Zobrazeno 1 - 10
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pro vyhledávání: '"Le, An V."'
Despite their success in many domains, large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. This is crucial as many real-world applications, ranging from personalized recommendations to h
Externí odkaz:
http://arxiv.org/abs/2410.06238
Autor:
Brown, Bradley, Juravsky, Jordan, Ehrlich, Ryan, Clark, Ronald, Le, Quoc V., Ré, Christopher, Mirhoseini, Azalia
Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit the amount of compute to only one attempt per problem. Here, we explore inference compute as
Externí odkaz:
http://arxiv.org/abs/2407.21787
Autor:
Zheng, Huaixiu Steven, Mishra, Swaroop, Zhang, Hugh, Chen, Xinyun, Chen, Minmin, Nova, Azade, Hou, Le, Cheng, Heng-Tze, Le, Quoc V., Chi, Ed H., Zhou, Denny
We introduce NATURAL PLAN, a realistic planning benchmark in natural language containing 3 key tasks: Trip Planning, Meeting Planning, and Calendar Scheduling. We focus our evaluation on the planning capabilities of LLMs with full information on the
Externí odkaz:
http://arxiv.org/abs/2406.04520
Autor:
Wei, Jerry, Yang, Chengrun, Song, Xinying, Lu, Yifeng, Hu, Nathan, Huang, Jie, Tran, Dustin, Peng, Daiyi, Liu, Ruibo, Huang, Da, Du, Cosmo, Le, Quoc V.
Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics. To benchmark a model's long-form factuality in open domains, we first use GPT-4 to generate LongFact, a pro
Externí odkaz:
http://arxiv.org/abs/2403.18802
Person attribute recognition and attribute-based retrieval are two core human-centric tasks. In the recognition task, the challenge is specifying attributes depending on a person's appearance, while the retrieval task involves searching for matching
Externí odkaz:
http://arxiv.org/abs/2403.06119
Autor:
Du, Yuchen, Le, Tho V.
This article presents a comprehensive sentiment analysis (SA) of comments on YouTube videos related to Sidewalk Delivery Robots (SDRs). We manually annotated the collected YouTube comments with three sentiment labels: negative (0), positive (1), and
Externí odkaz:
http://arxiv.org/abs/2405.00688
Autor:
Zhou, Pei, Pujara, Jay, Ren, Xiang, Chen, Xinyun, Cheng, Heng-Tze, Le, Quoc V., Chi, Ed H., Zhou, Denny, Mishra, Swaroop, Zheng, Huaixiu Steven
We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods. Core to the framework is a self-discovery proce
Externí odkaz:
http://arxiv.org/abs/2402.03620
Autor:
Real, Esteban, Chen, Yao, Rossini, Mirko, de Souza, Connal, Garg, Manav, Verghese, Akhil, Firsching, Moritz, Le, Quoc V., Cubuk, Ekin Dogus, Park, David H.
Computers calculate transcendental functions by approximating them through the composition of a few limited-precision instructions. For example, an exponential can be calculated with a Taylor series. These approximation methods were developed over th
Externí odkaz:
http://arxiv.org/abs/2312.08472
With the rise in demand for local deliveries and e-commerce, robotic deliveries are being considered as efficient and sustainable solutions. However, the deployment of such systems can be highly complex due to numerous factors involving stochastic de
Externí odkaz:
http://arxiv.org/abs/2310.17475
Autor:
Zheng, Huaixiu Steven, Mishra, Swaroop, Chen, Xinyun, Cheng, Heng-Tze, Chi, Ed H., Le, Quoc V, Zhou, Denny
We present Step-Back Prompting, a simple prompting technique that enables LLMs to do abstractions to derive high-level concepts and first principles from instances containing specific details. Using the concepts and principles to guide reasoning, LLM
Externí odkaz:
http://arxiv.org/abs/2310.06117