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The evaluation of generative or discriminative large language model (LLM)-based systems is often a complex multi-dimensional problem.
Sensitivity of decisions to probability estimation errors: A reexamination
Peter C Fishburn, Allan H Murphy, and Herbert H Isaacs · 1968
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Multiobjective optimization in river basin development
Lucien Duckstein and Serafim Opricovic · 1980
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Methods for Multiple Attribute Decision Making , pages 58–191
Ching-Lai Hwang and Kwangsun Yoon · 1981
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Statistical Power Analysis for the Behavioral Sciences
Jacob Cohen · 1988
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Calculating and synthesizing effect sizes
III Turner, Herbert M and Robert M Bernard · 2006
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New effect size rules of thumb
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Statsmodels: Econometric and statistical modeling with python
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Using effect size—or why the p value is not enough
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A multiple criteria decision making method based on relative value distances
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Summary and analysis of extension program evaluation in R
Salvatore S. Mangiafico · 2016
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Choosing between methods of combining-values
Nicholas A Heard and Patrick Rubin-Delanchy · 2018
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The harmonic mean p-value for combining dependent tests
Daniel J Wilson · 2019
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The spotis rank reversal free method for multi-criteria decision-making support
Jean Dezert, Albena Tchamova, Deqiang Han, and Jean-Marc Tacnet · 2020
Simultaneous confidence intervals for ranks with application to ranking institutions
Diaa Al Mohamad, Jelle J Goeman, and Erik W van Zwet · 2022
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pymcdm—the universal library for solving multi-criteria decision-making problems
Bartłomiej Kizielewicz, Andrii Shekhovtsov, and Wojciech Sałabun · 2023
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Using combinatorial optimization to design a high quality llm solution
Samuel Ackerman, Eitan Farchi, Rami Katan, and Orna Raz · 2024
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CrossCodeEval: A diverse and multilingual benchmark for cross-file code completion
Yangruibo Ding, Zijian Wang, Wasi Ahmad, Hantian Ding, Ming Tan, Nihal Jain, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, et al · 2024
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Performances are plateauing, let’s make the leaderboard steep again, 2024
Clementine Fourrier, Nathan Habib, Konrad Szafer Alina Lozovskaya, and Thomas Wolf · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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emmeans: Estimated Marginal Means, aka Least-Squares Means , 2024a
Russell V. Lenth
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Comparisons and contrasts in emmeans, 2024b
Russell V. Lenth
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Granite code models: A family of open foundation models for code intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Shivdeep Singh, et al · 2024
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harmonicmeanp
Daniel J Wilson · 2024
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