Zobrazeno 1 - 10
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pro vyhledávání: '"Watson, William P"'
Autor:
Watson, William, Cho, Nicole, Srishankar, Nishan, Zeng, Zhen, Cecchi, Lucas, Scott, Daniel, Siddagangappa, Suchetha, Kaur, Rachneet, Balch, Tucker, Veloso, Manuela
Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is challenging for an off-the-shelf Large Language Model (LLM) to ingest the
Externí odkaz:
http://arxiv.org/abs/2412.11063
Financial intelligence generation from vast data sources has typically relied on traditional methods of knowledge-graph construction or database engineering. Recently, fine-tuned financial domain-specific Large Language Models (LLMs), have emerged. W
Externí odkaz:
http://arxiv.org/abs/2410.19727
Publikováno v:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (2023) 7144-7159
A myriad of different Large Language Models (LLMs) face a common challenge in contextually analyzing table question-answering tasks. These challenges are engendered from (1) finite context windows for large tables, (2) multi-faceted discrepancies amo
Externí odkaz:
http://arxiv.org/abs/2406.10803
Hallucination continues to be one of the most critical challenges in the institutional adoption journey of Large Language Models (LLMs). While prior studies have primarily focused on the post-generation analysis and refinement of outputs, this paper
Externí odkaz:
http://arxiv.org/abs/2404.12535
Autor:
Zmigrod, Ran, Wang, Dongsheng, Sibue, Mathieu, Pei, Yulong, Babkin, Petr, Brugere, Ivan, Liu, Xiaomo, Navarro, Nacho, Papadimitriou, Antony, Watson, William, Ma, Zhiqiang, Nourbakhsh, Armineh, Shah, Sameena
The field of visually rich document understanding (VRDU) aims to solve a multitude of well-researched NLP tasks in a multi-modal domain. Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity ex
Externí odkaz:
http://arxiv.org/abs/2404.04003
Autor:
Watson, William, Liu, Bo
Table extraction has long been a pervasive problem in financial services. This is more challenging in the image domain, where content is locked behind cumbersome pixel format. Luckily, advances in deep learning for image segmentation, OCR, and sequen
Externí odkaz:
http://arxiv.org/abs/2405.05260
Autor:
Watson, William, Yong, Lawrence
We explore link prediction as a proxy for automatically surfacing documents from existing literature that might be topically or contextually relevant to a new document. Our model uses transformer-based graph embeddings to encode the meaning of each d
Externí odkaz:
http://arxiv.org/abs/2403.18855
Autor:
Zeng, Zhen, Watson, William, Cho, Nicole, Rahimi, Saba, Reynolds, Shayleen, Balch, Tucker, Veloso, Manuela
The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requiring spontaneous or unpredictable tasks demanded by users. This paper i
Externí odkaz:
http://arxiv.org/abs/2404.13050
Autor:
Beck, Zachariah P., Alpert, Brandon, Bowman, Alexander J., Watson, William R., Tepole, Adrian Buganza
Video games have emerged as a medium for learning by creating engaging environments, encouraging creative and deep thinking, and exposing learners to complex problems. Unfortunately, even though there are increasing examples of video games for many b
Externí odkaz:
http://arxiv.org/abs/2212.08124
We investigate the spatial distribution of Lyman-$\alpha$ (Ly $\alpha$) absorbers within cosmic voids. We create a catalogue of cosmic voids in Sloan Digital Sky Survey Data Release 7 (SDSS DR7) with the VoidFinder algorithm of the Void Analysis Soft
Externí odkaz:
http://arxiv.org/abs/2204.06708