datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
political-perspectives-sft
Neutrality Political Perspectives SFT
This dataset contains independent, open-ended political and public-policy
questions plus responses produced under documented strong U.S. progressive and
strong U.S. conservative interventions. It supports controlled research on
perspective-conditioned supervised fine-tuning. It is not a source of political
or factual ground truth.
The repository preserves the original 1,000-question release as v1, adds a
separate, substantially larger v2… See the full description on the dataset page: https://huggingface.co/datasets/TheNeutralityProject/political-perspectives-sft.perspectivegap
Bud Ecosystem mirror of sun1245/PerspectiveGap — a verbatim copy for offline, reproducible model evaluation. License unchanged (MIT); all rights remain with the original authors.
Dataset Card for PerspectiveGap
PerspectiveGap is a benchmark for evaluating LLMs' ability to compose orchestration prompts for multi-agent systems.
It tests whether a model can decide what each sub-agent in a multi-agent workflow needs to know, without leaking irrelevant context.
Paper:… See the full description on the dataset page: https://huggingface.co/datasets/budecosystem/perspectivegap.PerspectiveGap
Dataset Card for PerspectiveGap
PerspectiveGap is a benchmark for evaluating LLMs' ability to compose orchestration prompts for multi-agent systems.
It tests whether a model can decide what each sub-agent in a multi-agent workflow needs to know, without leaking irrelevant context.
Paper: PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting
Code and scorers: WhymustIhaveaname/PerspectiveGap
Interactive leaderboard: sun1245/PerspectiveGap-Leaderboard
Project… See the full description on the dataset page: https://huggingface.co/datasets/sun1245/PerspectiveGap.seshat-perspectiveSeshat-perspective is the first historical databank synthetically annotated with a perspectivist approach by means of multiple Large Language Models: Deepseek (dr1), Llama (l31l) and Mistral (m3m)
Paper with field description and validation procedure: https://github.com/facells/fabio-celli-publications/blob/main/docs/2026_perspective_seshat_clicit26.pdf
Code for replication: https://colab.research.google.com/drive/1_4aUNGjl7_uhLZZKE7mAYHPhWYUZ9jvr?usp=sharing