datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
A-Survey-for-LLM-Agent-Trajectory-Analysis
A Survey for LLM Agent Trajectory Analysis
This dataset repository hosts the survey paper A Survey for LLM Agent Trajectory Analysis: From Failure Attribution to Enhancement and a structured metadata snapshot of the companion paper collection from Awesome-LLM-Agent-Trajectory-Analysis.
The repository is intended for discovery, citation, and lightweight analysis of the literature around LLM agent trajectory analysis, including failure attribution, trajectory-based debugging… See the full description on the dataset page: https://huggingface.co/datasets/RobinChen2001/A-Survey-for-LLM-Agent-Trajectory-Analysis.harvey-labs-llm-artifact-analysis
Harvey Labs LLM artifact analysis
This dataset contains artifacts from a non-LLM analysis of the Harvey Labs DOCX corpus.
The analysis used filename similarity, document extraction heuristics, a small manually
labeled seed set, CatBoost native text features, and a native CatBoost embedding feature
built from a mean Word2Vec representation. It was designed to find documents where an
LLM refused the requested task and returned a safe alternative instead.
Source… See the full description on the dataset page: https://huggingface.co/datasets/Hanno-Labs/harvey-labs-llm-artifact-analysis.llm-ideology-analysisThis dataset contains evaluations of political figures by a diverse set of Large Language Models (LLMs), such that the ideology of these LLMs can be characterized.
📝 Dataset Description
The dataset contains responses from 19 different Large Language Models evaluating 3,991 political figures, with responses collected in the six UN languages: Arabic, Chinese, English, French, Russian, and Spanish.
The evaluations were conducted using a two-stage prompting strategy to assess the… See the full description on the dataset page: https://huggingface.co/datasets/aida-ugent/llm-ideology-analysis.analysis-oracle-verifierllm-ideology-analysis
Dataset Card for LLM Ideology Dataset
This dataset contains evaluations of political figures by various Large Language Models (LLMs), designed to analyze ideological biases in AI language models.
Dataset Details
Dataset Description
The dataset contains responses from 17 different Large Language Models evaluating 4,339 political figures, with responses collected in both English and Chinese. The evaluations were conducted using a two-stage prompting strategy to… See the full description on the dataset page: https://huggingface.co/datasets/ajrogier/llm-ideology-analysis.llm-error-analysis
LLM Error Analysis Dataset
This dataset contains evaluation examples for testing mathematical and logical reasoning capabilities of language models.
Dataset Overview
Number of examples: 11
Fields: input, type, expected_output, model_output
Types included: arithmetic, long_addition, date_knowledge, ambiguous_riddle, ordering, directional_reasoning, pattern_recognition, instruction_following, framing_bias, causal_vs_correlation
Model Tested
Model:… See the full description on the dataset page: https://huggingface.co/datasets/ParamTh/llm-error-analysis.llm-error-analysis-dataset
Model Evaluation and Fine-Tuning Recommendation
For this experiment, I used the Nanbeige4-3B-Base model from Hugging Face and performed prompt testing across multiple domains.
Model Setup
The following code was used to load the model and generate responses:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "Nanbeige/Nanbeige4-3B-Base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(… See the full description on the dataset page: https://huggingface.co/datasets/AnindaRoy/llm-error-analysis-dataset.llm-dataset-with-analysis
Contact Information
Dataset Maintainer
Name: Othmane Moutaouakkil
Email: othmoutaouakkil@gmail.com
GitHub: @moutaouakkil
LinkedIn: Othmane Moutaouakkil
How to Reach Me
Feel free to contact me with any questions about this dataset, including:
Usage inquiries
Bug reports
Collaboration opportunities
Feature requests
I typically respond within 1-2 business days.
sentiment_analysis_v1.0-non-toxicllm-compression-analysisseoul-urban-park-analysis-by-llmThis dataset contains analysis results of 29,866 urban park images. The analysis is done by GPT-4.
The elements for analysis are as follows:
"elements": All physical elements visible in the photo, and the proportion of each element in the photo (summing up to 1). For each type of element (e.g., structures, ground, objects), provide a descriptive analysis, stating them by specific pronouns. For example, you should not state a building just as a building, but more precisely like a pavilion or… See the full description on the dataset page: https://huggingface.co/datasets/RedBall/seoul-urban-park-analysis-by-llm.sentiment_analysis_v1.0llm-evaluation-analysisllm-evaluation-analysis-split