Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks
The paper discusses the challenges of measuring performance in Large Language Models (LLMs) as they move into production. It highlights the systemic measurement bias present in current evaluation methodologies and proposes a new framework to address these issues. The authors introduce a composite metric to improve accuracy in profiling LLM performance at scale.
- ▪Current evaluation methodologies for LLMs suffer from severe measurement bias.
- ▪The authors propose an unbiased, multi-process evaluation framework to distribute client-side load.
- ▪A new composite metric, Normalized Time Per Output Token (NTPOT), is introduced to improve latency measurement.
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Computer Science > Artificial Intelligence arXiv:2605.24217 (cs) [Submitted on 22 May 2026] Title:Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks Authors:Ashok Chandrasekar, Jason Kramberger View a PDF of the paper titled Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks, by Ashok Chandrasekar and 1 other authors View PDF HTML (experimental) Abstract:As Large Language Models (LLMs) transition from research environments to production deployments, evaluating their performance against strict Service Level Objectives (SLOs) has become critical. However, current evaluation methodologies suffer from severe measurement bias at scale.
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