Zobrazeno 1 - 10
of 28 972
pro vyhledávání: '"A A, Kazi"'
Quantum networking continues to encode information in polarization states due to ease and precision. The variable environmental polarization transformations induced by deployed fiber need correction for deployed quantum networking. Here we present a
Externí odkaz:
http://arxiv.org/abs/2411.15135
Medium and high-entropy alloys (M/HEAs) have garnered significant attention as potential nuclear structural materials due to their excellent stability at high temperatures and resistance to radiation. However, the common use of Co in M/HEAs, which ex
Externí odkaz:
http://arxiv.org/abs/2411.13665
The rapid spread of misinformation, particularly through online platforms, underscores the urgent need for reliable detection systems. This study explores the utilization of machine learning and natural language processing, specifically Support Vecto
Externí odkaz:
http://arxiv.org/abs/2411.12703
Monitoring agricultural activities is important to ensure food security. Remote sensing plays a significant role for large-scale continuous monitoring of cultivation activities. Time series remote sensing data were used for the generation of the crop
Externí odkaz:
http://arxiv.org/abs/2411.12667
The population of the urban areas is increasing daily, and this migration is causing serious environmental pollution. A larger population is creating pressure on the municipality's waste management and the city corporations of developing countries su
Externí odkaz:
http://arxiv.org/abs/2411.09710
Autor:
Fuad, Kazi Ahmed Asif, Chen, Lizhong
Large Language Models (LLMs) excel in data synthesis but can be inaccurate in domain-specific tasks, which retrieval-augmented generation (RAG) systems address by leveraging user-provided data. However, RAGs require optimization in both retrieval and
Externí odkaz:
http://arxiv.org/abs/2411.00294
Bayesian Kernel Machine Regression (BKMR) has emerged as a powerful tool to detect negative health effects from exposure to complex multi-pollutant mixtures. However, its performance is degraded when data deviate from normality. In this comprehensive
Externí odkaz:
http://arxiv.org/abs/2411.00286
This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT based model for natural language processing applications. After the model creation, we applied the resulting model, LastBERT, to
Externí odkaz:
http://arxiv.org/abs/2411.00052
Traditional Retrieval-Augmented Generation (RAG) benchmarks rely on different heuristic-based metrics for evaluation, but these require human preferences as ground truth for reference. In contrast, arena-based benchmarks, where two models compete eac
Externí odkaz:
http://arxiv.org/abs/2410.13716
Autor:
Bao, Forrest Sheng, Li, Miaoran, Qu, Renyi, Luo, Ge, Wan, Erana, Tang, Yujia, Fan, Weisi, Tamber, Manveer Singh, Kazi, Suleman, Sourabh, Vivek, Qi, Mike, Tu, Ruixuan, Xu, Chenyu, Gonzales, Matthew, Mendelevitch, Ofer, Ahmad, Amin
Summarization is one of the most common tasks performed by large language models (LLMs), especially in applications like Retrieval-Augmented Generation (RAG). However, existing evaluations of hallucinations in LLM-generated summaries, and evaluations
Externí odkaz:
http://arxiv.org/abs/2410.13210