arXiv (All)
open access
Decomposing and Measuring Evaluation Awareness
Frontier language models sometimes recognize that they are under evaluation and adjust their behavior which can undermine validity of benchmark results. Yet the field studies it without a shared foundation, conflating flaws of the evaluation with capabilities of the model, and detection with behavio
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Beyond Fluency: A Human-Centered Evaluation Rubric for Grounding, Answerability, and Reliability in LLM Outputs#118367
- Do Not Trust the Benchmark: Limitations of General LLM Rankings and a Case for Task-Specific Evaluation#1048945
- Do large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effects#792877
- Do large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effects#788873
- Aggregate benchmark scores obscure patient safety implications of errors across frontier language models#29744
Record · ID 1131543
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive