Troy, Walters, Tyce, Eloundou, Tyna, Qi, Valerie, Moeller, Veit, Monaco, Vinnie, Kuo, Vishal, Fomenko, Vlad, Chang, Wayne, Zheng, Weiyi, Zhou, Wenda, Manassra, Wesam, Sheu, Will, Zaremba, Wojciech, Patil, Yash, Qian, Yilei, Kim, Yongjik, Cheng, Youlong, Zhang, Yu, He, Yuchen, Zhang, Yuchen, Jin, Yujia, Dai, Yunxing, Malkov, Yury

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs

Externí odkaz: http://arxiv.org/abs/2410.21276

Does Differential Privacy Impact Bias in Pretrained NLP Models?

Autor: Islam, Md. Khairul, Wang, Andrew, Wang, Tianhao, Ji, Yangfeng, Fox, Judy, Zhao, Jieyu

Differential privacy (DP) is applied when fine-tuning pre-trained large language models (LLMs) to limit leakage of training examples. While most DP research has focused on improving a model's privacy-utility tradeoff, some find that DP can be unfair

Externí odkaz: http://arxiv.org/abs/2410.18749

Neural Scoring, Not Embedding: A Novel Framework for Robust Speaker Verification

Autor: Lin, Wan, Chen, Junhui, Wang, Tianhao, Zhou, Zhenyu, Li, Lantian, Wang, Dong

Current mainstream speaker verification systems are predominantly based on the concept of ``speaker embedding", which transforms variable-length speech signals into fixed-length speaker vectors, followed by verification based on cosine similarity bet

Externí odkaz: http://arxiv.org/abs/2410.16428

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning

Autor: Gao, Fengyu, Zhou, Ruida, Wang, Tianhao, Shen, Cong, Yang, Jing

Large Language Models (LLMs) rely on the contextual information embedded in examples/demonstrations to perform in-context learning (ICL). To mitigate the risk of LLMs potentially leaking private information contained in examples in the prompt, we int

Externí odkaz: http://arxiv.org/abs/2410.12085