4 citations · 5 across the 6 of their papers we have counts for
6 papers
Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results
Jan Batzner, Sree Harsha Nelaturu, Damian Stachura +45
AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First…
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
Avijit Ghosh, Anka Reuel, Jenny Chim +45
AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. The cost is interpretive: readers can…
What Do Evolutionary Coding Agents Evolve?
Nico Pelleriti, Sree Harsha Nelaturu, Zhanke Zhou +4
Recent work pairs LLMs with evolutionary search to iteratively generate, modify, and select code using task-specific feedback. These systems have produced strong results in mathema…
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Angelika Romanou, Negar Foroutan, Anna Sotnikova +56
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal val…
Cyclic Sparse Training: Is it Enough?
Advait Gadhikar, Sree Harsha Nelaturu, Rebekka Burkholz
The success of iterative pruning methods in achieving state-of-the-art sparse networks has largely been attributed to improved mask identification and an implicit regularization in…
On The Fairness Impacts of Hardware Selection in Machine Learning
Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran +2
In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This oversight is particularly prob…