SNIFFER: Multimodal Large Language Model for Out-of-Context Misinformation Detection

核心概念

Multimodal large language model SNIFFER detects and explains out-of-context misinformation effectively.

要約

SNIFFER is a novel multimodal large language model designed for detecting out-of-context misinformation. It employs two-stage instruction tuning on InstructBLIP, integrating external tools and retrieval methods. The model surpasses state-of-the-art methods in detection accuracy and provides accurate explanations validated by quantitative and human evaluations. Experiments show that SNIFFER can detect misinformation early with limited training data and generalize well across different datasets.

統計

No metrics or figures provided in the content.

引用

No striking quotes found in the content.

抽出されたキーインサイト

SNIFFER

by Peng Qi,Zeho... 場所 arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.03170.pdf

深掘り質問

How does SNIFFER handle inconsistencies between text and images?

SNIFFER addresses inconsistencies between text and images by employing a multi-perspective approach. It first conducts internal checking to analyze the image-text pairs for discrepancies. Additionally, it leverages external tools for retrieving web evidence to verify the context of the image in relation to the provided text. By integrating both internal and external verification methods, SNIFFER can effectively identify inconsistencies that may indicate out-of-context misinformation.

What are the implications of using external tools for contextual verification in misinformation detection?

The use of external tools for contextual verification in misinformation detection has significant implications. By incorporating retrieved web evidence, models like SNIFFER can enhance their analysis by considering additional information beyond the immediate image-text pair. This allows for a more comprehensive evaluation of whether an image is being used appropriately within a given news context. External tools provide valuable insights that help validate or invalidate claims made in captions, thereby improving the accuracy and reliability of misinformation detection systems.

How can SNIFFER's explainability contribute to building trust in debunking misinformation?

SNIFFER's explainability plays a crucial role in building trust when debunking misinformation. By providing clear and detailed explanations alongside its judgments, SNIFFER offers transparency into its decision-making process. Users can understand why certain content is flagged as misleading or incorrect, which fosters credibility and confidence in the system's capabilities. The precise and persuasive explanations generated by SNIFFER not only aid users in discerning fake news but also educate them on how to identify similar instances independently. This transparency ultimately strengthens public trust in debunking efforts against misinformation.