Zobrazeno 1 - 10
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pro vyhledávání: '"A. R. Thomas"'
What factors contribute to the relative success and corresponding difficulties of in-context learning for Large Language Models (LLMs)? Drawing on insights from the literature on human concept learning, we test LLMs on carefully designed concept lear
Externí odkaz:
http://arxiv.org/abs/2412.02823
Autor:
Bennecke, Wiebke, Oliva, Ignacio Gonzalez, Bange, Jan Philipp, Werner, Paul, Schmitt, David, Merboldt, Marco, Seiler, Anna M., Watanabe, Kenji, Taniguchi, Takashi, Steil, Daniel, Weitz, R. Thomas, Puschnig, Peter, Draxl, Claudia, Jansen, G. S. Matthijs, Reutzel, Marcel, Mathias, Stefan
Two-dimensional transition metal dichalcogenides (TMDs) and organic semiconductors (OSCs) have emerged as promising material platforms for next-generation optoelectronic devices. The combination of both is predicted to yield emergent properties while
Externí odkaz:
http://arxiv.org/abs/2411.14993
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:
Seiler, Anna M., Statz, Martin, Eckel, Christian, Weimer, Isabell, Pöhls, Jonas, Watanabe, Kenji, Taniguchi, Takashi, Zhang, Fan, Weitz, R. Thomas
AB-stacked bilayer graphene has emerged as a fascinating yet simple platform for exploring macroscopic quantum phenomena of correlated electrons. Unexpectedly, a phase with negative dR/dT has recently been observed when a large electric displacement
Externí odkaz:
http://arxiv.org/abs/2408.16628
Autor:
Falorsi, Francesca, Zhao, Shuangjie, Liu, Kejun, Eckel, Christian, Pöhls, Jonas F., Bennecke, Wiebke, Reutzel, Marcel, Mathias, Stefan, Watanabe, Kenji, Taniguchi, Takashi, Wang, Zhiyong, Polozij, Miroslav, Feng, Xinliang, Heine, Thomas, Weitz, R. Thomas
The vertical integration of multiple two-dimensional (2D) materials in heterostructures, held together by van der Waals forces, has opened unprecedented possibilities for modifying the (opto-)electronic properties of nanodevices. Graphene, with its r
Externí odkaz:
http://arxiv.org/abs/2407.11559
Chain-of-Thought (CoT) prompting has been shown to enhance the multi-step reasoning capabilities of Large Language Models (LLMs). However, debates persist about whether LLMs exhibit abstract generalization or rely on shallow heuristics when given CoT
Externí odkaz:
http://arxiv.org/abs/2407.01687
Large language models (LLMs) have shown the emergent capability of in-context learning (ICL). One line of research has explained ICL as functionally performing gradient descent. In this paper, we introduce a new way of diagnosing whether ICL is funct
Externí odkaz:
http://arxiv.org/abs/2406.18501
Autor:
Chi, Nathan A., Malchev, Teodor, Kong, Riley, Chi, Ryan A., Huang, Lucas, Chi, Ethan A., McCoy, R. Thomas, Radev, Dragomir
We introduce modeLing, a novel benchmark of Linguistics Olympiad-style puzzles which tests few-shot reasoning in AI systems. Solving these puzzles necessitates inferring aspects of a language's grammatical structure from a small number of examples. S
Externí odkaz:
http://arxiv.org/abs/2406.17038
Autor:
Seiler, Anna M., Zhumagulov, Yaroslav, Zollner, Klaus, Yoon, Chiho, Urbaniak, David, Geisenhof, Fabian R., Watanabe, Kenji, Taniguchi, Takashi, Fabian, Jaroslav, Zhang, Fan, Weitz, R. Thomas
Spin-orbit coupling (SOC) and electron-electron interaction can mutually influence each other and give rise to a plethora of intriguing phenomena in condensed matter systems. In pristine bilayer graphene, which has weak SOC, intrinsic Lifshitz transi
Externí odkaz:
http://arxiv.org/abs/2403.17140
Humans can learn new concepts from a small number of examples by drawing on their inductive biases. These inductive biases have previously been captured by using Bayesian models defined over symbolic hypothesis spaces. Is it possible to create a neur
Externí odkaz:
http://arxiv.org/abs/2402.07035