Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding (2024)

Abstract: Large Vision-Language Models (LVLMs) are increasingly adept at generating contextually detailed and coherent responses from visual inputs. However, their application in multimodal decision-making and open-ended generation is hindered by a notable rate of hallucinations, where generated text inaccurately represents the visual contents. To address this issue, this paper introduces the Instruction Contrastive Decoding (ICD) method, a novel approach designed to reduce hallucinations during LVLM inference. Our method is inspired by our observation that what we call disturbance instructions significantly exacerbate hallucinations in multimodal fusion modules. ICD contrasts distributions from standard and instruction disturbance, thereby increasing alignment uncertainty and effectively subtracting hallucinated concepts from the original distribution. Through comprehensive experiments on discriminative benchmarks (POPE and MME) and a generative benchmark (LLaVa-Bench), we demonstrate that ICD significantly mitigates both object-level and attribute-level hallucinations. Moreover, our method not only addresses hallucinations but also significantly enhances the general perception and recognition capabilities of LVLMs.

## introduction recent research in large vision-language models (lvlms) @xcite has seen remarkable progress, benefiting from the integration of advanced large language models (llms) @xcite known for their robust language generation and zero-shot transfer capabilities @xcite . in order to leverage off-theshell llms, it is crucial to facilitate cross-modal alignment. llava @xcite employs a linear projection approach, while blip-2 @xcite and instructblip @xcite narrow the modality gap using a q-former. although lvlms have shown promising outcomes, the issue of hallucination remains. this phenomenon occurs when the generated textual content, despite being fluent and coherent, does not accurately reflect the factual visual content. the object hallucination was initially explored within the realm of image captioning @xcite . as lvlms harness the sophisticated understanding and generative prowess of llms, the scope of hallucination extends beyond mere object existence. it now encompasses more complex elements such as attributes and relationships within the generated content. consequently, distinguishing discriminative hallucination and the nonhallucinatory portion in the generation has become pivotal in assessing the performance of lvlms in terms of their fidelity to factual visual information. the intertwined nature of modalities presents significant challenges in identifying the root causes of hallucinations in lvlms. research efforts have begun to uncover the primary contributors to lvlm hallucinations, including statistical biases @xcite encountered during the training process and excessive dependence on language priors @xcite . additionally, multimodal misalignment has been identified as a key factor in the occurrence of hallucinations @xcite @xcite . to address dataset bias, annotation enrichment techniques @xcite @xcite have been introduced. furthermore, to counteract the influence of language priors, post-processing strategies @xcite have been developed, along with comprehensive initiatives aimed at improving multimodal alignment through optimizing alignment with humans @xcite . while these interventions have proven to be effective in reducing hallucinations, they demand sub-stantial human involvement and incur significant computational costs for additional training or the integration of supplementary modules. in this work, we reveal that appending instructions with role prefixes to form disturbance instructions can significantly exacerbate hallucinations. we hypothesize that identifying and subsequently detaching hallucination concepts from the original distribution could effectively reduce such hallucinations. motivated by this insight, we introduce the instruction contrastive decoding (icd) method. this approach is novel in that it is training-free and agnostic to the underlying lvlms. icd differentiates between two distributions: one from the original instruction and another from the disturbance instruction within the multimodal alignment module. utilizing their difference, we aim at suppressing hallucinations. through comprehensive experiments on discrimination hallucination benchmarks such as pope @xcite and mme hallucination sets @xcite , as well as the generation hallucination benchmark llava-bench @xcite , our method incorporating state-of-the-art lvlms like minigpt4 and instructblip, demonstrates significant efficacy in mitigating hallucinations at both object and attribute levels. furthermore, our approach consistently enhances performance across 14 general perception and recognition tasks within the full mme benchmark. our main contributions are as follows: hallucination in vlms. generation hallucinations have been extensively studied in the field of language modeling @xcite . hallucination in vlms manifests as detailed, fluent, and coherent responses that inaccurately reflect the visual context, including erroneous objects, attributes, and relations @xcite . various strategies have been proposed to curb hallucinations. annotation enrichment techniques like m-haldetect @xcite and grit @xcite , as well as approaches such as hacl @xcite and llava-rlhl @xcite , seek to improve alignment with human instructions through additional annotations. similarly, woodpecker @xcite introduces post-processing aimed at mitigating biases from language priors. while these methods have shown promise in reducing hallucinations, they often require extensive data annotation, fine-tuning, and supplementary modules, complicating their implementation. in contrast, our method directly addresses hallucinations during inference. additionally, @xcite introduced a visual contrastive decoding (vcd) approach that contrasts with the distributions of distorted visual inputs, a concept that bears resemblance to our method. however, our icd method suppresses hallucinations through disturbance instructions affecting multimodal alignment. ## experiment in this section, we explore the evaluation of our icd method for mitigating hallucinations. our examination is twofold: firstly, through the lens of hallucination discrimination, and secondly, via the generation of non-hallucinatory content. more precisely, we assess the efficacy of icd in alleviating object-level hallucination symptoms utilizing the pope benchmark. furthermore, we extend our analysis to include both object and attribute-level symptoms through the mme benchmark. finally, the performance of our method in generating nonhallucinatory content is evaluated using the llava-bench dataset. ## conclusion we introduce a novel instruction contrastive decoding approach that effectively detaches hallucinatory concepts by contrasting distributions derived from standard and disturbance instructions where role prefixes are appended to amplify hallucinations. comprehensive experiments across various benchmarks and different lvlms demonstrate the capability of our method in mitigating hallucinations and substantially improving the general perception and recognition performance of lvlms.