Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
Authors: Gabriel Rioux, Apoorva Nitsure, Mattia Rigotti, Kristjan Greenewald, Youssef Mroueh
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase our method in comparing and benchmarking Large Language Models that are evaluated on multiple metrics. |
| Researcher Affiliation | Collaboration | Gabriel Rioux Center for Applied Mathematics Cornell University Apoorva Nitsure MIT-IBM Watson AI Lab IBM Research Mattia Rigotti MIT-IBM Watson AI Lab IBM Research Kristjan Greenewald MIT-IBM Watson AI Lab IBM Research Youssef Mroueh MIT-IBM Watson AI Lab IBM Research |
| Pseudocode | Yes | A Algorithm Algorithm 1 Multivariate Stochastic Order Multi-testing (relative and absolute) |
| Open Source Code | Yes | Code for these experiments is available at https://github.com/IBM/stochastic-order-eval. |
| Open Datasets | Yes | For our first evaluation we use the dataset from Jiang et al. [2023] (MIT license) that consists of responses from 12 different instruction following LLMs |
| Dataset Splits | No | The data has a train (100K rows) and test (5k rows) split where each row consists of an instruction, input sentence, the expected output from users, as well as the responses of a set of different LLMs with their decoding parameters and evaluation scores on different metrics. |
| Hardware Specification | Yes | All experiments were run on NVIDIA A100 80GB GPUs |
| Software Dependencies | Yes | All experiments were run on NVIDIA A100 80GB GPUs using Py Torch [Ansel et al., 2024] (v.2.3.0, BSD-3 license) and the Python Optimal Transport package [Flamary et al., 2021] (v.0.9.3, MIT license) |
| Experiment Setup | Yes | We then compute the pairwise ratios for these empirical distributions using the logistic loss with β = 0.2, the regularization parameter λ = 0.1, and utilize the relative testing procedure from Section 4.2 to rank the 12 LLMS |