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.

heading: 1 Introduction
text: Recent research in large vision-language models (LVLMs) (Liu et al., 2023c,b; Li et al., 2023a) has seen remarkable progress, benefiting from the integration of advanced large language models (LLMs) (Achiam et al., 2023; Touvron et al., 2023a,b) known for their robust language generation and zero-shot transfer capabilities (Zhong et al., 2023; Peng et al., 2023). In order to leverage off-theshell LLMs, it is crucial to facilitate cross-modal alignment. LLaVa (Liu et al., 2023c) employs a linear projection approach, while BLIP-2 (Li et al.,
∗Corresponding Authors.
2023a) and InstructBLIP (Liu et al., 2023b) 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 (Rohrbach et al., 2018). 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 (You et al., 2023) encountered during the training process and excessive dependence on language priors (Zhu et al., 2020; Zhibo et al., 2023). Additionally, multimodal misalignment has been identified as a key factor in the occurrence of hallucinations (Jiang et al., 2023; Liu et al., 2023a; Wang et al., 2023a). To address dataset bias, annotation enrichment techniques (Gunjal et al., 2024; You et al., 2023; Zhai et al., 2023) have been introduced. Furthermore, to counteract the influence of language priors, post-processing strategies (Yin et al., 2023; Zhou et al., 2023) have been developed, along with comprehensive initiatives aimed at improving multimodal alignment through optimizing alignment with humans (Sun et al., 2023; Jiang et al., 2023). While these interventions have proven to be effective in reducing hallucinations, they demand sub-
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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 (Li et al., 2023c) and MME hallucination sets (Fu et al., 2023), as well as the generation hallucination benchmark LLaVa-Bench (Liu et al., 2023c), 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:
• We perform an in-depth analysis of how disturbance in instructions exacerbates hallucinations. This phenomenon is elucidated through statistical bias and language priors, offering a nuanced understanding of underlying causes.
• Drawing on these insights above, we introduce the ICD method. This novel strategy, which emphasizes initial highlight followed by de-emphasize of hallucination, effectively mitigates hallucinations during inference, by adjusting the distributions away from hallucinations that we elicit.
• Through extensive experimentation and analysis, we validate the effectiveness of our proposed ICD method across both discrimination and generation hallucination benchmarks, showcasing its robustness and versatility in enhancing LVLMs performance.

heading: 2 Related Work
text: Large Vision-Language Models. The field of vision-language pre-training (VLP) (Radford et al., 2021; Li et al., 2022; Bao et al., 2022; Wang et al., 2023b) and fine-tuning (Wang et al., 2023c; Wiehe et al., 2022; Alayrac et al., 2022) have seen rapid advancements, propelled by the evolution of large language models (LLMs). As a result, large visionlanguage models (LVLMs) have emerged, leveraging the strengths of frozen LLMs while emphasizing the facilitating of multimodal alignment modules. Notably, models such as LLaVa and QwenVL (Bai et al., 2023) adopt simple linear projections to achieve alignment, contrasting with BLIP-2 and miniGPT4 (Zhu et al., 2023), which introduce a Q-Former. In further work, InstructBLIP integrates task-aware instructions, enriching the understanding of task-aware visual semantics. Our research builds upon these advancements in LVLMs, focusing on the impact of instruction disturbances. We explore how such disturbances increase the uncertainty in multimodal alignment, significantly contributing to the exacerbation of hallucinations.
Hallucination in VLMs. Generation hallucinations have been extensively studied in the field of language modeling (Tonmoy et al., 2024; Wen et al., 2024). Hallucination in VLMs manifests as detailed, fluent, and coherent responses that inaccurately reflect the visual context, including erroneous objects, attributes, and relations (Liu et al., 2024; Jing et al., 2023). Various strategies have been proposed to curb hallucinations. Annotation enrichment techniques like M-HalDetect (Gunjal et al., 2024) and GRIT (You et al., 2023), as well as approaches such as HACL (Jiang et al., 2023) and LLaVA-RLHL (Liu et al., 2023a), seek to improve alignment with human instructions through additional annotations. Similarly, Woodpecker (Yin et al., 2023) 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, Leng et al. (2023) 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.

heading: 3 Method

heading: 3.1 Inference in LVLMs
text: Large Vision-Language Models (LVLMs) are comprised of three pivotal components: a visual encoder, a fusion module, and a language model. For processing an input image, a pre-trained visual encoder, such as ViT-L/14 from CLIP (Radford et al., 2021), is employed to extract visual features, denoted as XV. The fusion module facilitates multimodal alignment. For instance, InstructBLIP introduces an instruction-aware querying transformer. Q-Former, a lightweight transformer architecture, utilizes K learnable query vectors QK to refine the extraction of visual features, thereby enhancing multimodal alignment. It allows the instruction Xins to interact with the query vectors, fostering the extraction of task-relevant image features:
ZV = Qθ(XV , QK , Xins), (1)
where, ZV = Qθ(·) represents the fused visual features, conditioned on the instructions. Given its sophistication and effectiveness in multimodal alignment, we advocate for the adoption of the instruction-aware Q-Former architecture.
For text queries Xq, a large language model, parameterized by ϕ, such as Vicuna (Chiang et al., 2023), processes the query, leveraging the derived visual features to formulate responses:
YR = LLMϕ(HV , Xins), (2)
where HV = g(ZV ) is the transformation ensuring the same dimensionality as the word embedding of the language model. By default, the instruction is the same as text query for both Q-Former and LLM as Xins = Xq.
Mathematically, in the decoding phase, the response R can be defined as a sequence of length L, sampled from a probability distribution:
p(YR|XV , Xq) = L∏
t=1
pϕ(yt|HV , Xq, y<t), (3)
where y<t represents the sequence of generated tokens up to the time steps (t − 1). In the decoding phase of LVLMs, hallucinations often emerge when probable tokens lack grounding in the visual context. Jiang et al. (2023); Liu et al. (2023a) indicate that multimodal misalignment is a critical
factor contributing to the generation of hallucinations. Thus, we conduct an in-depth analysis of the fusion module, specifically focusing on multimodal alignment. Our work first demonstrates that instructions within the multimodal alignment module can exacerbate hallucinations. To address this, we introduce instruction disturbance and propose an instruction contrastive decoding method, employing a highlight and then detach strategy.

heading: 3.2 Instruction Can Amplify Hallucination
text: Prior studies have attributed the occurrence of hallucinations in LVLMs to statistical biases within multimodal training datasets (You et al., 2023) and an over-reliance on language priors (Zhu et al., 2020; Zhibo et al., 2023). Extending this line of observation, we introduce the concept of instruction disturbance in this section. A prefix appended to instructions affects multimodal alignment, thereby exacerbating statistical biases and the over-reliance on language priors.
Introduction of instruction disturbance: We introduce the concept of instruction disturbance, which entails appending a role prefix to the original instructions delineated in Section 3.1. This disturbance aims to modulate the multimodal alignment uncertainty within LVLMs. As illustrated in Figure 1, the base instruction “Describe this photo in detail” is combined with learned query vectors in the Q-Former. To implement instruction disturbance, we append either positive or negative prefixes to the base instruction. Positive prefixes aim to increase the LVLM’s confidence in multimodal alignment. Conversely, negative prefixes are designed to reduce the model’s alignment confidence.
Xins = { [Xd, Xq] if disturbance Xq otherwise , (4)
where Xd denotes the role prefix, and Xq represents the original instruction. Through this method, we strategically influence the LVLM’s confidence level in multimodal alignment by either encouraging a more definitive understanding or introducing ambiguity.
Instruction disturbance amplifies statistical biases and language priors: Figure 1 presents the response from InstructBLIP, revealing that the LVLMs generate hallucinated tokens such as “fork and person.” To further explore this phenomenon, we undertake two specific analyses: the frequent hallucinated object occurrence and the
co-occurrence of object hallucinations. Our study utilizes MSCOCO validation set (Lin et al., 2014), a common dataset for LVLM pre-training, to perform hallucination detection across three distinct scenarios: the baseline LVLM, LVLM with a positive disturbance, and LVLM with a negative disturbance. Our analysis focuses on calculating the hallucination ratio, specifically identifying instances where the hallucinated objects are absent from the provided images.
Figure 2 demonstrates that introducing instruction disturbance significantly amplifies the occurrence of hallucinations. Under the influence of negative disturbance, LVLMs are more likely to hallucinate objects that frequently co-occur, such as “person and dining table,” and show an increased tendency to hallucinate objects that typically cooccur with those actually present in the image, for example, “fork and person.” This suggests that instruction disturbances, whether positive or negative,
intensify the hallucination effect, exacerbating the issues of imbalanced object distribution and correlation patterns inherent in the training dataset.

heading: 3.3 Instruction Contrastive Decoding

heading: 3.3.1 Contrastive Decoding with Disturbance
text: Our analysis reveals that instruction disturbances exacerbate hallucinations by increasing multimodal alignment uncertainty. This uncertainty predisposes LVLMs to more readily adopt biased cooccurrence concepts from pretraining datasets, as reflected in the learned query vectors. As these hallucinations accumulate, LVLMs increasingly over-rely on language priors. Notably, disturbances involving negative prefixes significantly intensify these hallucinations. We hypothesize that by initially emphasizing the probabilities of hallucinated concepts and subsequently detaching these from the original probability distribution, hallucinations may be reduced. Inspired by this insight, we introduce an Instruction Contrastive Decoding method (ICD) aimed at mitigating hallucinations during LVLM inference.
Motivated by the language contrastive decoding (Sennrich et al., 2024) in reducing hallucinations within machine translation frameworks—where it prevents potentially accurate translations that, however, deviate from the desired target language—we adopt a similar approach to our model. Given the extraction of visual features XV from the visual encoder and a textual query Xq, our model calcu-
lates two distinct token distributions: one conditioned on the original instructions, and the other on instructions with disturbance Xd as Equation 4. Contrary to the conventional approach of selecting the token that maximizes the probability, our strategy involves choosing the token that concurrently maximizes pϕ(yt|XV , Xins) and minimizes pϕ(yt|XV , X ′ins), the latter representing the probability of tokens that are more likely to be hallucinations. To adjust the balance between these probabilities, we introduce a hyperparameter λ, which regulates the intensity of the contrastive penalty. Formally, this process is described as follows:
picd(YR|XV , Xq) = L∏
t=1
( pϕ(yt|XV , Xins, y<t)
− λpϕ(yt|XV , X ′ins, y<t) ) , (5)
where larger λ indicates a more decisive penalty on the decision made by LVLMs with disturbances.

heading: 3.3.2 Adaptive Plausibility Constrains
text: The ICD objective is designed to favor tokens preferred by the LVLM output while imposing penalties on tokens influenced by instruction disturbances. However, this approach might inadvertently penalize accurate predictions—those tokens that, under both standard and disturbance instruction conditions, are confidently identified and are well-grounded in the visual context (such as objects, verbs, attributes, and relations) due to their simplicity and high likelihood. Conversely, it might erroneously reward tokens representing implausible concepts. To address this issue, we draw inspiration from adaptive plausibility constraints utilized in open-ended text generation (Li et al., 2023b). Consequently, we refine the ICD objective to incorporate an adaptive plausibility constraint:
yt ∼ softmax ( logitϕ(yt|XV , Xins, y<t)
− λlogitϕ(yt|XV , X ′ins, y<t) )
subject to yt ∈ Vhead(y<t) (6)
Vhead(y<t) = { yt ∈ V : pϕ(yt|XV , Xins, y<t)
≥ α max token
pϕ(token|XV , Xins, y<t) } ,
(7)
here, α acts as a pivotal hyperparameter that modulates the truncation of the probability distribution,
effectively tailoring the LVLM’s response to its confidence level. This is particularly crucial for mitigating the influence of implausible tokens, especially when LVLMs exhibit high confidence and are accurately anchored in visual semantics.
ICD serves as a self-corrective mechanism, which successfully identifies hallucinations in LVLMs and then de-emphasizes them through contrastive decoding. Moreover, the integration of adaptive plausibility constraints further hones the contrastive distribution by considering the confidence levels of LVLMs, thereby narrowing the decision-making process to a more reliable candidate pool. This method not only significantly reduces hallucinations within LVLMs but also curtails the generation of implausible tokens, showcasing the efficacy of our proposed method in enhancing model reliability and output validity.

heading: 4 Experiment
text: 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 LLaVaBench dataset.

heading: 4.1 Experimental Settings

heading: 4.1.1 Datasets and Evaluation Metrics
text: POPE: The Polling-based Object Probing Evaluation (POPE) stands as a popular benchmark in discerning hallucination at the object level. POPE employs a binary question-answering format, inquiring LVLMs to determine the presence or absence of a specified object within a given image. This benchmark is structured around three distinct subsets—MSCOCO, A-OKVQA (Schwenk et al., 2022), and GQA (Hudson and Manning, 2019)—each comprising 500 images alongside six questions per image. POPE introduces three settings within each subset: random (selecting absent objects at random), popular (choosing the most frequently occurring objects in the dataset as absent), and adversarial (selecting absent objects that of-
ten co-occur with ground-truth objects). We adopt Accuracy, Precision, Recall, and F1 score as the evaluation metrics.
MME: MME benchmark serves as a comprehensive tool for assessing the capabilities of LVLMs across both perception and cognition, spanning a total of 14 tasks. Among these, tasks focusing on existence, count, position, and color are specifically designed as hallucination discrimination benchmarks. These tasks aim to scrutinize both object-level and attribute-level hallucination symptoms. MME similarly utilizes a question-answering format to facilitate this evaluation. Consequently, task scores are reported as the evaluation metric for measuring performance.
LLaVa-Bench: The LLaVa-Bench is designed to quantify the extent of hallucinated content produced during the open-ended generation tasks performed by LVLMs. This benchmark encompasses a varied collection of 24 images, accompanied by
60 questions that cover a wide range of scenarios, including indoor and outdoor scenes, memes, paintings, and sketches. Unlike discriminative benchmarks, where accuracy serves as the evaluation metric, generative benchmarks, such as this, currently do not have well-established metrics specifically devised for the detailed analysis of hallucinations (Liu et al., 2024). Therefore, we utilize case studies on this dataset as a means to qualitatively evaluate the effectiveness of our ICD method (see in appendix B).

heading: 4.1.2 LVLM Baselines
text: We employ two state-of-the-art LVLMs as backbone frameworks. Specifically, we implement our ICD on InstructBLIP and miniGPT4, which utilize the Vicuna 7B as their underlying LLM and the sophisticated Q-Former architecture for fusion modules, respectively. Additionally, we explore the use of LLaVa-1.5 (Liu et al., 2023b), which incorpo-
rates linear projection for its fusion module alongside InstructBLIP, to identify optimal practices in applying the ICD method (see in appendix E). Finally, we compare our method against the visual contrastive decoding approach (Leng et al., 2023), designed to mitigate hallucinations arising from visual uncertainties. We posit that our method, being LVLM-agnostic, can be conveniently integrated into various off-the-shelf LVLMs.

heading: 4.2 Experimental Results

heading: 4.2.1 Results on POPE
text: The experimental results on POPE, summarized in Table 1, demonstrate the efficacy of our instruction contrastive decoding method across three distinct subsets within the POPE benchmark—MSCOCO, A-OKVQA, and GQA settings. Notably, our ICD method consistently outperforms the foundational LVLMs, miniGPT4, and InstructBLIP. Specifically, the ICD method exceeds the performance of miniGPT4 and InstructBLIP, showing a substantial improvement of 10.5% and 6.0%, respectively, across all metrics (7.0% in accuracy, 8.5% in precision, 8.7% in recall, and 7.9% in F1 score for both models). This significant enhancement as per four metrics on POPE underscores the effectiveness of our highlight and then detach strategy.
Furthermore, the progressive movement from random to popular and then to adversarial settings reveals a marked decline in performance, highlighting the growing impact of statistical biases and language prior to contributing to hallucinations in LVLMs. Despite these challenges, our ICD method consistently demonstrates improvements across all settings, affirming our hypothesis that disturbance instruction exacerbates hallucinations by influencing multimodal alignment, thereby deepening er-
rors rooted in statistical bias and over-reliance on language priors, which can be subtracted by contrastive decoding. Our method effectively mitigates these issues and object-level hallucinations.
In comparison to the VCD approach, our ICD method achieves an overall improvement of 3.9%. While the VCD method aims to ensure that the output distributions are closely aligned with visual inputs and compares distributions derived from distorted images, it requires additional processing to distort images via diffusion models (Ho and Salimans, 2022) and is sensitive to the choice of hyperparameters in its experimental setup (Leng et al., 2023). Conversely, our ICD method offers a more straightforward and efficient solution, yielding superior results in an end-to-end manner.

heading: 4.2.2 Results on MME
text: Results on MME Hallucination Subset: The analysis of the POPE benchmark underscores the efficacy of our ICD method in mitigating object-level hallucination symptoms. Given that hallucinations can also manifest at the attribute level (Liu et al., 2024), it becomes imperative to extend our investigation to these dimensions. To this end, we leverage the MME hallucination subset, which encompasses both object-level (existence and count tasks) and attribute-level (position and color tasks) benchmarks, to conduct a comprehensive evaluation of the ICD method.
As detailed in Table 2, our ICD method significantly surpasses the baseline LVLMs and the VCD method across all four tasks, demonstrating its superior capability in suppressing both object and attribute-level hallucinations with a large margin (+84.2 and +62.5 respectively in total scores). Interestingly, while the VCD method experiences a de-
cline in performance on the position hallucination task, our method maintains robust performance. This distinction underscores the adaptability and effectiveness of the ICD method in addressing a broader spectrum of hallucination symptoms, making it a more versatile solution in LVLMs.
Results on MME Benchmark: Our method is designed to mitigate hallucinations in LVLMs during inference. We delve deeper into ascertaining whether our approach not only preserves but potentially enhances the fundamental recognition and reasoning capabilities of LVLMs. To this end, we analyze performance across the full comprehensive MME benchmark, which encompasses 14 subtasks designed to assess perception and recognition.
Figure 3 illustrates that implementing ICD with both backbone models significantly improves task scores, surpassing the performance of foundation LVLMs and established VCD method. This outcome suggests that our method not only manages hallucinations effectively during inference but also elevates the accuracy of foundational LVLM tasks.
In a more detailed model-specific analysis, our approach consistently outperforms both the backbone miniGPT4 and the VCD method with the same backbone across all 14 subtasks. Conversely, the VCD method exhibits diminished performance in specific areas such as posters, artwork, OCR, numerical calculation, text translation, and code reasoning when compared to the baseline LVLM.
Moreover, when InstructBLIP serves as the backbone, the effectiveness of VCD decreases in tasks related to existence, position, scene, and code reasoning. We surmise that while leveraging visual uncertainty may anchor predictions more firmly in visual input, it simultaneously introduces drawbacks by fostering an over-reliance on visual cues at the expense of instruction-based grounding. Conversely, our ICD method, by focusing on multimodal alignment, does not compromise the fundamental reasoning capabilities of LVLMs. Notably, our method’s performance on the landmark,
OCR, commonsense reasoning, and text translation tasks under InstructBLIP is weaker than the VCD method, whereas VCD exhibits superior results in these domains. This suggests that these subtasks within the MME benchmark may demand a robust visual discrimination capability.

heading: 4.3 Discussions on ICD and VCD
text: In addressing hallucinations in LVLMs, our ICD method and the baseline VCD both leverage contrastive decoding tailored for open-ended generation (Li et al., 2023b). While our ICD method introduces disturbance instructions to increase multimodal alignment uncertainty, VCD employs distorted images to amplify visual uncertainty. Positing that a synergistic approach could harness the strengths of both methods, we propose to analyze a straightforward integration of these two methods.
Our combined approach begins with the VCD, utilizing standard instructions. This is followed by contrasting the resulting distribution with that of a VCD output generated under disturbance instructions, thereby establishing the final output distribution. Figure 4 showcases the integration method on color, posters, landmarks, OCR, commonsense reasoning, and text translation. This approach yields notable enhancements across these subtasks, underlining the importance of discriminative visual features and multimodal alignment as complements in grounding LVLM responses.
This exploration suggests a promising avenue for future research aimed at optimally amalgamating the advantages of both methods. Detailed results and comprehensive analysis of the combined method performance across full MME are provided in the appendix D for further reference.

heading: 5 Conclusion
text: 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.
Limitations
In this paper, we have concentrated on addressing hallucinations within LVLMs by deploying our novel ICD method. We have validated its efficacy through rigorous evaluation on various hallucination discrimination benchmarks and have also qualitatively assessed its performance on generative benchmarks, which are pivotal for examining hallucinatory content. Despite their importance, generative benchmarks currently lack established metrics for thoroughly analyzing hallucinations, indicating a significant area for future research to enhance open-ended generation performance evaluation with robust automatic metrics.
Ethics Statement
We propose the Instruction Contrastive Decoding method to address hallucination issues in LVLMs, thereby enhancing their safety and reliability within the community. Additionally, the datasets utilized for inferring and evaluating the ICD method are publicly accessible, promoting transparency and reproducibility in our research. Furthermore, we have made our code available to the public, ensuring it is convenient for researchers and practitioners to access and implement.

heading: Acknowledgments
text: We thank the anonymous reviewers and the area chair for their insightful comments and suggestions. This research was funded by the German Research Foundation DFG Transregio SFB 169: Crossmodal Learning: Adaptivity, Prediction, and Interaction.

heading: B Qualitative Evaluation on LLaVa-Bench
text: In this section, we extend our analysis by focusing on the evaluation of generative hallucination. Utilizing LLaVa-Bench, we conduct a qualitative analysis on the task of open-ended generation. Figure 5 showcases two case studies that compare our method with backbone LVLMs using identical input images. The example displayed on the left presents various Asian dishes. While the baseline LVLMs accurately identify and generate concepts such as spoons, tables, and cups, they also erroneously introduce the unrelated concept of a “person.” This error stems from the high frequency of co-occurrence between “person” and "tables" in the training data. Furthermore, the example on the right depicts a well-known scene from the movie "Titanic." Here, the baseline LVLMs incorrectly perceive the characters Jack and Rose as two women, leading to an inaccurate generation of text regarding same-sex relationships. This error is a result of the language prior biases, which contribute to hallucinations in LVLMs.
Contrastingly, our ICD approach produces fluent, coherent text that is closely grounded in the visual context, effectively mitigating the hallucinations caused by statistical biases and the inherent language priors of LVLMs.

heading: C General QA Benchmarks Performance
text: Our evaluation in Section 4, utilizing the POPE, MME, and LLava-Bench benchmarks, focused on assessing hallucinations in LVLMs, particularly regarding the presence of objects and attributes within images. In this section, we broaden our evaluation scope to include more general QA and caption datasets, such as MSCOCO (Lin et al., 2014),
OK-VQA (Marino et al., 2019), and TextVQA (Singh et al., 2019), using metrics like CHAIR (Rohrbach et al., 2018), CIDEr (Vedantam et al., 2015), and BLEU (Papineni et al., 2002) for a comprehensive analysis.
Specifically, MSCOCO comprises 118,000 images in the training set and 5,000 images in the validation set. Following the evaluation settings of Rohrbach et al. (2018), we report instance-level (CHAIR_I) and sentence-level (CHAIR_S) hallucination results using the MSCOCO validation set in Table 3. Additionally, in line with Yue et al. (2024), we randomly sample 500 instances from the MSCOCO training and validation sets, with our results presented in Table 4.
We further evaluate our ICD method on the OK-VQA and TextVQA datasets using CIDEr and BLEU metrics, with the improved results detailed in Tables 5 and 6.
These additional evaluations underscore the versatility and efficacy of the ICD method across diverse QA datasets and evaluation metrics. Notably, the improvements in metrics such as CHAIR,
CIDEr, and BLEU across the MSCOCO, OK-VQA, and TextVQA benchmarks reaffirm the significant impact of our method.

heading: D Further Analysis on VCD-Enhanced ICD
text: We comprehensively analyze the ICD and VCD combined method, detailed in Section 4.3, within the full MME benchmark, utilizing InstructBLIP as the backbone LVLM. Figure 6 illustrates that integrating our ICD method significantly enhances the VCD’s performance across various tasks, including existence, count, color, celebrity, scene, landmark, and artwork. Similarly, incorporating VCD in ICD yields improvements in color, posters, landmarks, OCR, commonsense reasoning, and translation tasks. These findings suggest that addressing both visual and multimodal alignment uncertainties in a complementary fashion effectively mitigates hallucinations. However, we also note a performance decrement in the ICD method for count, position, artwork, calculation, and code reasoning tasks when combined with VCD. This observation underscores the necessity for more refined combination strategies to fully harness the potential of
integrating these two methods. Combining the strengths of both the ICD and VCD methods has opened a promising avenue for future investigations. We aim to develop and refine contrastive decoding methods for the seamless integration of both techniques, potentially a new method for mitigating hallucinations in LVLMs.

heading: E Optimal Position to Apply Contrastive Decoding
text: Upon detailed examination of the inference framework depicted in Figure 1, we identify three potential points for integrating the ICD method: within the Q-Former’s instruction, the LLM’s instruction, and a combination of both. This analysis, based on the POPE (GQA random setting), aims to pinpoint the optimal implementation site for ICD. To ensure a comprehensive comparison, we selected two distinct LVLMs, InstructBLIP and LLaVa, as backbones to represent varied fusion approaches. InstructBLIP employs Q-Former for multimodal alignment, whereas LLaVa utilizes a linear projection.
Figure 7 reveals that, under the InstructBLIP framework, ICD enhances performance across all implementation sites, with the singular application within Q-Former yielding the most significant improvement. A comparison between the LVLMs indicates that LLaVa also benefits from the ICD method when ICD is applied within LLMs. However, exclusive application of ICD in LLMs produces less pronounced improvements, mirroring
the observations with InstructBLIP as the backbone. Consequently, our findings suggest that deploying the ICD method within the Q-Former architecture represents the most effective strategy.

heading: Future Work
text: Combining the strengths of both the ICD and VCD methods has opened a promising avenue for future investigations. We aim to develop and refine contrastive decoding methods for the seamless integration of both techniques, potentially a new method for mitigating hallucinations in LVLMs