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..
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
Authors: Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher Kanan, Anton van den Hengel
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that embarrassingly-simple methods, including one that generates answers at random, surpass the state of the art on some question types. We provide shortand long-term solutions to avoid these pitfalls and realize the benefits of OOD evaluation. |
| Researcher Affiliation | Collaboration | 1Australian Institute for Machine Learning, University of Adelaide, Australia 2Adobe Research 3Rochester Institute of Technology |
| Pseudocode | No | The paper describes methods in text and equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | The VQA-CP dataset (for Changing Priors) was designed to evaluate VQA models in a setting where they cannot rely on language biases. The dataset was built by reorganizing the training/test splits of VQA v2 as follows. The questions are assigned to one of 65 question types according to their prefix (first few words). The prefixes were defined in [20]. All question/image/answer triplets are then clustered according to the combination of prefix and answer. |
| Dataset Splits | Yes | On VQA-CP, we hold out 8,000 instances from the training set (VQA-CP val.) to measure in-domain performance as proposed in [22, 14, 41]. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies or versions (e.g., programming languages, libraries, frameworks with version numbers) are mentioned in the paper. |
| Experiment Setup | Yes | The regularizer weight λ allows tuning the trade-off between in-domain and OOD performance. We plot in Fig. 4 and Fig. 5 (in the supp. mat.) the accuracy as a function of λ. |