Zobrazeno 1 - 10
of 11 735
pro vyhledávání: '"Iso, A."'
Autor:
Belem, Catarina G., Pezeskhpour, Pouya, Iso, Hayate, Maekawa, Seiji, Bhutani, Nikita, Hruschka, Estevam
Although many studies have investigated and reduced hallucinations in large language models (LLMs) for single-document tasks, research on hallucination in multi-document summarization (MDS) tasks remains largely unexplored. Specifically, it is unclea
Externí odkaz:
http://arxiv.org/abs/2410.13961
The rapid increase in textual information means we need more efficient methods to sift through, organize, and understand it all. While retrieval-augmented generation (RAG) models excel in accessing information from large document collections, they st
Externí odkaz:
http://arxiv.org/abs/2410.11996
Publikováno v:
Soft Matter, 2024,20, 6848-6856(Open Access)
Given the wide range of length scales, the analysis of polymer systems often requires coarse-graining, for which various levels of description may be possible depending on the phenomenon under consideration. Here, we provide a super-coarse grained de
Externí odkaz:
http://arxiv.org/abs/2409.02461
Distinguishing photon-arrival time and position is crucial for advancing quantum technology. However, capturing spatial and temporal information efficiently remains challenging. Here, we present a novel photon-detection technique to achieve a signifi
Externí odkaz:
http://arxiv.org/abs/2407.16149
We introduce a novel Image Quality Assessment (IQA) dataset comprising 6073 UHD-1 (4K) images, annotated at a fixed width of 3840 pixels. Contrary to existing No-Reference (NR) IQA datasets, ours focuses on highly aesthetic photos of high technical q
Externí odkaz:
http://arxiv.org/abs/2406.17472
Autor:
Kandogan, Eser, Rahman, Sajjadur, Bhutani, Nikita, Zhang, Dan, Chen, Rafael Li, Mitra, Kushan, Gurajada, Sairam, Pezeshkpour, Pouya, Iso, Hayate, Feng, Yanlin, Kim, Hannah, Shen, Chen, Wang, Jin, Hruschka, Estevam
Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases. Towards this goal, there is a notable shift to building compound AI systems, wherein L
Externí odkaz:
http://arxiv.org/abs/2406.00584
We propose an experiment for constructing a spatial cat state of a suspended mirror with an order of $\mathcal{O}$(mg). The mirror is set at the center of two mirrors, creating two optical cavities and optical springs. The induced potential exhibits
Externí odkaz:
http://arxiv.org/abs/2404.08435
Autor:
Shirakawa, Toru, Li, Yi, Wu, Yulun, Qiu, Sky, Li, Yuxuan, Zhao, Mingduo, Iso, Hiroyasu, van der Laan, Mark
We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings. Our approach utilizes a transformer a
Externí odkaz:
http://arxiv.org/abs/2404.04399
Autor:
Niwa, Ayana, Iso, Hayate
We introduce AmbigNLG, a novel task designed to tackle the challenge of task ambiguity in instructions for Natural Language Generation (NLG). Ambiguous instructions often impede the performance of Large Language Models (LLMs), especially in complex N
Externí odkaz:
http://arxiv.org/abs/2402.17717
While large language models (LMs) demonstrate remarkable performance, they encounter challenges in providing accurate responses when queried for information beyond their pre-trained memorization. Although augmenting them with relevant external inform
Externí odkaz:
http://arxiv.org/abs/2402.13492