Zobrazeno 1 - 10
of 7 229
pro vyhledávání: '"COLLINS, MICHAEL A."'
Autor:
Llama, Joe, Zhao, Lily L., Brewer, John M., Szymkowiak, Andrew, Fischer, Debra A., Collins, Michael, Tiegs, Jake, Cornelius, Frank
The signal induced by a temperate, terrestrial planet orbiting a Sun-like star is an order of magnitude smaller than the host stars' intrinsic variability. Understanding stellar activity is, therefore, a fundamental obstacle in confirming the smalles
Externí odkaz:
http://arxiv.org/abs/2407.07967
Autor:
Bohnet, Bernd, Swersky, Kevin, Liu, Rosanne, Awasthi, Pranjal, Nova, Azade, Snaider, Javier, Sedghi, Hanie, Parisi, Aaron T, Collins, Michael, Lazaridou, Angeliki, Firat, Orhan, Fiedel, Noah
We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Previous efforts to construct such datasets relied on crowd-sourcing, but the emergence of transformers with a
Externí odkaz:
http://arxiv.org/abs/2406.00179
Autor:
Collins, Michael, Deshmukh, Jyotirmoy V., Dinesh, Dristi, Raghothaman, Mukund, Ravi, Srivatsan, Xia, Yuan
Network security analysts gather data from diverse sources, from high-level summaries of network flow and traffic volumes to low-level details such as service logs from servers and the contents of individual packets. They validate and check this data
Externí odkaz:
http://arxiv.org/abs/2403.01314
Autor:
Jacovi, Alon, Bitton, Yonatan, Bohnet, Bernd, Herzig, Jonathan, Honovich, Or, Tseng, Michael, Collins, Michael, Aharoni, Roee, Geva, Mor
Prompting language models to provide step-by-step answers (e.g., "Chain-of-Thought") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusse
Externí odkaz:
http://arxiv.org/abs/2402.00559
Autor:
Mudgal, Sidharth, Lee, Jong, Ganapathy, Harish, Li, YaGuang, Wang, Tao, Huang, Yanping, Chen, Zhifeng, Cheng, Heng-Tze, Collins, Michael, Strohman, Trevor, Chen, Jilin, Beutel, Alex, Beirami, Ahmad
KL-regularized reinforcement learning (RL) is a popular alignment framework to control the language model responses towards high reward outcomes. We pose a tokenwise RL objective and propose a modular solver for it, called controlled decoding (CD). C
Externí odkaz:
http://arxiv.org/abs/2310.17022
We study the problem of classification with a reject option for a fixed predictor, applicable in natural language processing. We introduce a new problem formulation for this scenario, and an algorithm minimizing a new surrogate loss function. We prov
Externí odkaz:
http://arxiv.org/abs/2301.09044
Autor:
Bohnet, Bernd, Tran, Vinh Q., Verga, Pat, Aharoni, Roee, Andor, Daniel, Soares, Livio Baldini, Ciaramita, Massimiliano, Eisenstein, Jacob, Ganchev, Kuzman, Herzig, Jonathan, Hui, Kai, Kwiatkowski, Tom, Ma, Ji, Ni, Jianmo, Saralegui, Lierni Sestorain, Schuster, Tal, Cohen, William W., Collins, Michael, Das, Dipanjan, Metzler, Donald, Petrov, Slav, Webster, Kellie
Large language models (LLMs) have shown impressive results while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribu
Externí odkaz:
http://arxiv.org/abs/2212.08037
Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and
Externí odkaz:
http://arxiv.org/abs/2211.12142
The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message formalized as a set of entity-relationship triples. It embeds the LM in a
Externí odkaz:
http://arxiv.org/abs/2211.09070
Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. While the former can be validated using well-known evaluation metrics, the latter is difficul
Externí odkaz:
http://arxiv.org/abs/2210.17525