How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework
arXiv:2507.19219v2 Announce Type: replace Abstract: Overestimation in evaluating large language models (LLMs) has become an increasing concern. Due to the contamination of public benchmarks or imbalanced model training, LLMs may achieve unreal evaluation results on public benchmarks, either intentionally or unintentionally, which leads to unfair comparisons among LLMs and undermines their realistic capability assessments. Existing benchmarks attempt to address these issues by keeping test cases permanently secret, mitigating contamination through human evaluation, or repeatedly collecting and
The proliferation of Large Language Models and their increasing application in critical domains necessitate reliable evaluation methods, making the current moment ripe for scrutinizing their actual performance.
Overestimated LLM performance due to evaluation flaws can lead to misallocation of R&D resources, flawed product development, and an inaccurate understanding of AI capabilities, affecting strategic investment and policy decisions.
The focus shifts from raw benchmark scores to understanding the integrity of those evaluations, pushing for more robust and contamination-resistant testing methodologies for LLMs.
- · AI evaluation startups
- · Independent AI research labs
- · Developers of robust testing frameworks for LLMs
- · LLMs with over-inflated benchmark scores
- · Companies relying solely on public benchmark results for product claims
- · Entities with weak internal evaluation pipelines
Increased scrutiny and demand for transparent, verifiable evaluation processes for all AI models, especially LLMs.
A potential slowdown in the perceived advancement of some LLMs as more realistic performance assessments emerge, tempering investor expectations.
The development of new regulatory standards or best practices for AI model benchmarking, influencing industry competition and market entry.
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