factual
Datasets
All datasets matching “factual”moltbook-factual-threshold-v2
Moltbook: Factual Threshold Dose-Response Experiment (Replicated)
Experimental data from a replicated dose-response study on Moltbook, a Reddit-like social network for AI agents. The experiment measures how varying the number of factual posts (0→5) in a conspiracy-heavy environment affects agent voting and engagement behavior.
Research question: How many factual posts are needed before LLM agents start preferentially upvoting them over conspiracy content?
This dataset contains 20… See the full description on the dataset page: https://huggingface.co/datasets/Ayushnangia/moltbook-factual-threshold-v2.self-alignment-for-factualityThe data was organized and utilized in Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation.
If you find our data useful, please cite our work using the following reference:
@inproceedings{zhang-etal-2024-self,
title = "Self-Alignment for Factuality: Mitigating Hallucinations in {LLM}s via Self-Evaluation",
author = "Zhang, Xiaoying and
Peng, Baolin and
Tian, Ye and
Zhou, Jingyan and
Jin, Lifeng and
Song, Linfeng and… See the full description on the dataset page: https://huggingface.co/datasets/xyingzhang/self-alignment-for-factuality.xsum_factualityNeural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input
document. The popular metric such as ROUGE fails to show the severity of the problem. The dataset consists of
faithfulness and factuality annotations of abstractive summaries for the XSum dataset. We have crowdsourced 3 judgements
for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.news_media_bias_and_factuality
News Media Factual Reporting and Political Bias
Dataset introduced in the paper "Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions" published in the CLEF 2024 main conference.
Similar to the news media reliability dataset, this dataset consists of a collections of 4K new media domains names with political bias and factual reporting labels.
Columns of the dataset:
source: domain name
bias: the political bias label. Values: "left"… See the full description on the dataset page: https://huggingface.co/datasets/sergioburdisso/news_media_bias_and_factuality.mixtral-factual-QA
Mixtral Factual QA
Generate questions and answers based on context provided. We use contexts from,
maktabahalbakri.com
muftiwp.gov.my
asklegal.my
dewanbahasa-jdbp
gov.my
patriots
rootofscience
majalahsains
nasilemaktech
alhijrahnews
https://huggingface.co/datasets/open-phi/textbooks
notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/question-answer/mixtral-factual
factually-wrong-qa-coding.jsonl, 31253 rows, 425 MB
factually-wrong-qa.jsonl, 1108037 rows, 10… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/mixtral-factual-QA.event_factuality
Event Factuality (It Happened / UDS-IH2)
Source
Decomp “It Happened” (UDS-IH2): https://decomp.io/projects/factuality/
UD English-EWT v1.2 (r1.2) for sentence reconstruction: https://github.com/UniversalDependencies/UD_English-EWT/tree/r1.2
Contains raw web/news text; included for research purposes only (no endorsement).
Task
Binary predicate-level event factuality (one row per predicate).
Labels
label: 0=false, 1=true
label_rule: single, agree, na_other, tie_conf4_vs0… See the full description on the dataset page: https://huggingface.co/datasets/compling/event_factuality.