datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
synthetic_polistance
Fully Synthetic Prompts for LLM Political Stance Detection
All resources developed in the article "Templated or fully Synthetic? Prompt construction as a confound in measuring LLM political stance beyond writing assistance" (Chalkidis, 2026).
Paper Abstract
Political stance detection in LLMs has long been dominated by closed-ended, multiple-choice political survey questions—originally designed for humans, and thus lacks the realism and nuance of human-AI… See the full description on the dataset page: https://huggingface.co/datasets/kiddothe2b/synthetic_polistance.gemma-2b-cameroon-cultural-blindspots
Gemma-2b Cameroon Cultural Blindspots
This dataset highlights the "blind spots" of the Google Gemma-2-2b base model regarding Cameroonian culture, geography, and local languages.
1. Model Tested
Model Name: google/gemma-2-2b
Type: Base Model (Pre-trained)
2. Loading Procedure
The model was loaded using the transformers library on a Google Colab T4 GPU:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "google/gemma-2-2b"… See the full description on the dataset page: https://huggingface.co/datasets/zox-BT/gemma-2b-cameroon-cultural-blindspots.qwen3.5-2b-base-blind-spots
Qwen3.5-2B-Base Blind Spots Dataset
Overview
This dataset documents 10 blind spots (incorrect predictions) found in the
Qwen3.5-2B-Base model — a 2B parameter
base (pre-trained only) causal language model released by the Qwen team.
The errors span diverse categories including common sense reasoning, spatial reasoning,
negation understanding, character/word counting, science, geography, and logic.
Model Tested
Model: Qwen/Qwen3.5-2B-Base
Type: Causal… See the full description on the dataset page: https://huggingface.co/datasets/wilchesch/qwen3.5-2b-base-blind-spots.qwen2b-blindspots
Qwen3.5-2B Blindspots Dataset
Overview
This dataset contains examples where the base language model Qwen3.5-2B-Base produces incorrect or unexpected outputs. The goal of this dataset is to identify blind spots in small frontier language models by testing them on tasks involving reasoning, counting, symbolic manipulation, and strict instruction following.
The dataset records:
The input prompt
The expected output
The actual model output
The error category
These examples… See the full description on the dataset page: https://huggingface.co/datasets/Sriyanshsh1805/qwen2b-blindspots.qwen35-2b-base-blind-spots
Blind Spots of Qwen3.5-2B-Base
This dataset documents incorrect predictions from Qwen/Qwen3.5-2B-Base, a 2.21B-parameter pre-trained base model released February 2026.
How the Model Was Loaded
Loaded in Google Colab (free T4 GPU) with Hugging Face Transformers:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_ID = "Qwen/Qwen3.5-2B-Base"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model =… See the full description on the dataset page: https://huggingface.co/datasets/Becky4382/qwen35-2b-base-blind-spots.