datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
llm-srbench
LLM-SRBench: Benchmark for Scientific Equation Discovery with LLMs
We introduce LLM-SRBench, a comprehensive benchmark with 239 challenging problems across four scientific domains specifically designed to evaluate LLM-based scientific equation discovery methods while preventing trivial memorization.
Our benchmark comprises two main categories: LSR-Transform, which transforms common physical models into less common mathematical representations to test reasoning beyond memorization… See the full description on the dataset page: https://huggingface.co/datasets/nnheui/llm-srbench.hiring-bias-mitigation-responses
Hiring-bias mitigation — model responses
Every response produced in the mitigation study of LLM hiring decisions: 61 runs,
2,689,200 responses, from 5 open-weight models in English and Ukrainian, at
baseline and under each mitigation family (baseline, embedding, prompt, scrub, sft). 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.Global-LLMs-Replies
Global LLMs Replies
GPT-4o
-> 74,644 rows
mixtral-8x22b
-> 13,129 rows
claude-3-haiku
-> 3,871 rows
llmsql-2.0-fine-tune-ready
LLMSQL Benchmark 2.0 (Finetune-Ready)
This benchmark is designed to evaluate text-to-SQL models. For usage of this benchmark see llmsql-bench/llmsql-2.0.
This repository contains a finetune-ready version of the LLMSQL benchmark: LLMSQL 2.0 on Hugging Face.
The dataset is structured in a messages format suitable for instruction-tuned models, where each example has a messages field. This field is a list of dictionaries with:
"role": "user" — the input question or prompt
"role":… See the full description on the dataset page: https://huggingface.co/datasets/llmsql-bench/llmsql-2.0-fine-tune-ready.llmsplit_deepseekllm-similarity-riskjailbreak-detection-dataset
Jailbreak Detection Dataset (MLCommons-Aligned)
A comprehensive dataset for training AI safety classifiers, aligned with the MLCommons AI Safety taxonomy.
Dataset Description
This dataset combines multiple sources for robust jailbreak and safety detection:
Primary Sources
nvidia/Aegis-AI-Content-Safety-Dataset-2.0: 18,164 samples with MLCommons-aligned labels
lmsys/toxic-chat: Toxic content detection
jackhhao/jailbreak-classification: Jailbreak attack patterns… See the full description on the dataset page: https://huggingface.co/datasets/llm-semantic-router/jailbreak-detection-dataset.llmsplit_4o_mini_decontextualizedllmsys-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.llmsplit_deepseek_decontextualizedMaliciousInstructllms.txt
llms.txt files extracted from the Common Crawl corpus
This dataset contains llms.txt files extracted from the Common Crawl corpus.
Specifically, we extracted response records matching */llms.txt and */llms-full.txt with status 200 and MIME-type text/plain or text/markdown.
What is llms.txt?
The /llms.txt file: A proposal to standardise on using an /llms.txt file to provide information to help LLMs use a website at inference time.
Our own analysis of this dataset is available… See the full description on the dataset page: https://huggingface.co/datasets/commoncrawl/llms.txt.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.fact-check-classification-dataset
Fact-Check Classification Dataset
🎯 Purpose: Binary classification dataset for determining whether a prompt needs external fact-checking.
Dataset Description
This dataset is designed to train classifiers that can route LLM requests based on whether they require external fact verification. It's part of the vLLM Semantic Router project.
Labels
FACT_CHECK_NEEDED (1): Information-seeking questions requiring external verification
Factual questions about dates… See the full description on the dataset page: https://huggingface.co/datasets/llm-semantic-router/fact-check-classification-dataset.llm-srbench
LLM-SRBench: Benchmark for Scientific Equation Discovery with LLMs
This dataset contains LLM-SRBench, a comprehensive benchmark for evaluating Large Language Models (LLMs) on scientific equation discovery (symbolic regression) tasks.
Paper: LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models (ICML 2025 Oral)
Original Repository: deep-symbolic-mathematics/llm-srbench
Original Dataset: nnheui/llm-srbench
📊 Dataset Overview… See the full description on the dataset page: https://huggingface.co/datasets/pkuHaowei/llm-srbench.llm-security-leaderboard-contentsllms-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.alvarobartt-improving-text-embeddings-with-llms-full
Dataset Card for alvarobartt-improving-text-embeddings-with-llms-full
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/alvarobartt-improving-text-embeddings-with-llms-full/raw/main/pipeline.yaml"
or explore the… See the full description on the dataset page: https://huggingface.co/datasets/distilabel-internal-testing/alvarobartt-improving-text-embeddings-with-llms-full.llm-safety-instruct-v7longcontext-haldetect
Long-Context Hallucination Detection Benchmark
A synthetic benchmark dataset for evaluating hallucination detection models on long documents (8K-24K tokens). This dataset is specifically designed to test models that can handle contexts beyond the typical 8K token limit.
Dataset Summary
Property
Value
Total samples
3,366
Token range
8,005 - 23,998
Average tokens
17,852
Hallucinated
1,681 (49.9%)
Supported
1,685 (50.1%)
Splits… See the full description on the dataset page: https://huggingface.co/datasets/llm-semantic-router/longcontext-haldetect.feedback-detector-dataset
Feedback Detector Dataset
A large-scale multilingual dataset for 4-class user feedback classification, labeled using GPT-OSS-120B on AMD MI300X GPU.
Dataset Description
This dataset contains 51,694 examples of user feedback classified into 4 categories:
Label
Description
Count
%
SAT
User is satisfied
8,649
17%
NEED_CLARIFICATION
User needs more information
16,179
31%
WRONG_ANSWER
System gave incorrect response
19,919
39%
WANT_DIFFERENT
User wants… See the full description on the dataset page: https://huggingface.co/datasets/llm-semantic-router/feedback-detector-dataset.GigaVerbo-filteredllm-sast-v1
LLM-SAST v1
A high-quality, audited training dataset for fine-tuning small-to-mid-size language models to perform static application security testing (SAST) on infrastructure-as-code and application code — replacing rule-based scanners (Checkov, Trivy, Semgrep, KICS, Bearer, …) rather than auditing their output.
Task: given a single source file, the model emits a structured list of security findings (line ranges, category, severity, reasoning, remediation). No SAST-tool input. No… See the full description on the dataset page: https://huggingface.co/datasets/aioutfitters/llm-sast-v1.llms-txt
Context & Motivation
https://llmstxt.org/ is a project from Answer.AI which proposes to "standardise on using an /llms.txt file to provide information to help LLMs use a website at inference time."
I've noticed many tool providers begin to offer /llms.txt files for their websites and documentation. This includes developer tools and platforms like Perplexity, Anthropic, Hugging Face, Vercel, and others.
I've also come across https://directory.llmstxt.cloud/, a directory of… See the full description on the dataset page: https://huggingface.co/datasets/megrisdal/llms-txt.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.hiring-analyses-second_model_verification-enllms-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.toxicchat_output-Ukrllm-security-en
LLM Security & Prompt Injection Dataset (EN)
Comprehensive English dataset on Large Language Model (LLM) security, covering attack techniques, defense patterns, OWASP LLM Top 10 (2025) and AI Act compliance.
Description
This dataset provides a structured knowledge base in English for training, fine-tuning and awareness on the security of LLM-based applications. It covers the full landscape of threats and defenses for generative AI systems.
Dataset Content… See the full description on the dataset page: https://huggingface.co/datasets/AYI-NEDJIMI/llm-security-en.SV-TrustEval-C-1.0
SV‑TrustEval‑C 🚨🔒
🔍 Overview
SV‑TrustEval‑C is the first reasoning‑based benchmark designed to rigorously evaluate Large Language Models (LLMs) on both structure (control/data flow) and semantic reasoning for vulnerability analysis in C source code. Unlike existing benchmarks that focus solely on pattern recognition, SV‑TrustEval‑C measures logical consistency, adaptability to code transformations, and real‑world security reasoning across six core tasks.
Our… See the full description on the dataset page: https://huggingface.co/datasets/LLMs4CodeSecurity/SV-TrustEval-C-1.0.