datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DEBATE
DEBATE: Diverse Multi-Agent Debates
This dataset is presented in the paper "MALLM: Multi-Agent Large Language Models Framework".
Citation
comming soon.
llmsys-hpobench
LLMSYS-HPOBench
LLMSYS-HPOBench is an offline benchmark dataset for hyperparameter optimization of real-world LLM systems. It covers inference engines, RAG pipelines, and agent frameworks, with normalized tabular measurements linked to log and hardware artifacts when available.
Project Links
GitHub repository, benchmark loader, and contribution guide: https://github.com/ideas-labo/llmsys-hpobench
Paper: https://arxiv.org/abs/2605.08305
Full data archive on… See the full description on the dataset page: https://huggingface.co/datasets/KleinWu/llmsys-hpobench.llms-mental-health-crisis-benchmark
Dataset Card for Between Help and Harm - Crisis Benchmark
Dataset Summary
This dataset repo contains the benchmark-side artifacts prepared for Hugging Face from the paper Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs, published in JMIR Mental Health.
If you use this dataset, please cite the paper. The citation is included below, the arXiv version is available at https://arxiv.org/abs/2509.24857, and the final DOI is allocated as… See the full description on the dataset page: https://huggingface.co/datasets/arnaiztech/llms-mental-health-crisis-benchmark.hiring-bias-mitigation-responses
Hiring-bias mitigation — model responses
Every response produced in the mitigation study of LLM hiring decisions: 54 runs,
2,471,850 responses, from 5 open-weight models in English and Ukrainian, at
baseline and under each mitigation family (baseline, embedding, prompt, scrub). Each run is one subset.
All released artifacts: the Hiring Bias Mitigation collection.
Training data of the fine-tuned runs: hiring-bias-mitigation-synthetic-data.
Code, configs, full results and… See the full description on the dataset page: https://huggingface.co/datasets/Stereotypes-in-LLMs/hiring-bias-mitigation-responses.llm-security-leaderboard-contentsai-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.llm-serving-selector-regret
LLM-Serving Selector Regret
LLM-Serving Selector Regret is a metrics-only research dataset for studying learned policy selection in LLM-serving schedulers. It contains derived selector/oracle/regret objects generated by Soroush Vahidi's research workflow, not raw request traces.
Creator / Provider
Dataset creator/provider: Soroush Vahidi.
The released selector/regret and policy-suitability metrics were generated by Soroush Vahidi's research workflow. Underlying… See the full description on the dataset page: https://huggingface.co/datasets/SoroushVahidi/llm-serving-selector-regret.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.GigaVerbo-filteredLLMs-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.llms-mental-health-crisis-responses
Dataset Card for Between Help and Harm - Responses and Evaluations
Dataset Summary
This dataset repo contains the response-side artifacts prepared for Hugging Face from the paper Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs, published in JMIR Mental Health.
If you use this dataset, please cite the paper. The citation is included below, the arXiv version is available at https://arxiv.org/abs/2509.24857, and the final DOI is allocated as… See the full description on the dataset page: https://huggingface.co/datasets/arnaiztech/llms-mental-health-crisis-responses.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.llm-serving-scheduler-baselines
LLM-Serving Scheduler Baselines: Simulation Performance Outcomes for Scheduler Policies
This is a comprehensive, text-free, highly structured simulation results dataset for large language model (LLM) serving schedulers. It contains policy-level outcome records generated across synthetic scheduler stress tests and an added TraceLab-derived out-of-distribution policy sweep. The dataset compares 12 highly optimized third-party baseline schedulers against APT-Serve (a… See the full description on the dataset page: https://huggingface.co/datasets/SoroushVahidi/llm-serving-scheduler-baselines.toxicchat_output-Ukrllm-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.hiring-bias-mitigation-synthetic-data
Hiring-bias mitigation — synthetic training data
Semi-synthetic data for training LLMs to make hiring decisions that do not depend on a
protected attribute (military status, gender, religion), in English and Ukrainian.
Real inputs, synthetic labels. CVs and job descriptions are real, anonymised postings
from the Djinni Recruitment Dataset (MIT). Decisions and rationales were written by the
teacher model Qwen/Qwen3.5-122B-A10B-GPTQ-Int4.
Code and results:… See the full description on the dataset page: https://huggingface.co/datasets/Stereotypes-in-LLMs/hiring-bias-mitigation-synthetic-data.tts_llmsr_data
tts_llmsr_data
LLMs-First-Task
Super easy task for humans that All SOTA LLM fail to retrieve the correct answer from context. Including SOTA models: GPT5, Grok4, DeepSeek, Gemini 2.5PRO, Mistral, Llama4...etc
Update: Accepted to COLM 2026 (San Francisco).
AAAI 2026 Worshop Oral: Jan/2026 LaMAS (LLM-based Multi-Agent Systems: Towards Responsible, Reliable, and Scalable Agentic Systems) Jan/2026 Singapole
ICML 2025 Long-Context Foundation Models Workshop Accepted.(https://arxiv.org/abs/2506.08184)
Update: This dataset… See the full description on the dataset page: https://huggingface.co/datasets/giantfish-fly/LLMs-First-Task.astro_paper_corpusworld_values_survey_2017_2022_sftKorean-YouTube-Comment-Sentiment-Dataset
Korean YouTube Comment Sentiment Dataset
Data Overview
Summary
본 데이터셋은 유튜브에서 수집된 한국어 댓글 5,482개와 이에 대응하는 감정 레이블(긍정, 부정, 중립, 불명확)로 구성된 감정 분류용 데이터셋입니다.
주요 레이블: 긍정, 부정, 중립, 불명확
Features
수집 대상: 요리, 뷰티, 게임, 여행, 쇼핑 등 분야의 10만 명 이상 구독자를 보유한 유튜브 채널
형식: JSON (id, text, label)
검수: 한국인 검수자에 의한 수작업 라벨링 및 교차 검토
본 데이터셋은 구어체, 이모지, 줄임말 등 실제 사용자 표현이 반영되어 있습니다.
Dataset Structure
Dataset Fields
Field
Type
Description
id
string
각 댓글의… See the full description on the dataset page: https://huggingface.co/datasets/LLM-SocialMedia/Korean-YouTube-Comment-Sentiment-Dataset.repro-evaluating-llms-comparative-signals-traces
Agent traces
Agent sessions published from a Trackio Logbook.
fragility-moral-judgment-llms
Fragility of Moral Judgment in Large Language Models
Companion dataset for the FAccT paper Fragility of Moral Judgment in Large Language Models by Tom van Nuenen. Contains the moral dilemmas, community labels, and per-model verdicts (with explanations and reasoning traces) used in the study.
The paper investigates how stable LLM moral judgments are under minimal, morally-irrelevant perturbations of the same dilemma, and whether protocols and reasoning chains improve or worsen… See the full description on the dataset page: https://huggingface.co/datasets/ucberkeley-dlab/fragility-moral-judgment-llms.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.valkompass-2026-llms
Valkompass 2026 × LLMs
How do 50 popular large language models answer the 35
questions in SVT's Swedish election compass (Valkompass 2026, Riksdag) — and which
of the 8 Riksdag parties does each model end up closest to?
Unlike comparisons that query chat products (ChatGPT, Gemini, Claude, Grok web UIs),
which have web-search / tools and act as agents, this dataset probes the raw model
weights only, via the OpenRouter API, with no system prompt, no tools, no web access.… See the full description on the dataset page: https://huggingface.co/datasets/nordan-ai/valkompass-2026-llms.llm-strawberryLLMSReuropean_social_survey_2023_sftrepro-codetaste-can-llms-generate-human-level-code-refactorings-traces
Agent traces
Agent sessions published from a Trackio Logbook.
llms_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.