Berk Kalelioğlu
Research interests
Berk focuses on machine learning, agentic AI tools, and large and small language models (LLMs and SLMs).He is part of the AIMultiple benchmark team, conducting assessments and providing insights to help readers understand emerging technologies and their real-world applications.
Professional experience
He began his career as a Tech Project Lead at ODTU IVME-R, where he led a project to build physical quantum and pseudorandom number generators.After his tenure at IVME-R, he co-founded a game development company and released a game on Steam.
He later shifted his career toward AI and joined AIMultiple as a Researcher.
Education
Berk holds a Bachelor’s degree in Mathematics from Ankara University.Latest Articles from Berk
Agentic IT: Can LLMs Design a Benchmark
We gave 12 large language models the job a benchmark team does: invent a benchmark, build it, run four models through it, and report the results. Each did it twice. None of the 24 attempts passed every criterion, and six of the rubric’s checks were passed by none of them. The two topics are text-to-SQL,…
Best Flat-Rate LLM API Providers
Flat-rate LLM providers sell unlimited model usage for a fixed monthly price instead of billing per token. This model spread because agentic coding sessions can use tens of millions of tokens, so a per-token bill is hard to predict. Very few providers offer a true flat fee; most plans marketed as flat carry a usage…
Tabular Models Benchmark: Performance Across 19 Datasets
We benchmarked 8 tabular learning models on 19 real-world datasets covering roughly 260,000 samples, with dataset sizes from 435 to 48,800 rows. Every model ran on the same machine with 5-fold cross-validation and identical splits. Each dataset is a round-robin of head-to-head matches between models, decided by the primary metric. Elo aggregates all 483 matches…
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