{"url":"/dataset/finetune-raf","name":"Finetune-RAG","full_name":null,"description_markdown":"This dataset is part of the Finetune-RAG project, which aims to tackle hallucination in retrieval-augmented LLMs. It consists of synthetically curated and processed RAG documents that can be utilised for LLM fine-tuning.\r\n\r\nEach line in the finetunerag_dataset.jsonl file is a JSON object:\r\n\r\n```json\r\n{\r\n  \"content\": \"<correct content chunk retrieved>\",\r\n  \"filename\": \"<original document filename>\",\r\n  \"fictitious_filename1\": \"<filename of fake doc 1>\",\r\n  \"fictitious_content1\": \"<misleading content chunk 1>\",\r\n  \"fictitious_filename2\": \"<filename of fake doc 2>\",\r\n  \"fictitious_content2\": \"<misleading content chunk 2>\",\r\n  \"question\": \"<user query>\",\r\n  \"answer\": \"<GPT-4o answer based only on correct content>\",\r\n  \"content_before\": \"<optional preceding content>\",\r\n  \"content_after\": \"<optional succeeding content>\"\r\n}\r\n```\r\n\r\nNote that the documents contain answers generated by GPT-4o. Additionally, the prompts used to generate the selected answers do not involve any ficticious data, ensuring that the answers are not contaminated when used for fine-tuning.","description_withheld":null,"homepage":"https://huggingface.co/datasets/pints-ai/Finetune-RAG","introduced_date":"2025-05-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/finetune-rag-fine-tuning-language-models-to","title":"Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation","first_author":"Zhan Peng Lee","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Finetune-RAG"],"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."}