Do LLMs Know They Are Being Tested? Evaluation Awareness and Incentive-Sensitive Failures in GPT-OSS-20B

• URL: http://arxiv.org/abs/2510.08624v1
• Date: Wed, 08 Oct 2025 09:49:05 GMT
• Title: Do LLMs Know They Are Being Tested? Evaluation Awareness and Incentive-Sensitive Failures in GPT-OSS-20B
• Authors: Nisar Ahmed, Muhammad Imran Zaman, Gulshan Saleem, Ali Hassan,
• Abstract summary: We investigate whether "evaluation scent" inflates measured performance without commensurate capability gains.<n>We run six paired A/B scenarios that hold task content and decoding fixed while varying framing.<n>We provide a reproducible A/B framework (prompt banks, validators, per-run scores, scripts) and practical guidance.
• Score: 1.948261185683419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Benchmarks for large language models (LLMs) often rely on rubric-scented prompts that request visible reasoning and strict formatting, whereas real deployments demand terse, contract-bound answers. We investigate whether such "evaluation scent" inflates measured performance without commensurate capability gains. Using a single open-weights model (GPT-OSS-20B), we run six paired A/B scenarios that hold task content and decoding fixed while varying framing (evaluation-oriented vs. real-world) and reasoning depth (Medium/High): deterministic math, strict code-fix, citation generation, incentive flips (caution vs. competence), CoT visibility, and multilingual (Urdu) headers. Deterministic validators compute accuracy, answer-only compliance, hedging/refusals, chain-of-thought (CoT) length, and schema compliance, with pre-registered deltas and composite indices. Across scenarios, evaluation framing reliably inflates CoT (hundreds to >1000 characters) and reduces answer-only compliance, with limited or inconsistent accuracy gains. In structured outputs, it improves wrappers (e.g., fenced blocks, enumerated lists) but not regex-validated substance. Incentive wording reweights error composition: praising caution modestly improves accuracy at high reasoning and reduces wrong-but-confident errors, whereas praising competence yields terser but riskier outputs. Urdu rubric headers reproduce these signatures and can decrease accuracy at higher reasoning depth, indicating multilingual parity risks. We provide a reproducible A/B framework (prompt banks, validators, per-run scores, scripts; versioned DOI) and practical guidance: neutral phrasing or dual-framing checks, contract-aware grading, style-delta reporting, confidence governance, and multilingual dashboards to ensure that benchmark gains reflect deployable capability.

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