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pro vyhledávání: '"Beauchamp, A"'
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives
Reasoning about time and temporal relations is an integral aspect of human cognition, essential for perceiving the world and navigating our experiences. Though large language models (LLMs) have demonstrated impressive performance in many reasoning ta
Externí odkaz:
http://arxiv.org/abs/2410.05558
Autor:
Clairoux-Trepanier, Vanessa, Beauchamp, Isa-May, Ruellan, Estelle, Paquet-Clouston, Masarah, Paquette, Serge-Olivier, Clay, Eric
Large language models (LLMs) can be used to analyze cyber threat intelligence (CTI) data from cybercrime forums, which contain extensive information and key discussions about emerging cyber threats. However, to date, the level of accuracy and efficie
Externí odkaz:
http://arxiv.org/abs/2408.03354
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 8, p e18476 (2020)
BackgroundElectronic health (eHealth) has the potential to improve health outcomes. However, eHealth systems need to match the eHealth literacy needs of users to be equitably adopted. Socially disadvantaged groups have lower access and skills to use
Externí odkaz:
https://doaj.org/article/128cefc410fc457ea5da90a4e3eebef2
Autor:
Beauchamp, Maxime, Desassis, Nicolas, Johnson, J. Emmanuel, Benaichouche, Simon, Tandeo, Pierre, Fablet, Ronan
The spatio-temporal interpolation of large geophysical datasets has historically been adressed by Optimal Interpolation (OI) and more sophisticated model-based or data-driven DA techniques. In the last ten years, the link established between Stochast
Externí odkaz:
http://arxiv.org/abs/2402.01855
Autor:
Lu, Xiaoding, Liu, Zongyi, Liusie, Adian, Raina, Vyas, Mudupalli, Vineet, Zhang, Yuwen, Beauchamp, William
In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand
Externí odkaz:
http://arxiv.org/abs/2401.02994
Autor:
Devaney, Johanna, Beauchamp, Cecilia
Publikováno v:
Late-Breaking Demo Session of the 24th International Society for Music Information Retrieval Conference (2023)
This paper presents a new method of encoding performance data in MEI using the recently added \texttt{} element. Performance data was extracted using the Automatic Music Performance Analysis and Comparison Toolkit (AMPACT) and encoded as a J
Externí odkaz:
http://arxiv.org/abs/2311.11363
News media often strive to minimize explicit moral language in news articles, yet most articles are dense with moral values as expressed through the reported events themselves. However, values that are reflected in the intricate dynamics among partic
Externí odkaz:
http://arxiv.org/abs/2311.09733
Historically, the interpolation of large geophysical datasets has been tackled using methods like Optimal Interpolation (OI) or model-based data assimilation schemes. However, the recent connection between Stochastic Partial Differential Equations (S
Externí odkaz:
http://arxiv.org/abs/2311.01783
Autor:
Liu, Yujian, Zhang, Xinliang Frederick, Zou, Kaijian, Huang, Ruihong, Beauchamp, Nick, Wang, Lu
Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topi
Externí odkaz:
http://arxiv.org/abs/2310.18827
News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideological positions. Prior work in NLP has only studied media bias via lingui
Externí odkaz:
http://arxiv.org/abs/2310.18768