The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering

Autor: Chiesurin, Sabrina, Dimakopoulos, Dimitris, Cabezudo, Marco Antonio Sobrevilla, Eshghi, Arash, Papaioannou, Ioannis, Rieser, Verena, Konstas, Ioannis

Large language models are known to produce output which sounds fluent and convincing, but is also often wrong, e.g. "unfaithful" with respect to a rationale as retrieved from a knowledge base. In this paper, we show that task-based systems which exhi

Externí odkaz: http://arxiv.org/abs/2305.16519

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

Autor: Dhole, Kaustubh D., Gangal, Varun, Gehrmann, Sebastian, Gupta, Aadesh, Li, Zhenhao, Mahamood, Saad, Mahendiran, Abinaya, Mille, Simon, Shrivastava, Ashish, Tan, Samson, Wu, Tongshuang, Sohl-Dickstein, Jascha, Choi, Jinho D., Hovy, Eduard, Dusek, Ondrej, Ruder, Sebastian, Anand, Sajant, Aneja, Nagender, Banjade, Rabin, Barthe, Lisa, Behnke, Hanna, Berlot-Attwell, Ian, Boyle, Connor, Brun, Caroline, Cabezudo, Marco Antonio Sobrevilla, Cahyawijaya, Samuel, Chapuis, Emile, Che, Wanxiang, Choudhary, Mukund, Clauss, Christian, Colombo, Pierre, Cornell, Filip, Dagan, Gautier, Das, Mayukh, Dixit, Tanay, Dopierre, Thomas, Dray, Paul-Alexis, Dubey, Suchitra, Ekeinhor, Tatiana, Di Giovanni, Marco, Goyal, Tanya, Gupta, Rishabh, Hamla, Louanes, Han, Sang, Harel-Canada, Fabrice, Honore, Antoine, Jindal, Ishan, Joniak, Przemyslaw K., Kleyko, Denis, Kovatchev, Venelin, Krishna, Kalpesh, Kumar, Ashutosh, Langer, Stefan, Lee, Seungjae Ryan, Levinson, Corey James, Liang, Hualou, Liang, Kaizhao, Liu, Zhexiong, Lukyanenko, Andrey, Marivate, Vukosi, de Melo, Gerard, Meoni, Simon, Meyer, Maxime, Mir, Afnan, Moosavi, Nafise Sadat, Muennighoff, Niklas, Mun, Timothy Sum Hon, Murray, Kenton, Namysl, Marcin, Obedkova, Maria, Oli, Priti, Pasricha, Nivranshu, Pfister, Jan, Plant, Richard, Prabhu, Vinay, Pais, Vasile, Qin, Libo, Raji, Shahab, Rajpoot, Pawan Kumar, Raunak, Vikas, Rinberg, Roy, Roberts, Nicolas, Rodriguez, Juan Diego, Roux, Claude, S., Vasconcellos P. H., Sai, Ananya B., Schmidt, Robin M., Scialom, Thomas, Sefara, Tshephisho, Shamsi, Saqib N., Shen, Xudong, Shi, Haoyue, Shi, Yiwen, Shvets, Anna, Siegel, Nick, Sileo, Damien, Simon, Jamie, Singh, Chandan, Sitelew, Roman, Soni, Priyank, Sorensen, Taylor, Soto, William, Srivastava, Aman, Srivatsa, KV Aditya, Sun, Tony, T, Mukund Varma, Tabassum, A, Tan, Fiona Anting, Teehan, Ryan, Tiwari, Mo, Tolkiehn, Marie, Wang, Athena, Wang, Zijian, Wang, Gloria, Wang, Zijie J., Wei, Fuxuan, Wilie, Bryan, Winata, Genta Indra, Wu, Xinyi, Wydmański, Witold, Xie, Tianbao, Yaseen, Usama, Yee, Michael A., Zhang, Jing, Zhang, Yue

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python

Externí odkaz: http://arxiv.org/abs/2112.02721

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Autor: Gehrmann, Sebastian, Adewumi, Tosin, Aggarwal, Karmanya, Ammanamanchi, Pawan Sasanka, Anuoluwapo, Aremu, Bosselut, Antoine, Chandu, Khyathi Raghavi, Clinciu, Miruna, Das, Dipanjan, Dhole, Kaustubh D., Du, Wanyu, Durmus, Esin, Dušek, Ondřej, Emezue, Chris, Gangal, Varun, Garbacea, Cristina, Hashimoto, Tatsunori, Hou, Yufang, Jernite, Yacine, Jhamtani, Harsh, Ji, Yangfeng, Jolly, Shailza, Kale, Mihir, Kumar, Dhruv, Ladhak, Faisal, Madaan, Aman, Maddela, Mounica, Mahajan, Khyati, Mahamood, Saad, Majumder, Bodhisattwa Prasad, Martins, Pedro Henrique, McMillan-Major, Angelina, Mille, Simon, van Miltenburg, Emiel, Nadeem, Moin, Narayan, Shashi, Nikolaev, Vitaly, Niyongabo, Rubungo Andre, Osei, Salomey, Parikh, Ankur, Perez-Beltrachini, Laura, Rao, Niranjan Ramesh, Raunak, Vikas, Rodriguez, Juan Diego, Santhanam, Sashank, Sedoc, João, Sellam, Thibault, Shaikh, Samira, Shimorina, Anastasia, Cabezudo, Marco Antonio Sobrevilla, Strobelt, Hendrik, Subramani, Nishant, Xu, Wei, Yang, Diyi, Yerukola, Akhila, Zhou, Jiawei

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this m

Externí odkaz: http://arxiv.org/abs/2102.01672

Efficient strategies for hierarchical text classification: External knowledge and auxiliary tasks

Autor: Rojas, Kervy Rivas, Bustamante, Gina, Oncevay, Arturo, Cabezudo, Marco A. Sobrevilla

In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy. Most of the studies have focused on developing novels neural network architectures to de

Externí odkaz: http://arxiv.org/abs/2005.02473

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