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].
Measuring Diversity: Axioms and Challenges
Authors: Mikhail Mironov, Liudmila Prokhorenkova
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, we conduct a systematic review of existing diversity measures and explore their undesirable behavior in certain cases. Based on this review, we formulate three desirable properties (axioms)... Then, we construct two examples of measures that have all the desirable properties, thus proving that the list of axioms is not self-contradictory. Unfortunately, the constructed examples are too computationally expensive (NP-hard) for practical use. Thus, we pose an open problem of constructing a diversity measure that has all the listed properties and can be computed in practice or proving that all such measures are NP-hard to compute. Evaluating these measures in practical applications goes beyond the scope of the current paper, which addresses the theoretical aspects of diversity measures applicable to a wide variety of scenarios. |
| Researcher Affiliation | Industry | 1Yandex Research. Correspondence to: Mikhail Mironov <EMAIL>, Liudmila Prokhorenkova <EMAIL>. |
| Pseudocode | No | The paper describes methods mathematically and through textual explanations, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper discusses theoretical aspects of diversity measures and computational complexity but does not provide any statement regarding the release of source code for the methodology described. |
| Open Datasets | No | The paper uses conceptual examples (e.g., '16 points in the unit square with Euclidean distance') to illustrate theoretical concepts, but does not describe or use any specific datasets for empirical evaluation, nor does it provide access information for any such datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation on datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper focuses on theoretical analysis and mathematical proofs; thus, it does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |