Latest Articles for Hallucinations

Understanding and mitigating hallucination in AI for reliable performance.

Advancements in AI models enhance accuracy in medical image interpretation.

MASSIVE-AMR dataset enhances multilingual understanding in AI systems.

A new dataset analyzes misleading information in LLM responses.

Research explores ways to improve trust in LLM outputs through factuality and sourcing.

This study assesses the reliability of AI tools in legal practice.

A new method to edit language models effectively while maintaining performance.

This article examines how negation affects large language models and their accuracy.

A new approach enhances accuracy and creativity in language model outputs.

This article explains the phenomenon of hallucinations in image generation models.

New dataset helps assess AI text accuracy and reliability.

New techniques aim to fix errors in language models without complete retraining.

New benchmark assesses how video-language models handle inaccuracies effectively.

A new framework aims to improve accuracy in semantic parsing models.

A new method to detect and correct inaccuracies in language models.

Enhancing pharmacovigilance through reliable language model outputs.

This study assesses how medical LVLMs perform amidst hallucinations using a new dataset.

A new framework aims to detect and fix errors in LVLM outputs.

This study examines how LLMs evaluate their own knowledge and risk of errors.

A tool to identify misleading answers from large language models.

TongGu simplifies the understanding of Classical Chinese with specialized techniques.

A new method generates synthetic data to improve detection of false outputs.

This study evaluates how well large models handle multiple objects in images.

Research focuses on improving accuracy and reliability of language models.

GenSco enhances QA systems by improving multi-hop question answering accuracy and coherence.

A new method to assess accuracy in language model outputs.

An overview of NLG progress, challenges, and future research directions.

This paper studies how training influences the predictions of large language models.

A critique-based model improves accuracy in spotting inaccuracies in AI-generated text.

Research highlights methods to detect false information in automotive AI.

A new benchmark sheds light on hallucination in vision language models.

Generative AI is improving how data professionals write SQL queries.

A new dataset enhances the accuracy of event factuality detection in texts.

Introducing DOPRA, a cost-effective way to improve MLLM accuracy.

This article evaluates web agents' effectiveness in managing complex online tasks.

HaloQuest addresses hallucination issues in vision-language models with a new dataset.

pRAGe helps simplify medical terms for better patient understanding.

This article discusses challenges in detecting hallucinations in machine translation across various languages.

This article presents a method to improve context understanding in language models.

A new benchmark evaluates LLMs for factual accuracy.