Zobrazeno 1 - 10
of 254 486
pro vyhledávání: '"Friedman, Or"'
Large language models can absorb a massive amount of knowledge through pretraining, but pretraining is inefficient for acquiring long-tailed or specialized facts. Therefore, fine-tuning on specialized or new knowledge that reflects changes in the wor
Externí odkaz:
http://arxiv.org/abs/2411.07175
Autor:
Friedman, Scott E., Benkler, Noam, Mosaphir, Drisana, Rye, Jeffrey, Schmer-Galunder, Sonja M., Goldwater, Micah, McLure, Matthew, Wheelock, Ruta, Gottlieb, Jeremy, Goldman, Robert P., Miller, Christopher
Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the
Externí odkaz:
http://arxiv.org/abs/2411.05040
Autor:
Cai, Jinjin, Wang, Ruiqi, Zhao, Dezhong, Yuan, Ziqin, McKenna, Victoria, Friedman, Aaron, Foot, Rachel, Storey, Susan, Boente, Ryan, Vhaduri, Sudip, Min, Byung-Cheol
Audio-based disease prediction is emerging as a promising supplement to traditional medical diagnosis methods, facilitating early, convenient, and non-invasive disease detection and prevention. Multimodal fusion, which integrates features from variou
Externí odkaz:
http://arxiv.org/abs/2410.09289
We introduce a framework that allows for the exact analytic treatment of quantum dynamics subject to coherent noise. The noise is modeled via unitary evolution under a Hamiltonian drawn from a random-matrix ensemble for arbitrary Hilbert-space dimens
Externí odkaz:
http://arxiv.org/abs/2410.07321
Light cone selection effects on cosmic observables must be precisely accounted for in the next generation of surveys, including the Dark Energy Spectroscopic Instrument (DESI) survey. This will allow us to correctly model the data and extract subtle
Externí odkaz:
http://arxiv.org/abs/2410.04705
In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist w
Externí odkaz:
http://arxiv.org/abs/2410.01792
Autor:
Wines, Daniel, Ibrahim, Akram, Gudibandla, Nishwanth, Adel, Tehseen, Abel, Frank M., Jois, Sharadh, Saritas, Kayahan, Krogel, Jaron T., Yin, Li, Berlijn, Tom, Hanbicki, Aubrey T., Stephen, Gregory M., Friedman, Adam L., Krylyuk, Sergiy, Davydov, Albert, Donovan, Brian, Jamer, Michelle E., Walker, Angela R. Hight, Choudhary, Kamal, Tavazza, Francesca, Ataca, Can
Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion
Externí odkaz:
http://arxiv.org/abs/2409.19082
Autor:
Berijanian, Maryam, Dork, Spencer, Singh, Kuldeep, Millikan, Michael Riley, Riggs, Ashlin, Swaminathan, Aadarsh, Gibbs, Sarah L., Friedman, Scott E., Brugnone, Nathan
Understanding and modeling collective intelligence is essential for addressing complex social systems. Directed graphs called fuzzy cognitive maps (FCMs) offer a powerful tool for encoding causal mental models, but extracting high-integrity FCMs from
Externí odkaz:
http://arxiv.org/abs/2409.18911
Stochastic resetting, a method for accelerating target search in random processes, often incurs temporal and energetic costs. For a diffusing particle, a lower bound exists for the energetic cost of reaching the target, which is attained at low reset
Externí odkaz:
http://arxiv.org/abs/2409.10108
Autor:
Friedman, Roy, Chowers, Rhea
We propose a new method for generating realistic datasets with distribution shifts using any decoder-based generative model. Our approach systematically creates datasets with varying intensities of distribution shifts, facilitating a comprehensive an
Externí odkaz:
http://arxiv.org/abs/2409.07940