Zobrazeno 1 - 10
of 3 211
pro vyhledávání: '"O'Brien, Sean"'
Autor:
Singh, Ishneet Sukhvinder, Aggarwal, Ritvik, Allahverdiyev, Ibrahim, Taha, Muhammad, Akalin, Aslihan, Zhu, Kevin, O'Brien, Sean
Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related information. Existing methods, which operate at the document level, fail to effe
Externí odkaz:
http://arxiv.org/abs/2410.19572
Autor:
Shimizu, Ryotaro, Wada, Takashi, Wang, Yu, Kruse, Johannes, O'Brien, Sean, HtaungKham, Sai, Song, Linxin, Yoshikawa, Yuya, Saito, Yuki, Tsung, Fugee, Goto, Masayuki, McAuley, Julian
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between the predicted and ground-truth explanations. However, this approach fai
Externí odkaz:
http://arxiv.org/abs/2410.13248
While large language models (LLMs) have rapidly improved their performance on a broad number of tasks, they still often fall short on reasoning tasks. As LLMs become more integrated in diverse real-world tasks, advancing their reasoning capabilities
Externí odkaz:
http://arxiv.org/abs/2410.07839
Despite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with surrounding objects, remains a substantial challenge. Two critical factors in assimilating thes
Externí odkaz:
http://arxiv.org/abs/2410.00487
Autor:
Rawat, Rajat, McBride, Hudson, Nirmal, Dhiyaan, Ghosh, Rajarshi, Moon, Jong, Alamuri, Dhruv, O'Brien, Sean, Zhu, Kevin
As large language models (LLMs) gain traction in healthcare, concerns about their susceptibility to demographic biases are growing. We introduce {DiversityMedQA}, a novel benchmark designed to assess LLM responses to medical queries across diverse pa
Externí odkaz:
http://arxiv.org/abs/2409.01497
Detecting biases in natural language understanding (NLU) for African American Vernacular English (AAVE) is crucial to developing inclusive natural language processing (NLP) systems. To address dialect-induced performance discrepancies, we introduce A
Externí odkaz:
http://arxiv.org/abs/2408.14845
This study addresses the racial biases in facial expression recognition (FER) systems within Large Multimodal Foundation Models (LMFMs). Despite advances in deep learning and the availability of diverse datasets, FER systems often exhibit higher erro
Externí odkaz:
http://arxiv.org/abs/2408.14842
Although LLMs have the potential to transform many fields, they still underperform humans in reasoning tasks. Existing methods induce the model to produce step-by-step calculations, but this research explores the question: Does making the LLM analyze
Externí odkaz:
http://arxiv.org/abs/2407.03624
Autor:
Shim, Jay, Kruttschnitt, Grant, Ma, Alyssa, Kim, Daniel, Chek, Benjamin, Anand, Athul, Zhu, Kevin, O'Brien, Sean
Rapidly increasing model scales coupled with steering methods such as chain-of-thought prompting have led to drastic improvements in language model reasoning. At the same time, models struggle with compositional generalization and are far from human
Externí odkaz:
http://arxiv.org/abs/2407.03600
Autor:
O'Brien, Sean M., Schwamb, Megan E., Gill, Samuel, Watson, Christopher A., Burleigh, Matthew R., Kendall, Alicia, Anderson, David R., Vines, José I., Jenkins, James S., Alves, Douglas R., Trouille, Laura, Ulmer-Moll, Solène, Bryant, Edward M., Apergis, Ioannis, Battley, Matthew P., Bayliss, Daniel, Eisner, Nora L., Gillen, Edward, Goad, Michael R., Günther, Maximilian N., Henderson, Beth A., Heo, Jeong-Eun, Jackson, David G., Lintott, Chris, McCormac, James, Moyano, Maximiliano, Nielsen, Louise D., Osborn, Ares, Saha, Suman, Sefako, Ramotholo R., Stephens, Andrew W., Tilbrook, Rosanna H., Udry, Stéphane, West, Richard G., Wheatley, Peter J., Zivave, Tafadzwa, Lim, See Min, Sainio, Arttu
Publikováno v:
AJ 167 (2024) 238
We present the results from the first two years of the Planet Hunters NGTS citizen science project, which searches for transiting planet candidates in data from the Next Generation Transit Survey (NGTS) by enlisting the help of members of the general
Externí odkaz:
http://arxiv.org/abs/2404.15395