Zobrazeno 1 - 10
of 2 705
pro vyhledávání: '"Ghaddar, A."'
Autor:
Zhou, Jiaming, Ghaddar, Abbas, Zhang, Ge, Ma, Liheng, Hu, Yaochen, Pal, Soumyasundar, Coates, Mark, Wang, Bin, Zhang, Yingxue, Hao, Jianye
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains. In this work, we explore the potential an
Externí odkaz:
http://arxiv.org/abs/2409.12437
Autor:
Ghaddar, Nadim, Wang, Lele
The problem of coding for the uplink and downlink of cloud radio access networks (C-RAN's) with $K$ users and $L$ relays is considered. It is shown that low-complexity coding schemes that achieve any point in the rate-fronthaul region of joint coding
Externí odkaz:
http://arxiv.org/abs/2408.14565
Autor:
Dehghan, Mohammad, Alomrani, Mohammad Ali, Bagga, Sunyam, Alfonso-Hermelo, David, Bibi, Khalil, Ghaddar, Abbas, Zhang, Yingxue, Li, Xiaoguang, Hao, Jianye, Liu, Qun, Lin, Jimmy, Chen, Boxing, Parthasarathi, Prasanna, Biparva, Mahdi, Rezagholizadeh, Mehdi
The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems is vital from both accuracy (completeness of information) and efficienc
Externí odkaz:
http://arxiv.org/abs/2406.10393
Autor:
Mo, Fengran, Ghaddar, Abbas, Mao, Kelong, Rezagholizadeh, Mehdi, Chen, Boxing, Liu, Qun, Nie, Jian-Yun
In this paper, we study how open-source large language models (LLMs) can be effectively deployed for improving query rewriting in conversational search, especially for ambiguous queries. We introduce CHIQ, a two-step method that leverages the capabil
Externí odkaz:
http://arxiv.org/abs/2406.05013
Autor:
González-Rodríguez, Brais, Gómez-Casares, Ignacio, Ghaddar, Bissan, González-Díaz, Julio, Pateiro-López, Beatriz
Over the last few years, there has been a surge in the use of learning techniques to improve the performance of optimization algorithms. In particular, the learning of branching rules in mixed integer linear programming has received a lot of attentio
Externí odkaz:
http://arxiv.org/abs/2406.03626
Autor:
Huang, Chenyang, Ghaddar, Abbas, Kobyzev, Ivan, Rezagholizadeh, Mehdi, Zaiane, Osmar R., Chen, Boxing
Recently, there has been considerable attention on detecting hallucinations and omissions in Machine Translation (MT) systems. The two dominant approaches to tackle this task involve analyzing the MT system's internal states or relying on the output
Externí odkaz:
http://arxiv.org/abs/2406.01919
Autor:
Ghaddar, Abbas, Alfonso-Hermelo, David, Langlais, Philippe, Rezagholizadeh, Mehdi, Chen, Boxing, Parthasarathi, Prasanna
In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial data contains annotation artifacts, which may bias models towards com
Externí odkaz:
http://arxiv.org/abs/2405.15110
Let $\mathsf{TH}_k$ denote the $k$-out-of-$n$ threshold function: given $n$ input Boolean variables, the output is $1$ if and only if at least $k$ of the inputs are $1$. We consider the problem of computing the $\mathsf{TH}_k$ function using noisy re
Externí odkaz:
http://arxiv.org/abs/2403.07227
In this paper, we develop novel mathematical models to optimize utilization of community energy storage (CES) by clustering prosumers and consumers into energy sharing communities/microgrids in the context of a smart city. Three different microgrid c
Externí odkaz:
http://arxiv.org/abs/2401.14499
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to en
Externí odkaz:
http://arxiv.org/abs/2401.03244