{"url":"/dataset/rt-inod-finance","name":"rt-inod-finance","full_name":"Red Teaming Innodata Finance","description_markdown":"The Innodata Red Teaming Prompts aims to rigorously assess models’ factuality and safety. This dataset, due to its manual creation and breadth of coverage, facilitates a comprehensive examination of LLM performance across diverse scenarios.","description_withheld":null,"homepage":"https://huggingface.co/datasets/innodatalabs/rt-inod-finance","introduced_date":"2024-04-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-llama2-mistral-gemma-and-gpt-for","title":"Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Hallucinations","first_author":"David Nadeau","url":null},"license":{"name":"CC BY-SA 4.0","url":"https://creativecommons.org/licenses/by-sa/4.0/deed.en"},"modalities":[],"tasks":[],"languages":[],"variants":["rt-inod-finance"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}