Datasets:

KRLabsOrg
/

lettucedetect-prose-hallucination

license: cc-by-4.0
task_categories:
  - token-classification
  - text-classification
language:
  - en
  - de
  - fr
  - es
  - it
  - ca
  - cs
  - fi
  - sv
  - ar
  - fa
  - hi
  - zh
  - eu
tags:
  - hallucination-detection
  - rag
  - multilingual
  - lettucedetect
size_categories:
  - 10K<n<100K

LettuceDetect Prose Hallucination Dataset

Token-level hallucination annotations on LLM answers grounded in prose context, drawn from two public RAG hallucination resources and mapped into one unified taxonomy. This is the prose counterpart to the structured-context (code, tool output, documents) collection — together they let a single detector be trained across modalities.

Two sources sit side by side, distinguished by the dataset field:

`dataset`	Spans	Source
`psiloqa`	natural — produced by real LLMs answering Wikipedia-grounded questions	s-nlp/PsiloQA
`ragtruth`	annotated on LLM RAG answers (QA, summarization, data-to-text)	RAGTruth

PsiloQA matters because its hallucinations are not synthetically injected — they occur naturally when models answer grounded questions. That makes this collection a check that a detector generalizes beyond any single corruption process, and it spans 14 languages.

Statistics

Split	Total	Hallucinated	Clean
train	78,882	63,349	15,533
validation	3,355	2,993	362
test	5,597	3,563	2,034
Total	87,834	69,905	17,929

Per source:

Source	Total	Languages
`psiloqa`	70,044	14
`ragtruth`	17,790	en

RAGTruth has no native validation split, so validation is PsiloQA only.

Hallucination Taxonomy

Spans are labeled with the LettuceDetect unified taxonomy — three injectable top-level categories, each with subtypes. Both sources map into the same space.

Category	Definition	Example subtypes
`contradiction`	Conflicts with what the context states (wrong value, number, date, entity, relation)	`numerical`, `temporal`, `entity`, `relational`, `value`
`unsupported_addition`	Adds a claim the context neither states nor implies	`claim`, `elaboration`, `subjective`
`fabricated_reference`	References a named element absent from the context	`entity`, `section`, `identifier`

How each source is typed:

RAGTruth maps mechanically from its native labels (Evident/Subtle Conflict → contradiction; Baseless Info → unsupported_addition, or fabricated_reference when the span introduces a proper noun absent from the context).
PsiloQA ships only binary character spans with no error type, so each span is classified into the taxonomy by an LLM that reads the context and the answer with the span marked. Clean (non-hallucinated) PsiloQA answers are kept as natural negatives.

Data Format

Each line in the JSONL files is one sample:

{
  "prompt": "User request: ...\n\n...grounded passage...",
  "context": "...grounded passage...",
  "question": "the user request",
  "answer": "the LLM answer (hallucinated or clean)",
  "labels": [
    {"start": 23, "end": 41, "label": "contradiction",
     "category": "contradiction", "subcategory": "numerical"}
  ],
  "split": "train",
  "task_type": "qa",
  "dataset": "psiloqa",
  "language": "en",
  "context_modality": "prose",
  "category": "contradiction",
  "subcategory": "numerical",
  "metadata": "{\"id\": \"...\", \"is_hallucinated\": true, \"classifier_model\": \"Qwen/Qwen3.6-35B-A3B\"}"
}

Fields:

prompt — the full model input, request first (User request: {question}\n\n{context}) so it survives context truncation
context / question — the grounding passage and the request, separately (question is null for RAGTruth summarization). PsiloQA's foreign-language questions are kept verbatim.
answer — the LLM response (hallucinated or clean)
labels — annotated hallucination spans (empty for clean samples)
category / subcategory — unified taxonomy fields (sample-level is a majority vote over its spans)
dataset — source discriminator (psiloqa or ragtruth)
metadata — a JSON-encoded string of source-specific provenance. Parse with json.loads.

Usage

import json
from datasets import load_dataset

ds = load_dataset("KRLabsOrg/lettucedetect-prose-hallucination")

# Everything, or filter by source / language
psiloqa = ds["train"].filter(lambda r: r["dataset"] == "psiloqa")
german = ds["train"].filter(lambda r: r["language"] == "de")

for sample in ds["train"]:
    if sample["labels"]:
        meta = json.loads(sample["metadata"])
        print(sample["language"], sample["category"], meta.get("is_hallucinated"))

Limitations

PsiloQA span types are assigned by an LLM rather than human annotators, so the (category, subcategory) labels carry model noise; the underlying spans are from the original human/automatic PsiloQA annotation. RAGTruth subcategories are derived from coarse native labels and are not exhaustive.

Citation

This dataset is the prose part of the unified benchmark in:

@inproceedings{kovacs-etal-2026-beyond,
    title = "Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documents",
    author = "Kov{\'a}cs, {\'A}d{\'a}m  and
      He, Bowei  and
      Liu, Xue  and
      Boros, Istv{\'a}n  and
      T{\'o}th, Szilveszter  and
      Recski, Gabor",
    editor = "Wang, Yimu  and
      Choi, Yee Man  and
      Wu, Di  and
      Ouyang, Siqi  and
      Azadani, Mozhgan Nasr  and
      Fung, Yi R. (May)",
    booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.groundlm-1.44/",
    pages = "566--577",
    ISBN = "979-8-89176-478-1"
}

Also on arXiv: 2607.00895. LettuceDetect itself:

@misc{Kovacs:2025,
      title={LettuceDetect: A Hallucination Detection Framework for RAG Applications},
      author={Ádám Kovács and Gábor Recski},
      year={2025},
      eprint={2502.17125},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.17125},
}

Please also cite the original PsiloQA and RAGTruth sources.

License

CC-BY-4.0