Finetune-RAG

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.

Each line in the finetunerag_dataset.jsonl file is a JSON object:

{
  "content": "<correct content chunk retrieved>",
  "filename": "<original document filename>",
  "fictitious_filename1": "<filename of fake doc 1>",
  "fictitious_content1": "<misleading content chunk 1>",
  "fictitious_filename2": "<filename of fake doc 2>",
  "fictitious_content2": "<misleading content chunk 2>",
  "question": "<user query>",
  "answer": "<GPT-4o answer based only on correct content>",
  "content_before": "<optional preceding content>",
  "content_after": "<optional succeeding content>"
}

Note 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.