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..
Test-Time Fairness and Robustness in Large Language Models
Authors: Leonardo Cotta, Chris J. Maddison
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we use six benchmark datasets with nine different protected/spurious attributes to show that OOC prompting achieves state-of-the-art results on fairness and robustness across different model families and sizes, without sacrificing much predictive performance. |
| Researcher Affiliation | Collaboration | Leonardo Cotta EMAIL Vector Institute Chris J Maddison EMAIL University of Toronto Vector Institute |
| Pseudocode | Yes | Algorithm 1 OOC prompting strategy. |
| Open Source Code | No | The paper does not provide explicit links to source code or statements about its release. The link provided in the header is for the paper's review on Open Review, not a code repository. |
| Open Datasets | Yes | Toxic Comments. We consider the dataset civilcomments as proposed in Koh et al. (2021). Bios. We take the dataset of biography passages originally proposed by De-Arteaga et al. (2019). Amazon. Here, we have the Amazon fashion reviews dataset (Ni et al., 2019). Discrimination. We also take the synthetic dataset of yes/no questions recently proposed by Tamkin et al. (2023). Clinical. Finally, we consider the MIMIC-III (Johnson et al., 2016) set of clinical notes (X). Both the context and the label information are extracted from the subset MIMICSBDH (Ahsan et al., 2021). |
| Dataset Splits | Yes | For each dataset and context pair, we estimate the SI-bias with 200 random examples balanced according to S and Z. To compute the predictive performance (macro F1-score2) of each prompting strategy, we take 200 random examples sampled i.i.d. from the original dataset. |
| Hardware Specification | No | The paper mentions several LLM models used (e.g., gpt-3.5-turbo, gpt-4-turbo, LLAMA-3-70B, Claude 3.5 Sonnet, gpt-4o-mini) but does not provide any specific details about the hardware (GPUs, CPUs, etc.) on which these models were run for the experiments. |
| Software Dependencies | No | The paper mentions the use of various LLMs (e.g., gpt-3.5-turbo, gpt-4-turbo, LLAMA-3-70B, Claude 3.5 Sonnet), but it does not specify any software libraries, frameworks, or their version numbers that would be necessary to replicate the experiments. |
| Experiment Setup | Yes | As common practice (Wei et al., 2022), we use temperature 0 to predict the labels of each task (including OOC). We evaluate stratified invariance in three popular, frontier LLM models: gpt-3.5-turbo, gpt-4-turbo (Open AI, 2023), and LLAMA-3-70B (Dubey et al., 2024). As suggested in (Sordoni et al., 2023), we generate our counterfactual transformations with a temperature of 0.7 (GPT family) and 0.8 in the other models. In each task, we used m = 3 samples for OOC with all models and tasks except for gpt-4-turbo and Clinical where we used m = 1 due to their high monetary cost and larger input size, respectively. |