Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model

TL;DR

This paper introduces an input-level ensemble preprocessing framework that adaptively filters visual inputs to reduce hallucinations in multimodal language models, significantly improving factual accuracy without altering the model.

Contribution

The study proposes a novel adaptive preprocessing approach that selects optimal filtering techniques based on question type to mitigate hallucinations in multimodal LLMs.

Findings

44.3% reduction in hallucination rates on HaloQuest dataset

Improved factual grounding without modifying model architecture

Adaptive input filtering enhances multimodal reasoning accuracy

Abstract

Visual hallucinations in Large Language Models (LLMs), where the model generates responses that are inconsistent with the visual input, pose a significant challenge to their reliability, particularly in contexts where precise and trustworthy outputs are critical. Current research largely emphasizes post-hoc correction or model-specific fine-tuning strategies, with limited exploration of preprocessing techniques to address hallucination issues at the input stage. This study presents a novel ensemble-based preprocessing framework that adaptively selects the most appropriate filtering approach -- noise reduced (NR), edge enhanced (EE), or unaltered input (org) based on the type of question posed, resulting into reduced hallucination without requiring any modifications to the underlying model architecture or training pipeline. Evaluated on the `HaloQuest' dataset -- a benchmark designed to test multimodal reasoning on visually complex inputs, our method achieves a 44.3% reduction in hallucination rates, as measured by Natural Language Inference (NLI) scores using SelfCheckGPT. This demonstrates that intelligent input conditioning alone can significantly enhance factual grounding in LLM responses. The findings highlight the importance of adaptive preprocessing techniques in mitigating hallucinations, paving the way for more reliable multimodal systems capable of addressing real-world challenges.