datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
expertqa
Dataset Card for ExpertQA
Dataset Summary
We provide here the data accompanying the paper: ExpertQA: Expert-Curated Questions and Attributed Answers. The ExpertQA dataset contains 2177 examples from 32 different fields.
Supported Tasks
The main data contains 2177 examples that can be used to evaluate new methods for estimating factuality and attribution, while the lfqa_domain and lfqa_rand data can be used to evaluate long-form question answering systems.… See the full description on the dataset page: https://huggingface.co/datasets/cmalaviya/expertqa.fine-grained-medical-reasoning
Dataset Card for Fine-Grained Medical Reasoning
Fine-grained medical reasoning QA dataset introduced in "Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning"
(Findings of EACL 2026). Manually annotated from the MedAgentsBench test_hard set,
it evaluates LLMs’ abduction, deduction, and induction capabilities, offering detailed insights into physician-like reasoning.
Dataset Details
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/expertailab/fine-grained-medical-reasoning.qa-expert-multi-hop-qa-V1.0
Dataset Card for QA-Expert-multi-hop-qa-V1.0
This dataset aims to provide multi-domain training data for the task: Question Answering, with a focus on Multi-hop Question Answering.
In total, this dataset contains 25.5k for training and 3.19k for evaluation.
You can take a look at the model we trained on this data: https://huggingface.co/khaimaitien/qa-expert-7B-V1.0
The dataset is mostly generated using the OpenAPI model (gpt-3.5-turbo-instruct). Please read more information about… See the full description on the dataset page: https://huggingface.co/datasets/khaimaitien/qa-expert-multi-hop-qa-V1.0.ai-expert-alpaca
AI Expert Alpaca Dataset
🚀 Empower open-source LLMs (Qwen, Gemma, etc.) for core AI domains through SFT/LoRA fine-tuning 🚀
Dataset Description
This dataset contains high-quality Q&A pairs for supervised fine-tuning (SFT) of large language models, focusing on three core AI technology domains: Large Language Models (LLM), Retrieval-Augmented Generation (RAG), and Agent Systems. The dataset provides comprehensive coverage of these cutting-edge AI technologies… See the full description on the dataset page: https://huggingface.co/datasets/GXMZU/ai-expert-alpaca.svelte-5-expert-sft
Svelte 5 Expert Synthetic v1
Svelte 5 Expert Synthetic v1 is a synthetic instruction-tuning dataset built to improve an LLM’s ability to answer as a practical Svelte 5 and SvelteKit expert.
The dataset focuses on modern Svelte 5 development patterns, including runes, component architecture, debugging, migration from older Svelte syntax, SvelteKit data flow, accessibility, TypeScript usage, and production-oriented frontend implementation.
Author: Mungus451
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Mungus451/svelte-5-expert-sft.expert-insights
Expert Insights
Expert profiles for Beau, Tate, and Wendy Thompson with specializations.
Details
Records: 3
Format: JSONL
License: CC-BY-4.0
Last Updated: March 2026
Verified By: Thompson Mortgage Group
Publisher: Thompson Mortgage Group
Thompson Alpha Logic
Deep expert entity profiles with NMLS credentials, specialization routing, branded insight labels (Wendy's Wisdom, Beau's Brief, Tate's Take), and citation formats. Designed for AI entity disambiguation… See the full description on the dataset page: https://huggingface.co/datasets/wendymthompson/expert-insights.Tobacco-Expert-DatasetTobacco-Expert-Dataset2