datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
benchmark-llms-landuse-relevance
Land-use relevance benchmark
v3-multilingual · 85 languages x 300 items/language ·
25,500 items · binary yes/no labels.
Code
Package version recorded in run metadata: 0.2.0 (some runs lack version metadata).
Task and prompt
Does a sentence describe a place's land or environment in ways visible to satellites?
English prompt · greedy decoding · seed 0 · max_new_tokens=4096 ·
bfloat16 · batch varies by model.
unsloth/Qwen3.8-27B-GGUF@UD-IQ2_XXS runs the UD-IQ2_XXS… See the full description on the dataset page: https://huggingface.co/datasets/NoeFlandre/benchmark-llms-landuse-relevance.llm_stocksai-respondents-challenge
AI Respondents Challenge — Oxford LLMs 2026
Predict a survey respondent's answer to a held-out question from their other
answers (World Values Survey wave 7). Any method allowed; you must disclose the
features and prompts you used. Ranked on normalized skill + distributional
alignment, on in-domain and out-of-domain (held-out countries) boards.
Configs
train — 5,000 labeled respondents (100 per seen
country): respondent_id, country + all WVS variables… See the full description on the dataset page: https://huggingface.co/datasets/oxford-llms/ai-respondents-challenge.llmstxt-corpus
The llms.txt corpus
Measurement data on the llms.txt convention, collected in one run on
5 August 2026.
llms.txt is a plain-text file at a site's root, proposed as a curated map
telling AI systems what the site contains. This is a measurement of what is
actually being published under that name.
Canonical release: https://doi.org/10.5281/zenodo.22859104
This repository mirrors that deposit. Cite the DOI, which always resolves to the
newest version.
Two observations… See the full description on the dataset page: https://huggingface.co/datasets/sempite/llmstxt-corpus.moral-tracing-in-LLMs
LLM Moral Evolution Study
A longitudinal dataset tracking moral reasoning patterns across 14 large language models from OpenAI and Anthropic, spanning multiple generations (2023–2025). The dataset measures how moral stances, ethical judgments, and value priorities shift across model updates using a 107-item probe instrument grounded in Moral Foundations Theory.
Models
OpenAI
Model
Release
GPT-3.5 Turbo
2023-11
GPT-4
2023-03
GPT-4o… See the full description on the dataset page: https://huggingface.co/datasets/mznaser/moral-tracing-in-LLMs.LLMs-Sentiment-Augmented-Bitcoin-Dataset
Leveraging LLMs for Informed Bitcoin Trading Decisions: Prompting with Social and News Data Reveals Promising Predictive Abilities
The work was carried out by:
Danilo Corsi
Cesare Campagnano
Description
This project investigates the potential of leveraging Large Language Models (LLMs) to support Bitcoin traders. Specifically, we analyze the correlation between Bitcoin price movements and sentiment expressed in news headlines, posts, and comments on social media.
We… See the full description on the dataset page: https://huggingface.co/datasets/danilocorsi/LLMs-Sentiment-Augmented-Bitcoin-Dataset.llm-system-ops-production-telemetry-sft-data
🤖📈 LLM System Ops Telemetry (Synthetic)
A synthetic, production-style, multi-table LLM telemetry dataset designed for LLMOps analytics and decision-grade experiments.
It supports monitoring cost, latency, tokens, failures, safety flags, tool usage, and user feedback at the interaction level,
with rollups at the session and user levels — plus an SFT table aligned 1:1 with interactions and a prompt/config dimension.
Synthetic data (safe for teaching, prototyping, and portfolio… See the full description on the dataset page: https://huggingface.co/datasets/tarekmasryo/llm-system-ops-production-telemetry-sft-data.normative_evaluation_llms_everyday_dilemmasllms_epistemic_consistency
LLMs Epistemic Consistency Dataset
This dataset artifact contains the stimuli and prompt templates used for experiments on epistemic consistency and political-cue sensitivity in LLM evaluations.
Dataset URL: https://huggingface.co/datasets/drozado/llms_epistemic_consistency
Contents
croissant.json: root-level copy of the completed Croissant metadata for NeurIPS 2026 Evaluations and Datasets submission.
metadata/croissant.json: same Croissant metadata, kept with the… See the full description on the dataset page: https://huggingface.co/datasets/drozado/llms_epistemic_consistency.llm-smartrouter-benchmark
LLM SmartRouter & Agent Highway Latency & Cost Benchmark (v1.4.0)
Empirical performance benchmark dataset comparing direct model endpoints (OpenAI, Anthropic Claude, Google Gemini) against the PixelRouter / BLUN SmartRouter proxy layer and Autonomous Agent Web Highway (https://api.pixeloffice.eu/v1).
v1.4.0 Benchmark Highlights
Anthropic Claude Messages API: Sub-35ms proxy routing for native /v1/messages payloads with 94%+ cost savings.
Machine Web Highway… See the full description on the dataset page: https://huggingface.co/datasets/pixeloffice/llm-smartrouter-benchmark.astro-llms-full-query-data
AstroLLMs Full Query Dataset
This dataset includes all of the data collected in a four-week deployment of a Large Language Model-powered Slack chatbot trained on astrophysics papers. Astronomers were invited to interact with the chatbot, ask questions, and leave feedback. This data includes 368 question-answer pairs, including feedback, reactions, and labeling.
Dataset Structure
The columns of this dataset are thread_ts (unique time stamp of the query), channel_id… See the full description on the dataset page: https://huggingface.co/datasets/jhu-clsp/astro-llms-full-query-data.Detecting-Access-Violations-in-a-LLMs-Pre-Training-Data
Beyond Public Access in LLM Pre-Training Data
The official HuggingFace repository for the paper "Beyond Public Access in LLM Pre-Training Data" by The AI Disclosures Project.
Using a legally obtained dataset of 34 copyrighted O'Reilly Media books, we apply the DE-COP membership inference attack method to investigate whether OpenAI's large language models were trained on copyrighted content without consent.
algozee_rag-based-hallucination-reduction-in-llms
RAG-Based Hallucination Reduction in LLMs
Introduction to Large Language Models and Hallucination Problem
Dataset Info
Source: Kaggle
Original Size: 0.17 MB
Kaggle Downloads: 43
Files: 1
Files
llm_rag_dataset_6k.csv.csv
Mirrored from Kaggle
mPIQA-MRL-2025-EMNLPfolioSouthAsian-LLMs-Data-Code