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 [1].
Position: Measure Dataset Diversity, Don’t Just Claim It
Authors: Dora Zhao, Jerone Andrews, Orestis Papakyriakopoulos, Alice Xiang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our research explores the implications of this issue by analyzing diversity across 135 image and text datasets. |
| Researcher Affiliation | Collaboration | 1Stanford University, Stanford, USA 2Sony AI, London, UK 3Technical University of Munich, Munich, Germany 4Sony AI, Seattle, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology described. |
| Open Datasets | Yes | Our scrutiny extends to 135 text and image datasets, where we uncover key considerations and offer recommendations for applying measurement theory to their collection. ...To inform our position, we conducted a systematic literature review encompassing 135 datasets presented as being more diverse. |
| Dataset Splits | No | The paper discusses the concept of validation as applied to datasets (e.g., "construct validity"), but it does not describe training, validation, or test dataset splits for its own research, which is a systematic literature review and conceptual analysis. |
| Hardware Specification | No | The paper describes a systematic literature review and conceptual framework. It does not involve computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper describes a systematic literature review and conceptual framework. It does not specify any software components with version numbers used for its analysis. |
| Experiment Setup | No | The paper describes a systematic literature review and conceptual framework. It does not include specific details on experimental setup such as hyperparameters, training configurations, or system-level settings. |