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pro vyhledávání: '"Manning, A. D."'
Models that rely on subword tokenization have significant drawbacks, such as sensitivity to character-level noise like spelling errors and inconsistent compression rates across different languages and scripts. While character- or byte-level models li
Externí odkaz:
http://arxiv.org/abs/2410.20771
Autor:
Chai, Wenhao, Song, Enxin, Du, Yilun, Meng, Chenlin, Madhavan, Vashisht, Bar-Tal, Omer, Hwang, Jeng-Neng, Xie, Saining, Manning, Christopher D.
Video detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation. In this paper, we propose AuroraCap, a video captioner based on a lar
Externí odkaz:
http://arxiv.org/abs/2410.03051
We introduce NNetscape Navigator (NNetnav), a method for training web agents entirely through synthetic demonstrations. These demonstrations are collected by first interacting with a browser to generate trajectory rollouts, which are then retroactive
Externí odkaz:
http://arxiv.org/abs/2410.02907
Instruction tuning commonly means finetuning a language model on instruction-response pairs. We discover two forms of adaptation (tuning) that are deficient compared to instruction tuning, yet still yield instruction following; we call this implicit
Externí odkaz:
http://arxiv.org/abs/2409.14254
The Linear Representation Hypothesis (LRH) states that neural networks learn to encode concepts as directions in activation space, and a strong version of the LRH states that models learn only such encodings. In this paper, we present a counterexampl
Externí odkaz:
http://arxiv.org/abs/2408.10920
Autor:
Doumbouya, Moussa Koulako Bala, Nandi, Ananjan, Poesia, Gabriel, Ghilardi, Davide, Goldie, Anna, Bianchi, Federico, Jurafsky, Dan, Manning, Christopher D.
The safety of Large Language Models (LLMs) remains a critical concern due to a lack of adequate benchmarks for systematically evaluating their ability to resist generating harmful content. Previous efforts towards automated red teaming involve static
Externí odkaz:
http://arxiv.org/abs/2408.04811
Teaching Computer Science (CS) by having students write programs by hand on paper has key pedagogical advantages: It allows focused learning and requires careful thinking compared to the use of Integrated Development Environments (IDEs) with intellig
Externí odkaz:
http://arxiv.org/abs/2408.07220
Static word embeddings are ubiquitous in computational social science applications and contribute to practical decision-making in a variety of fields including law and healthcare. However, assessing the statistical uncertainty in downstream conclusio
Externí odkaz:
http://arxiv.org/abs/2406.12165
Autor:
Magesh, Varun, Surani, Faiz, Dahl, Matthew, Suzgun, Mirac, Manning, Christopher D., Ho, Daniel E.
Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. But the large la
Externí odkaz:
http://arxiv.org/abs/2405.20362
Autor:
Csordás, Róbert, Irie, Kazuki, Schmidhuber, Jürgen, Potts, Christopher, Manning, Christopher D.
Previous work on Universal Transformers (UTs) has demonstrated the importance of parameter sharing across layers. By allowing recurrence in depth, UTs have advantages over standard Transformers in learning compositional generalizations, but layer-sha
Externí odkaz:
http://arxiv.org/abs/2405.16039