Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Reddy, Varshini"'
Autor:
Lovering, Charles, Krumdick, Michael, Lai, Viet Dac, Kumar, Nilesh, Reddy, Varshini, Koncel-Kedziorski, Rik, Tanner, Chris
Some information is factual (e.g., "Paris is in France"), whereas other information is probabilistic (e.g., "the coin flip will be a [Heads/Tails]."). We believe that good Language Models (LMs) should understand and reflect this nuance. Our work inve
Externí odkaz:
http://arxiv.org/abs/2410.16007
FActScore has gained popularity as a metric to estimate the factuality of long-form texts generated by Large Language Models (LLMs) in English. However, there has not been any work in studying the behavior of FActScore in other languages. This paper
Externí odkaz:
http://arxiv.org/abs/2406.19415
Autor:
Lai, Viet Dac, Krumdick, Michael, Lovering, Charles, Reddy, Varshini, Schmidt, Craig, Tanner, Chris
The financial domain frequently deals with large numbers of long documents that are essential for daily operations. Significant effort is put towards automating financial data analysis. However, a persistent challenge, not limited to the finance doma
Externí odkaz:
http://arxiv.org/abs/2406.14394
Autor:
Schmidt, Craig W., Reddy, Varshini, Zhang, Haoran, Alameddine, Alec, Uzan, Omri, Pinter, Yuval, Tanner, Chris
Tokenization is a foundational step in natural language processing (NLP) tasks, bridging raw text and language models. Existing tokenization approaches like Byte-Pair Encoding (BPE) originate from the field of data compression, and it has been sugges
Externí odkaz:
http://arxiv.org/abs/2402.18376
Autor:
Reddy, Varshini, Koncel-Kedziorski, Rik, Lai, Viet Dac, Krumdick, Michael, Lovering, Charles, Tanner, Chris
For large language models (LLMs) to be effective in the financial domain -- where each decision can have a significant impact -- it is necessary to investigate realistic tasks and data. Financial professionals often interact with documents that are h
Externí odkaz:
http://arxiv.org/abs/2401.06915
Autor:
Koncel-Kedziorski, Rik, Krumdick, Michael, Lai, Viet, Reddy, Varshini, Lovering, Charles, Tanner, Chris
Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. Together, these requirements make this domain difficult for large language models (LLMs). We introduce BizBench, a benchmark for
Externí odkaz:
http://arxiv.org/abs/2311.06602
Autor:
Le, Van Anh, Reddy, Varshini, Chen, Zixi, Li, Mengyuan, Tang, Xinran, Ortiz, Anthony, Nsutezo, Simone Fobi, Robinson, Caleb
In this paper we propose a mask-conditional synthetic image generation model for creating synthetic satellite imagery datasets. Given a dataset of real high-resolution images and accompanying land cover masks, we show that it is possible to train an
Externí odkaz:
http://arxiv.org/abs/2302.04305
Autor:
Penrod, Mark, Termotto, Harrison, Reddy, Varshini, Yao, Jiayu, Doshi-Velez, Finale, Pan, Weiwei
Publikováno v:
International Conference on Machine Learning. PMLR 162 (2022)
For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictive uncertainty is useful for these tasks, it is not evident from litera
Externí odkaz:
http://arxiv.org/abs/2208.01705
Publikováno v:
Journal of Computer Virology and Hacking Techniques; December 2021, Vol. 17 Issue: 4 p333-346, 14p
