README.md

metadata

pretty_name: CitationGround-1M (Platinum)
language:
  - en
license: apache-2.0
task_categories:
  - question-answering
  - text-generation
tags:
  - rag
  - grounding
  - citations
  - retrieval
  - hallucination-reduction
  - hard-negatives
size_categories:
  - n<1K
dataset_info:
  creator: Within US AI
  contact: Within US AI
  created: '2025-12-30T16:53:41Z'
  schema: See Features section below

CitationGround-1M (Platinum)

Developer/Publisher: Within US AI
Version: 0.1.0 (sample pack)
Created: 2025-12-30T16:53:41Z

What this dataset is

CitationGround-1M is a citation-locked grounded QA/RAG dataset:

• Answer using only the provided contexts
• Provide span-level citations (doc_id + offsets)
• Includes answerable=false hard negatives for abstention behavior

Features / schema (JSONL)

• example_id (string)
• question (string)
• contexts (list of docs)
• answer (string)
• citations (list of spans)
• answerable (bool)
• difficulty (int; 1–5)
• reason (string)
• language (string)
• created_utc (string)
• license_note (string)

Context doc format

• doc_id, title, text, source_type, provenance

Citation span format

• doc_id, start, end (character offsets in text)

Splits

• data/train.jsonl
• data/validation.jsonl
• data/test.jsonl

How to load

from datasets import load_dataset
ds = load_dataset("json", data_files={"train":"data/train.jsonl","validation":"data/validation.jsonl","test":"data/test.jsonl"})
print(ds["train"][0])