Data · dataset · 2026
A Hallucination Detection Dataset for Large Language Models Based on Four Chinese Classical Novels
Listed in ScienceDB
This dataset is developed for factual consistency evaluation and hallucination detection tasks in large language models (LLMs).
Description
It is constructed based on four representative Chinese classical novels: Dream of the Red Chamber, Journey to the West, Romance of the Three Kingdoms, and Water Margin.The dataset is derived from the original texts of the four classical novels and constructed through a series of preprocessing procedures, including data cleaning, text filtering, and hallucination sample generation.
It consists of both non-hallucinated samples and hallucinated samples. Specifically, non-hallucinated samples preserve factual information consistent with the original literary texts, representing correct factual expressions in model-generated content. In contrast, hallucinated samples are constructed by introducing information inconsistent with the original texts, factual errors, or content that conflicts with the original narratives, thereby simulating factual errors that may occur during the generation process of large language models.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00133.000lk ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Artificial intelligence 68% · Text 75%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00133.000lk | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4602 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (68%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:text | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |