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Search Publications by: Kay Peterson (Fed)

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Displaying 1 - 11 of 11

2024 NIST GenAI (Pilot Study): Text-to-Text Evaluation Overview and Results

June 25, 2025
Author(s)
Hariharan Iyer, Seungmin Seo, Lukas Diduch, Kay Peterson, George Awad, Yooyoung Lee
The 2024 NIST Generative AI (GenAI) Pilot Study focuses on evaluating text-to-text (T2T) generation and discrimination tasks to assess the capabilities and limitations of generative AI models and AI detectors. The study aims to measure the effectiveness of

2024 NIST Generative AI (GenAI): Data Creation Specification for Text-to-Text (T2T) Generators

April 1, 2024
Author(s)
Yooyoung Lee, George Awad, Asad Butt, Lukas Diduch, Kay Peterson, Seungmin Seo, Ian Soboroff, Hariharan Iyer
Generator (G) teams will be tested on their system ability to generate content that is indistinguishable from human-generated content. For the pilot study, the evaluation will help determine strengths and weaknesses in their approaches including insights

2024 NIST Generative AI (GenAI): Evaluation Plan for Text-to-Text (T2T) Discriminators

April 1, 2024
Author(s)
Yooyoung Lee, George Awad, Asad Butt, Lukas Diduch, Kay Peterson, Seungmin Seo, Ian Soboroff, Hariharan Iyer
Generator (G) teams will be tested on their system's ability to generate content that is indistinguishable from human-generated content. For the pilot study, the evaluation will help determine strengths and weaknesses in their approaches including insights

Overview of the NIST 2016 LoReHLT Evaluation

November 13, 2017
Author(s)
Audrey N. Tong, Lukasz L. Diduch, Jonathan G. Fiscus, Yasaman Haghpanah, Shudong Huang, David M. Joy, Kay Peterson, Ian M. Soboroff
Initiated in conjunction with DARPA's Low Resource Languages for Emergent Incidents (LORELEI) Program, the NIST LoReHLT (Low Re-source Human Language Technology) evaluation series seeks to incubate research on fundamental natural language processing tasks

Translation Adequacy and Preference Evaluation Tool (TAP-ET)

May 28, 2008
Author(s)
Mark A. Przybocki, Kay Peterson, P. S. Bronsart
Evaluation of Machine Translation (MT) technology is often tied to the requirement for tedious manual judgments of translation quality. While automated MT metrology continues to be an active area of research, a well known and often accepted standard metric
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