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01mznaser /Role-of-Provider-on-Safety-Alignment-in-Large-Language-Models Evaluating the Role of Provider on Safety Alignment in Large Language Models: dataset Data for the paper Naser, M.Z. (2026). Evaluating the Role of Provider on Safety Alignment in Large Language Models. Neurocomputing, 135173. https://doi.org/10.1016/j.neucom.2026.135173 It holds the Extended Context Safety Benchmark (ECSB) scenario bank and every trial result. If you use the data, please cite the paper (BibTeX under Citation). The metadata.paper field inside… See the full description on the dataset page: https://huggingface.co/datasets/mznaser/Role-of-Provider-on-Safety-Alignment-in-Large-Language-Models.tabulartext-classification10K<n<100K0 likes19 downloads1d agoHugging Face02eval-aware /Large-Language-Models-Often-Know-When-They-Are-Being-Evaluatedgated Dataset Card for Evaluation Awareness Benchmark Dataset Summary This benchmark checks whether a language model can recognise when a conversation is itself part of an evaluation rather than normal, real-world usage. The dataset contains 976 conversational transcripts with rich metadata, including: True evaluation transcripts from prompt-injection tests, red-teaming tasks, and coding challenges Organic/real transcripts from actual user queries, scraped chats, and… See the full description on the dataset page: https://huggingface.co/datasets/eval-aware/Large-Language-Models-Often-Know-When-They-Are-Being-Evaluated.tabulartext-classificationn<1K0 likes17 downloads6mo agoHugging Face

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