DISCO: Diversifying Sample Condensation for Efficient Model Evaluation
Authors: name: Alexander Rubinstein, name: Benjamin Raible, name: Martin Gubri, name: Seong Joon Oh
Organization: Eberhard Karls Universität Tübingen

Evaluating modern machine learning models has become prohibitively expensive.
Benchmarks such as LMMs-Eval and HELM demand thousands of GPU hours per model.
Costly evaluation reduces inclusivity, slows the cycle of innovation, and
worsens environmental impact. The typical approach follows two steps. First,
select an anchor subset of data. Second, train a mapping from the accuracy on
this subset to the final test result. The drawback is that anchor selection
depends on clustering, which can be complex and sensitive to design choices. We
argue that promoting diversity among samples is not essential; what matters is
to select samples that maximise diversity in model responses. Our
method, Diversifying Sample Condensation (DISCO), selects the top-k
samples with the greatest model disagreements. This uses greedy, sample-wise
statistics rather than global clustering. The approach is conceptually simpler.
From a theoretical view, inter-model disagreement provides an
information-theoretically optimal rule for such greedy selection.
DISCO shows empirical gains over prior methods, achieving
state-of-the-art results in performance prediction across MMLU, Hellaswag,
Winogrande, and ARC. Code is available here:
https://github.com/arubique/disco-public.