HalluQA
by OpenMOSS
Dataset and evaluation script for "Evaluating Hallucinations in Chinese Large Language Models"
AI summary
Hallucination detector
An evaluation framework for assessing the performance of large language models on question-answering tasks with hallucination detection
- stars
- 111
- forks
- 4
- watching
- 5
Similar projects
Found by comparing what the projects do, not just their names.
Hallucination detector
A framework to detect and mitigate hallucinations in multimodal large language models
Hallucination detector
A framework for detecting hallucinations in large language models
Object detector
An evaluation framework for detecting object hallucinations in vision-language models
Hallucination Detector
Automates fine-grained hallucination detection in large language model outputs
Hallucination fixer
A framework to improve large language model performance by mitigating hallucination effects through data and optimization techniques.
Hallucination detection and mitigation
A unified framework and benchmark for detecting and mitigating hallucinations in open-vocabulary image captioning models
Hallucination tester
Evaluates and mitigates hallucinations in multimodal large language models
Hallucination corrector
A method to correct hallucinations in multimodal large language models without requiring retraining
Hallucination fixer
An implementation of a method to mitigate hallucinations in large language models using visual re-tracing
Object detection method
An approach to reduce object hallucinations in large vision-language models by contrasting output distributions derived from original and distorted visual inputs
Data processing framework
This project provides tools and frameworks to mitigate hallucinatory toxicity in visual instruction data, allowing researchers to fine-tune MLLM models on specific datasets.
Hallucination mitigation
A research project focused on mitigating hallucinations in large multi-modal models by improving instruction tuning through robust training methods.
Hallucination detector
Evaluates answers generated by large vision-language models to assess hallucinations
Benchmark
An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy
Image model flaw detection
An investigation into the relationship between misleading images and hallucinations in large language models