Latest Articles for Hallucinations

Evaluating large language models for enhancing road safety in self-driving cars.

Two methods enhance how models analyze medical images for better diagnosis.

A new framework aims to reduce hallucinations in LVLMs through active retrieval.

A framework to reduce false outputs in language-vision models across multiple languages.

A new framework enhances evaluation of RAG systems in specialized domains.

Exploring AI tutors' role in enhancing robotics education through advanced techniques.

A study on the challenges and solutions for hallucination in MLLMs.

New method improves accuracy in vision-language models, reducing misleading content.

A new method improves accuracy in advanced AI models by addressing hallucinations.

Exploring LLMs for identifying anomalies in time series data.

A new method enhances the accuracy of financial report generation using language models.

Study reveals effective methods to identify hallucinations in large vision-language models.

This article examines issues of code hallucination in LLMs and their implications.

A study on hallucinations in language models and their training implications.

New tools enhance surgical training by utilizing video and text data.

This study examines how planning aids in reducing factual errors in text generation.

A method to enhance accuracy in language models by detecting hallucinations.

ConVis aims to minimize inaccuracies in multimodal large language models.

A new framework improves answer accuracy in AI models by focusing on evidence.

Introducing LRP4RAG, a method to better detect hallucinations in language models.

A new framework enhances image captioning accuracy and reduces errors.

A new method for detecting hallucinations in language models using corrupted data.

A look at the complexities and improvements in speech-to-speech translation technology.

A new method to assess uncertainty in language model outputs for greater reliability.

A new method addresses the challenge of detecting inaccuracies in AI-generated text.

A new model improves how researchers manage citations and scholarly articles.

A new framework enhances text descriptions using images and structured data.

A new approach to reduce inaccuracies in language models using skepticism.

Examining the risks and challenges of AI technology in medical applications.

Study reveals how language models utilize context for accurate responses.

Examining AI hallucinations and critical thinking in human interactions.

A new framework enhances accuracy and reduces errors in medical text generation.

THaMES offers a framework to reduce hallucinations in language models.

New methods aim to reduce inaccuracies in language models within information retrieval systems.

This research tests a tool to improve accuracy of traffic-based language models.

A new method improves detection of inaccuracies in language models.

SLaVA-CXR improves chest X-ray report generation for better clinical efficiency.

PACU framework enhances VLLMs by refining prompts and utilizing image captions.

Examining the accuracy issues in large language models and their societal effects.

A new framework improves detection of false outputs in language models using unlabeled data.