datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llm/or-bench.pypi-20241031crates-20250307npm-20241031rubygems-20241031npm-20240828crates-20240903benchmark-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.Bitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.llm_global_opinions
Dataset Card for GlobalOpinionQA
Dataset Summary
The data contains a subset of survey questions about global issues and opinions adapted from the World Values Survey and Pew Global Attitudes Survey.
The data is further described in the paper: Towards Measuring the Representation of Subjective Global Opinions in Language Models.
Purpose
In our paper, we use this dataset to analyze the opinions that large language models (LLMs) reflect on complex global… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/llm_global_opinions.Bitext-events-ticketing-llm-chatbot-training-dataset
Bitext - Events and Ticketing Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [events and ticketing] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset.LLM-Artifacts
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
Debarati Das†¶, Karin de Langis¶, Anna Martin-Boyle¶, Jaehyung Kim¶, Minhwa Lee¶, Zae Myung Kim¶
Shirley Anugrah Hayati, Risako Owan, Bin Hu, Ritik Sachin Parkar, Ryan Koo,
Jong Inn Park, Aahan Tyagi, Libby Ferland, Sanjali Roy, Vincent Liu
Dongyeop Kang
Minnesota NLP, University of Minnesota Twin Cities
† Project Lead,
¶ Core Contribution,
Arxiv
Project Page
📌 Table of Contents
Introduction… See the full description on the dataset page: https://huggingface.co/datasets/minnesotanlp/LLM-Artifacts.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llms/or-bench.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our leaderboard at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue… See the full description on the dataset page: https://huggingface.co/datasets/orbench-llm/or-bench.local-llm-benchmark
Local LLM Benchmark — Technical and Uncensored Behavior (NVIDIA RTX 5070 Ti 16GB)
English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Português | తెలుగు | Français | Deutsch | Italiano | Tiếng Việt | العربية | اردو | বাংলা | فارسی | Română | Türkçe
Manual evaluation results of local GGUF model variants on a single consumer machine,
combining two fully independent benchmarks:
technical/
uncensored/
Measures
capability: coding, systems, networking, DB, agents… See the full description on the dataset page: https://huggingface.co/datasets/nanimani/local-llm-benchmark.w2t-llm-arc-easy-lora
W2T Llm Arc Easy Lora
This repository contains artifacts for the W2T paper:
Paper: W2T: LoRA Weights Already Know What They Can Do
Repo: Weight2Token
Summary
ARC-Easy LoRA checkpoints and prepared metadata used for performance prediction.
Source Status
Storage location: local
Verification status: confirmed
Files
See manifest.json for the exact local or remote source paths used to prepare this release.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/Xiaolong-Han/w2t-llm-arc-easy-lora.llm-latency-tracker
LLM Latency Tracker
Independent, continuously measured latency and availability for AI inference API
providers, aggregated by day. Covers 46 providers across
4 regions (ap-tokyo, eu-hetzner, sa-east, us-central), built from
3,878,804 raw probes collected between 2026-07-23 and
2026-10-05.
Live rankings and full methodology: llmlatency.dev
How the numbers are produced
Probes run every five minutes from separate network locations and are never routed
through a… See the full description on the dataset page: https://huggingface.co/datasets/llmlatency/llm-latency-tracker.llm-cost-same-prompt
Measured per-call LLM cost — same prompt, every model
Vendors publish prices per million tokens. Nobody publishes what one call actually costs, because
that depends on how many tokens the model chooses to emit — and on the same question models differ by
more than an order of magnitude. One model finishes a JSON extraction in 23 tokens; another writes 300.
This dataset sends a fixed set of prompts to every model at temperature 0, every night, and records
the cost computed from… See the full description on the dataset page: https://huggingface.co/datasets/mario0369/llm-cost-same-prompt.snac_llm_parler_ttsBitext-telco-llm-chatbot-training-dataset
Bitext - Telco Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [telco] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset.LLM-Ads
LLM-Ads — Sponsored-recommendation evaluation traces
Per-trial responses and labels from the experiments in
Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored
Recommendations in Twelve LLMs
(arXiv:2605.12772).
The data set reproduces and extends the evaluation of Wu et al.\ 2026
(arXiv:2604.08525) on a twelve-model
pool (ten open-source chat models served through an OpenAI-compatible
API endpoint plus the two paper-overlap OpenAI models
gpt-3.5-turbo and gpt-4o).… See the full description on the dataset page: https://huggingface.co/datasets/akmaier/LLM-Ads.jgpqa
JGPQA
This repository provides GPQA dataset translated from English into Japanese by LLM-jp, a collaborative project launched in Japan.
The dataset was translated from English to Japanese using machine translation, then checked and corrected by external experts.
The links of the original GPQA dataset are here(HuggingFace).
Send Questions to
llm-jp(at)nii.ac.jp
Model Card Authors
Yuji Tamakoshi, Kouta Nakayama, Yusuke Miyao.
DEBATE_LLM
DEBATE Benchmark
This repository contains CSV files from the DEBATE project: large-scale
human conversation experiments organized around controversial and
opinion-based topics. The data consists of multi-round conversations
between human participants discussing political, social, and belief-related
topics, following the protocol described in:
Chuang, Y.-S., Tu, R., Dai, C., Vasani, S., Li, Y., Yao, B., Tessler, M. H., Yang, S., Shah, D., Hawkins, R., Hu, J., & Rogers, T. T. (2026).… See the full description on the dataset page: https://huggingface.co/datasets/seantw/DEBATE_LLM.Bitext-insurance-llm-chatbot-training-dataset
Bitext - Insurance Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [insurance] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset.or-bench-toxic-all
OR-Bench: An Over-Refusal Benchmark for Large Language Models
This dataset constains highly toxic prompts, use with caution!!!
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic… See the full description on the dataset page: https://huggingface.co/datasets/bench-llms/or-bench-toxic-all.linalg-bench-llm
LinAlg-Bench: Where LLMs Stop Computing and Start Hallucinating
Ten frontier LLMs drop from near-perfect to near-zero on 5×5 eigenvalue problems. Complete computational collapse is dimension-gated: rare at 3×3, dominant at 4×4 and 5×5. Failures dissociate cleanly by task — eigenvalues fail by constraint-aware fabrication (invented eigenvalues that still match the matrix trace), determinants by sign-accumulation drift. Nearly a third of irrational-spectrum eigenvalue failures are… See the full description on the dataset page: https://huggingface.co/datasets/mst-ai/linalg-bench-llm.multimodal-LLMs-See-Sentiment
MLLMsent — datasets and experiment results
Every input and every output of "Multimodal LLMs See Sentiment"
(arXiv:2508.16873): the image descriptions generated by six multimodal
LLMs, the sentiment labels derived from the PerceptSent annotations, and the complete
per-fold results of all 141 experiments.
Paper: arXiv:2508.16873
Code, training and inference: https://github.com/neemiasbsilva/multimodal-LLMs-see-sentiment
Model checkpoints:… See the full description on the dataset page: https://huggingface.co/datasets/neemiasbsilva/multimodal-LLMs-See-Sentiment.LLMFineTuningBench
Dataset Card for LLMFineTuningBench
A dataset of over 30,000 LLM fine-tuning experiments, capturing detailed performance metrics from jobs run on high-performance computing (HPC) clusters. It spans a wide range of models, fine-tuning methods, and hardware configurations, and is intended to support research on predictive resource allocation, performance optimization, and cost estimation for LLM fine-tuning workloads.
Dataset Details
Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/LLMFineTuningBench.rl_llm_experiment_p6