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Autor:
V. N. Zavialov, Lianmei Li
Publikováno v:
Научный диалог, Vol 11, Iss 4, Pp 29-45 (2022)
The object of the article is the conjunction khot’… khot’..., and the subject is its morphological and syntactic properties. The relevance of the work lies in its direct focus on solving problems related to the description of the service fund o
Externí odkaz:
https://doaj.org/article/cb72355c1d864c2b954b51f42bcd6cc8
Autor:
Bogin, Ben, Yang, Kejuan, Gupta, Shashank, Richardson, Kyle, Bransom, Erin, Clark, Peter, Sabharwal, Ashish, Khot, Tushar
Given that Large Language Models (LLMs) have made significant progress in writing code, can they now be used to autonomously reproduce results from research repositories? Such a capability would be a boon to the research community, helping researcher
Externí odkaz:
http://arxiv.org/abs/2409.07440
In a $3$-$\mathsf{XOR}$ game $\mathcal{G}$, the verifier samples a challenge $(x,y,z)\sim \mu$ where $\mu$ is a probability distribution over $\Sigma\times\Gamma\times\Phi$, and a map $t\colon \Sigma\times\Gamma\times\Phi\to\mathcal{A}$ for a finite
Externí odkaz:
http://arxiv.org/abs/2408.09352
Autor:
Varma, Girish
In a recent result, Khot and Saket [FOCS 2014] proved the quasi-NP-hardness of coloring a 2-colorable 12-uniform hypergraph with $2^{(\log n)^{\Omega(1)}}$ colors. This result was proved using a novel outer PCP verifier which had a strong soundness g
Externí odkaz:
http://arxiv.org/abs/1408.0262
We propose a framework of algorithm vs. hardness for all Max-CSPs and demonstrate it for a large class of predicates. This framework extends the work of Raghavendra [STOC, 2008], who showed a similar result for almost satisfiable Max-CSPs. Our framew
Externí odkaz:
http://arxiv.org/abs/2408.15377
Autor:
Qian, Kun, Sang, Yisi, Bayat, Farima Fatahi, Belyi, Anton, Chu, Xianqi, Govind, Yash, Khorshidi, Samira, Khot, Rahul, Luna, Katherine, Nikfarjam, Azadeh, Qi, Xiaoguang, Wu, Fei, Zhang, Xianhan, Li, Yunyao
Prompt engineering is an iterative procedure often requiring extensive manual effort to formulate suitable instructions for effectively directing large language models (LLMs) in specific tasks. Incorporating few-shot examples is a vital and effective
Externí odkaz:
http://arxiv.org/abs/2408.04637
Autor:
Trivedi, Harsh, Khot, Tushar, Hartmann, Mareike, Manku, Ruskin, Dong, Vinty, Li, Edward, Gupta, Shashank, Sabharwal, Ashish, Balasubramanian, Niranjan
Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via APIs, but also generate rich code with complex control flow in an itera
Externí odkaz:
http://arxiv.org/abs/2407.18901
Autor:
Majumder, Bodhisattwa Prasad, Surana, Harshit, Agarwal, Dhruv, Mishra, Bhavana Dalvi, Meena, Abhijeetsingh, Prakhar, Aryan, Vora, Tirth, Khot, Tushar, Sabharwal, Ashish, Clark, Peter
Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely from a set of provided datasets? To evaluate this question, we present Di
Externí odkaz:
http://arxiv.org/abs/2407.01725
Autor:
Jansen, Peter, Côté, Marc-Alexandre, Khot, Tushar, Bransom, Erin, Mishra, Bhavana Dalvi, Majumder, Bodhisattwa Prasad, Tafjord, Oyvind, Clark, Peter
Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reasoning is challenging as running real-world experiments is often prohibit
Externí odkaz:
http://arxiv.org/abs/2406.06769