Generating Faithful and Salient Text from Multimodal Data

TL;DR

This paper introduces a framework that enhances large multimodal models by reducing hallucinations and improving the saliency of generated text through a vision critic that identifies and emphasizes important visual features.

Contribution

The paper presents a novel approach using a vision critic to detect hallucinated and salient features, improving faithfulness and saliency in multimodal text generation.

Findings

Improved faithfulness and saliency in generated text.

Outperformed recent hallucination reduction techniques.

Effective on multiple datasets.

Abstract

While large multimodal models (LMMs) have obtained strong performance on many multimodal tasks, they may still hallucinate while generating text. Their performance on detecting salient features from visual data is also unclear. In this paper, we develop a framework to generate faithful and salient text from mixed-modal data, which includes images and structured data ( represented in knowledge graphs or tables). Specifically, we train a small vision critic model to identify hallucinated and non-salient features from the image modality. The critic model also generates a list of salient image features. This information is used in the post editing step to improve the generation quality. Experiments on two datasets show that our framework improves LMMs' generation quality on both faithfulness and saliency, outperforming recent techniques aimed at reducing hallucination.