Zobrazeno 1 - 10
of 4 050
pro vyhledávání: '"Chandan, K."'
Autor:
Guo, Taicheng, Liu, Chaochun, Wang, Hai, Mannam, Varun, Wang, Fang, Chen, Xin, Zhang, Xiangliang, Reddy, Chandan K.
Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the relationships between users and items, leading to inaccurate u
Externí odkaz:
http://arxiv.org/abs/2410.19627
Large language models (LLMs) often show unwarranted preference for certain choice options when responding to multiple-choice questions, posing significant reliability concerns in LLM-automated systems. To mitigate this selection bias problem, previou
Externí odkaz:
http://arxiv.org/abs/2409.18857
Autor:
Dementyev, Artem, Reddy, Chandan K. A., Wisdom, Scott, Chatlani, Navin, Hershey, John R., Lyon, Richard F.
Low latency models are critical for real-time speech enhancement applications, such as hearing aids and hearables. However, the sub-millisecond latency space for resource-constrained hearables remains underexplored. We demonstrate speech enhancement
Externí odkaz:
http://arxiv.org/abs/2409.18239
We experimentally explore the morphological evolution of cages in quasi-two-dimensional suspensions of colloidal fluids, uncovering a complex dynamic restructuring in the fluid. Although cages display isotropic evolution in the laboratory frame, we o
Externí odkaz:
http://arxiv.org/abs/2407.18032
Tabular reasoning involves interpreting natural language queries about tabular data, which presents a unique challenge of combining language understanding with structured data analysis. Existing methods employ either textual reasoning, which excels i
Externí odkaz:
http://arxiv.org/abs/2407.05952
Autor:
Khatir, Mehrdad, Reddy, Chandan K.
This paper explores the concept formation and alignment within the realm of language models (LMs). We propose a mechanism for identifying concepts and their hierarchical organization within the semantic representations learned by various LMs, encompa
Externí odkaz:
http://arxiv.org/abs/2406.05315
In this study, we tackle the challenge of inadequate and costly training data that has hindered the development of conversational question answering (ConvQA) systems. Enterprises have a large corpus of diverse internal documents. Instead of relying o
Externí odkaz:
http://arxiv.org/abs/2406.03703
Feature transformation aims to reconstruct the feature space of raw features to enhance the performance of downstream models. However, the exponential growth in the combinations of features and operations poses a challenge, making it difficult for ex
Externí odkaz:
http://arxiv.org/abs/2405.16203
Autor:
Tipirneni, Sindhu, Adkathimar, Ravinarayana, Choudhary, Nurendra, Hiranandani, Gaurush, Amjad, Rana Ali, Ioannidis, Vassilis N., Yuan, Changhe, Reddy, Chandan K.
Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed that powerful closed-source LLMs provide good quality clusterings of ent
Externí odkaz:
http://arxiv.org/abs/2405.00988
Autor:
Barbhuiya, Noman Hanif, Mohanty, Pritam K., Mondal, Saikat, Hussian, Aminul, Agarwala, Adhip, Mishra, Chandan K.
Vapor deposition, known for precise structural control, is inevitably influenced by impurities. These impurities, often distinct from the depositing material, can significantly impact material properties, including local structure. Interestingly, the
Externí odkaz:
http://arxiv.org/abs/2404.19425