How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking
Factual evidence
What the source reports
Researchers analyzed eight LLM evaluation design choices, showing setup decisions significantly impact reported model performance scores.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 15 October 2026
- Collected by OneBench
- 16 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.19185v1 Announce Type: new Abstract: Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to…
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OneBench interpretation
Institutional assessment
So what
Inconsistent benchmarking choices distort LLM performance metrics, directly undermining validation rigour under enterprise model risk management frameworks.
Do what
Review internal model risk evaluation procedures to ensure standardisation across benchmarking setups and prompt formulations.