Venue: NAACL 2025

# Evaluating and Mitigating Object Hallucination in Large Vision-Language

Models: Can They Still See Removed Objects?

 Yixiao He, Haifeng Sun, Pengfei Ren, Jingyu Wang, Huazheng Wang,

**Qi Qi, Zirui Zhuang\({}^{\dagger}\), Jing Wang\({}^{\dagger}\)**

State Key Laboratory of Networking and Switching Technology

Beijing University of Posts and Telecommunications

{heyixiao,hfsun,rpf,wangjingwu,wanghz,qiqi8266}@bupt.edu.cn {zhuangzirui,wangjing}@bupt.edu.cn

Corresponding Author.

###### Abstract

Large Vision-Language Models (LVLMs) have a significant issue with object hallucinations, where researchers have noted that LVLMs often mistakenly determine objects as present in images where they do not actually exist. Some recent studies evaluate the occurrence of object hallucinations by asking LVLMs whether they see objects that do not exist in input images. However, we observe that these evaluation methods have some limitations, such as the objects being questioned potentially having little relevance to the image. In this paper, we introduce a more challenging benchmark for evaluating object hallucinations by removing objects from images and then asking the model whether it can still see the removed objects. Our evaluation result reveals that LVLMs suffer from severe hallucinations, as they often still claim to see the removed objects. Through our analysis, we find that biases in training result in LVLMs lacking guidance on learning about the absence of objects, which in turn leads to a lack of ability to determine that objects do not exist in images. To address this issue, we further propose oDPO, a direct preference optimization objective based on visual objects. By guiding LVLMs to learn to determine the existence of objects, oDPO effectively alleviates object hallucinations. It achieves more competitive results than other hallucination mitigation approaches across multiple object hallucination benchmarks and enhances the performance of LVLMs in various vision-language tasks.

## 1 Introduction

With the advancement of Large Language Models (LLMs) [35, 40] and the emergence of powerful pre-trained vision-language models [7, 9, 26, 24], several Large Vision-Language Models (LVLMs) have achieved remarkable performance in vision-language tasks such as visual question answering and image captioning [15, 16, 17, 18, 19]. Despite their impressive performance across various tasks, LVLMs still suffer from severe _object hallucination_ issue, which impedes their ability to describe image information at the object level, greatly reducing the reliability of their responses [14, 15, 16].

Recent studies convert the evaluation of object hallucination into a binary discrimination task [15, 16, 17, 18]. These studies typically design visual questions about objects (e.g., _"Is there a cat in the image?"_) and prompt LVLMs to provide correct answers (_"yes" or "no"_). However, we observed that the non-existent objects they choose for questioning may not be significantly relevant to the image, thereby failing to reveal object hallucinations in LVLMs. As illustrated on the left side of Figure 1, the non-existent _horse_ being questioned is not relevant to the tennis-playing scene, making it easy for the model to correctly determine

Figure 1: Comparison between existing evaluation benchmarks and ROHE. This example is taken from Fu et al. (2023). The non-existent objects sampled by existing benchmarks may lack challenge for LVLMs and fail to evaluate object hallucination. In contrast, ROHE reveals potential object hallucination by removing existent object in the image.



the absence of a _horse_. Additionally, a _"yes"_ response from LVLMs does not necessarily indicate the absence of hallucinations. As illustrated on the right side of Figure 1, even when the _sports ball_ is removed from the image, the LVLM still responds with _"yes."_ This suggests that the LVLM exhibits hallucinations concerning the _sports ball_, which existing methods have overlooked.

To uncover object hallucinations that existing methods have neglected and to provide guidance for mitigating them, we introduce **ROHE** (**R**emoved **O**bject **H**allucination **E**valuation benchmark). As shown on the right side of Figure 1, ROHE utilizes LaMa Suvorov et al. (2022) to remove existent objects from images. These modified images typically retain other objects and visual backgrounds closely associated with the removed objects, providing a highly challenging test of LVLMs' ability to determine object existence. Furthermore, ROHE considers the model free of hallucinations only if it correctly determines that the object exists in the original image and does not exist in the modified image. This approach uncovers the hallucinations that have been overlooked due to LVLMs' tendency to answer _"yes"_Li et al. (2023c); Zhou et al. (2024b); Leng et al. (2024).

To ensure the quality of the evaluation, we manually selected the constructed data. Specifically, ROHE comprises 5,504 high-quality evaluation examples (examples in Appendix A), effectively assessing LVLMs' hallucinations across different object categories. We evaluated several representative LVLMs, and the results in Table 1 indicate that LVLMs experience significant hallucinations when confronted with removed objects. Although these LVLMs effectively determine object exists in the image, they struggle to determine the absence of the same object after it has been removed.

In addition to addressing this, we further propose the **o**bject-based **D**irect **P**reference **O**ptimization objective (**oDPO**), a multimodal direct preference optimization (DPO) objective Rafailov et al. (2023). Unlike existing DPO approaches that construct text-only preference responses Yu et al. (2024); Li et al. (2023b); Zhou et al. (2024a); Pi et al. (2024); Sarkar et al. (2024), oOPO samples the most important object in the conversation and removes it from the image. oPPO encourages LVLMs to prefer the original image, thereby enhancing their ability to determine the absence of the removed object and reducing associated hallucinations. Extensive experiments (as shown in Figure 2) show that oDPO not only effectively reduces object hallucinations but also improves the performance of LVLMs across various vision-language tasks.

Our contributions can be summarized in three key aspects. (1) We introduce a challenging object hallucination evaluation benchmark called **ROHE** and construct the evaluation data through manual selection. (2) We evaluate several representative LVLMs, revealing the severity of object hallucinations. (3) We propose **oDPO** to mitigate object hallucinations, and experimental results demonstrate the effectiveness of our approach.

## 2 The Proposed ROHE Benchmark

In this section, we introduce ROHE (SS2.1) and the process of constructing the evaluation data (SS2.2). We then evaluate representative LVLMs using ROHE (SS2.3) and discuss the results (SS2.4).

### Overview of ROHE Description.

We devise ROHE to provide a more challenging evaluation of object hallucinations. ROHE utilizes LaMa Suvorov et al. (2022) to remove existent objects from images. We refer to the original image as the positive image and the image with the object removed as the negative image. To maintain consistency with existing methods Li et al. (2023c); Fu et al. (2023); Wang et al. (2023a), ROHE adopts a binary question-answering approach to prompt LVLMs to answer _"yes"_ or _"no"_, such as _"Is there a cat in the image?"_. For each pair of images, ROHE uses the same question to ask the LVLM whether it sees the object in the positive image or negative image. ROHE requires the LVLM to determine not only objects in the positive image (answering _"yes"_) but also their

Figure 2: Performance comparison between the model optimized by our proposed approach obDOP and the base model LLaVA-1.5-7B Liu et al. (2024c) on multiple various vision-language benchmarks. Our approach is effective in improving the performance on various tasks.

 

absence in the negative image (answering _"no"_).

Definition.Given an input image \(v\), an existent object \(o\), and the corresponding object mask \(m\), the image obtained by using LaMa Suvorov et al. (2022) to remove the object \(o\) is denoted as \(v_{ro}\). The images \(v\) and \(v_{ro}\) differ only in the content within the mask \(m\), while the content outside the mask \(m\) remains unchanged. The evaluation unit constructed by ROHE can be described as follows:

\[(\langle v,q(o),a\rangle,\langle v_{ro},q(o),a_{ro}\rangle) \tag{1}\]

where \(q(o)\) is the question about the object \(o\) based on the prompt template while \(a\) and \(a_{ro}\) represent answers to the questions when given \(v\) and \(v_{ro}\) respectively. Here, \(a\) is always _"yes"_ and \(a_{ro}\) is always _"no"_. We refer to \(\langle v,q(o),a\rangle\) as the positive VQA unit and \(\langle v_{ro},q(o),a_{ro}\rangle\) as the negative one.

Pipeline.Figure3 illustrates the ROHE pipeline. First, ROHE selects objects that are uniquely present in the image and then uses LaMa Suvorov et al. (2022) to remove these objects. Subsequently, ROHE constructs positive VQA units using images containing the objects and negative VQA units using images without the objects. Each VQA unit comprises both a positive and a negative unit concerning the same object. LVLMs are expected to respond _"yes"_ to the positive unit and _"no"_ to the negative one.

Metrics.ROHE reports two scores: _acc_ and _acc+_. The _acc_ score represents the proportion of correctly answered question in the positive VQA unit; while the _acc+_ score reflects the proportion of correctly answered question in both the positive VQA unit and negative VQA unit.

### Evaluation Data Construction

Dataset.Since most LVLMs are trained using the MSCOCO dataset Lin et al. (2014), they are expected to exhibit strong recognition capabilities for objects within MSCOCO. However, differences in data splits between the 2014 and 2017 versions of MSCOCO may lead to potential data leakage issues when using the validation set from MSCOCO 2014 for evaluation. To ensure that the evaluation is not out-of-distribution (OOD) and to prevent data leakage, we choose the MSCOCO 2017 validation set to construct our evaluation data.

Manual Selection.We discover that the ROHE evaluation data constructed using LaMa Suvorov et al. (2022) might still contain incomplete removal of visual information about the objects. To ensure a high-quality evaluation, we conducted manual filtering based on two guidelines: first, to confirm the complete removal of the object, ensuring no visual traces remain in the areas filled by LaMa; second, to verify that humans can determine the object is absent from the negative image. We selected 5,504 high-quality evaluation data units, and Figure4 provides statistics of our evaluation data.

### Evaluation Settings

We investigate object existence hallucinations in the following representative LVLMs: LLaVA-1.5 Liu et al. (2024c), LLaVA-1.6 Liu et al. (2024c), InstructBlip Dai et al. (2023), Qwen-VL-Chat Bai et al. (2023), ILaVA-MOF Tong et al. (2024), VWLMM Peng et al. (2024), Monkey-Chat Li et al. (2024), and SPHINX Lin et al. (2023). To maintain

Figure 4: Statistics of our evaluation data.

Figure 3: The pipeline of ROHE. Given an input image with ground-truth objects and their corresponding masks, ROHE sequentially removes unique objects using LaMa Suvorov et al. (2022). Then, ROHE constructs the positive and negative VQA units using images with (**w/**) and without (**w/o**) the object, respectively.

 

consistency with previous work Fu et al. (2023); Li et al. (2023c), we also use _"Is there a/an [obj] in the image?"_ as the prompt template. We leave more details in Appendix B.

### Evaluation Results

Table 1 presents the evaluation results, indicating that while LVLMs effectively determine the presence of objects in positive VQA units, most of them fail to determine the absence of the same objects in negative VQA units. Instead, they still to claim that they see those objects.

The overall results show that LVLMs with enhanced visual resolution performed better, suggesting that inputting more detailed visual tokens provides these models with sufficient fine-grained visual information, aiding them in better learning and perceiving visual objects. In contrast, Instruction BLIP Dai et al. (2023) achieved the lowest \(acc+\) score, possibly due to the limited visual information extracted by Q-Former Li et al. (2023a), which restricts the language model's access to sufficient object-level visual details. Additionally, LVLMs exhibit more hallucinations for categories such as outdoor and appliance, while showing fewer hallucinations for categories like person and animal.

Overall, the evaluated LVLMs still exhibit significant object hallucinations when confronted with removed objects, suggesting that they significantly lack the ability to determine the absence of objects. We observe that during training, LVLMs are instructed to learn what objects are present in an image, but there is considerably less focus on learning what objects are absent. This imbalance likely leads to their inability to determine the absence of objects, thereby resulting in severe hallucinations.

## 3 The Proposed oDPO Approach

To address the issue revealed in SS2.4, we propose oDPO, an object-based DPO objective designed to enhance LVLM' ability to determine the existence of objects, thereby mitigating hallucinations.

### Preliminaries

Preference optimization aims to align the model's behavior with human behavior through fine-tuning. Typically, given a text input \(x\), an image input \(v\), and an output text response \(y\), a model \(\pi_{\theta}\) parameterized by \(\theta\) can produce a conditional distribution \(\pi_{\theta}(y~{}|~{}x,v)\). The model is encouraged to maximize

\begin{table}
\begin{tabular}{r r l c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \multirow{6}{*}{vehicle} & acc+ & 46.84 & 34.39 & 64.13 & 64.87 & **74.91** & 22.30 & 11.90 & 61.34 & 35.69 & 44.24 & 74.16 & 57.06 & 69.33 \\  & acc & 99.81 & **100.0** & 99.44 & 98.33 & 95.54 & 95.54 & 98.70 & 94.61 & 99.63 & **100.0** & 87.36 & 97.03 & 97.40 \\  & acc+ & 25.51 & 8.73 & 42.97 & 33.29 & 47.75 & 4.37 & 1.36 & 35.74 & 9.96 & 19.65 & **56.34** & 11.05 & 36.56 \\  & acc & 99.86 & 99.86 & 99.45 & 99.18 & 98.23 & 99.32 & 99.86 & 97.68 & 99.86 & **100.0** & 93.45 & 99.59 & 99.18 \\  & acc+ & 16.62 & 9.14 & 45.15 & 45.98 & **62.83** & 31.30 & 9.42 & 34.63 & 11.91 & 91.4 & 54.02 & 21.88 & 43.21 \\  & acc & **100.0** & **100.0** & 96.12 & 96.40 & 89.20 & 86.43 & 95.57 & 93.63 & **100.0** & **100.0** & 91.97 & 99.45 & 95.84 \\ animal & acc+ & 74.57 & 58.19 & 88.26 & 82.15 & 88.26 & 40.34 & 27.63 & 75.55 & 49.88 & 72.13 & **91.20** & 63.57 & 84.84 \\  & acc & **100.0** & 99.51 & 98.95 & 93.96 & 99.84 & 99.27 & 98.04 & 99.22 & 99.76 & 97.31 & 99.02 & 99.51 \\ food & acc & 53.39 & 36.20 & 69.23 & 70.14 & **75.57** & 36.65 & 16.29 & 68.78 & 37.10 & 45.70 & 75.11 & 48.87 & 57.47 \\  & acc & 99.55 & **100.0** & 98.19 & 95.48 & 95.48 & 95.49 & 95.02 & 99.55 & 94.12 & 99.10 & 99.55 & 90.95 & 97.29 & 97.74 \\  & acc+ & 22.79 & 15.81 & 55.88 & 53.70 & 70.59 & 15.44 & 1.47 & 45.22 & 19.49 & 12.13 & **73.53** & 31.62 & 56.62 \\ outdoor & acc+ & **100.0** & **100.0** & 97.43 & **100.0** & 95.22 & 95.22 & 100.0 & 95.96 & **100.0** & **100.0** & 89.71 & 98.16 & 99.63 \\ kitchen & acc+ & 30.26 & 18.57 & 49.12 & 48.68 & **64.77** & 27.49 & 4.97 & 41.81 & 18.13 & 25.15 & 54.68 & 35.53 & 50.00 \\  & acc & 99.42 & **100.0** & 96.93 & 97.66 & 91.67 & 90.94 & 99.56 & 83.48 & 99.12 & 99.56 & 70.61 & 97.95 & 96.78 \\  & acc+ & 26.34 & 13.28 & 49.46 & 43.47 & 62.10 & 22.91 & 5.78 & 39.40 & 13.70 & 18.20 & **66.61** & 30.84 & 46.25 \\  & acc & **100.0** & **100.0** & 98.50 & 99.14 & 97.00 & 96.57 & 98.72 & 94.65 & 99.79 & **100.0** & 83.94 & 98.72 & 98.29 \\ furniture & acc+ & 36.91 & 28.41 & 57.05 & 54.14 & **65.55** & 19.02 & 5.15 & 54.14 & 30.65 & 29.75 & 62.86 & 43.85 & 55.93 \\  & acc & 99.55 & **99.78** & 98.43 & 97.99 & 93.96 & 92.60 & 99.55 & 91.72 & **99.78** & **99.78** & 76.51 & 97.54 & 98.43 \\ indoor & acc & 28.66 & 14.95 & 51.40 & 57.63 & 61.99 & 25.32 & 7.79 & 45.48 & 15.58 & 19.31 & **65.42** & 28.97 & 46.73 \\  & acc & **100.0** & **100.0** & 97.51 & 97.82 & 95.64 & 94.08 & 99.38 & 93.46 & **100.0** & **100.0** & 85.05 & 99.07 & 97.51 \\  & acc & 12.50 & S.33 & 37.88 & 33.71 & 43.56 & 11.36 & 3.03 & 36.36 & 9.47 & 8.71 & **55.30** & 21.97 & 29.92 \\ appliance & acc+ & 100.0** & **100.0** & 98.11 & 98.86 & 97.35 & 97.35 & 99.62 & 93.56 & **100.0** & **100.0** & 76.14 & 98.48 & 98.86 \\ person & acc+ & 70.39 & 61.75 & 83.99 & 80.94 & 83.23 & 47.65 & 16.65 & 60.23 & 58.58 & 70.78 & **86.66** & 52.60 & 62.52 \\  & acc & **99.87** & 97.75 & 99.62 & 99.75 & 93.36 & 98.09 & 99.62 & 98.73 & **99.87** & **99.87** & 90.60 & 99.49 & 99.62 \\
**total** & acc+ & 39.21 & 27.53 & 58.81 & 55.89 & 67.13 & 25.78 & 9.25 & 49.58 & 27.40 & 43.08 & **68.15** & 37.59 & 53.67 \\  & acc & 99.82 & **99.89** & 98.46 & 98.46 & 95.93 & 95.53 & 99.18 & 9 

the average reward of output \(r(x,v,y)\). To avoid over-optimization Gao et al. (2023), it is necessary to control the divergence between \(\pi_{\theta}\) and its reference model \(\pi_{\text{ref}}\) (\(\pi_{\theta}\) and \(\pi_{\text{ref}}\) are initialized from the same checkpoint). Thus, the overall objective loss is typically formulated as follows:

\[\mathcal{L}_{\text{PO}}=-\log\sigma(r(x,v,y)-\beta\log\frac{\pi_{\theta}(y|x, v)}{\pi_{\text{rot}}(y|x,v)}) \tag{2}\]

where \(\beta\) is a hyperparameter that controls the divergence between \(\pi_{\theta}\) and \(\pi_{\text{ref}}\), and \(\sigma(\cdot)\) is the sigmoid function. Recently, DPO Rafailov et al. (2023) simplifies the above process by maximizing the difference between the chosen reward \(r(x,v,y_{w})\) and the rejected reward \(r(x,v,y_{l})\). Following the Bradley-Terry model Bradley and Terry (1952), the optimization objective becomes:

\[\small\begin{split}\mathcal{L}_{\text{DPO}}&=-\log \sigma(\beta\log\frac{\pi_{\theta}(y_{\text{proj}}|x,v)}{\pi_{\text{rot}}(y_{ \text{proj}}|x,v)}-\beta\log\frac{\pi_{\theta}(y|x,v)}{\pi_{\text{rot}}(y|x,v)} )\\ \end{split} \tag{3}\]

### Object-based Optimization Objective

To mitigate the severe object hallucinations, we propose **oDOO** (object-based **D**irect **P**reference **O**ptimization objective). Unlike previous work Zhao et al. (2023); Li et al. (2023b); Zhou et al. (2024a), oDPO is a multimodal optimization objective based on visual objects. As illustrated in Figure 5, given a text input \(x\), an image input \(v\), and an output text response \(y\), oDPO removes the most frequently mentioned object \(o\) using its mask and obtain the rejected image input \(v_{\text{ro}}\). Here, \(r(x,v,y)\) represents the chosen reward, and \(r(x,v_{\text{ro}},y)\) represents the rejected reward. Then, the preference optimization objective is formulated as:

\[\small\begin{split}\mathcal{L}_{\text{noDPO}}&=-\log \sigma(\beta\log\frac{\pi_{\theta}(y|x,v)}{\pi_{\text{rot}}(y|x,v)}-\beta\log \frac{\pi_{\theta}(y|x,v_{\text{ro}})}{\pi_{\text{rot}}(y|x,v_{\text{ro}})})\\ \end{split} \tag{4}\]

Inspired by Wang et al. (2024a), we employ anchor preference optimization to ensure that the chosen reward consistently remains at a high value. The anchored objective is formulated as follows:

\[\small\begin{split}\mathcal{L}_{\text{AnePO}}&=- \log\sigma(\beta\log\frac{\pi_{\theta}(y~{}|~{}x,v)}{\pi_{\text{ref}}(y~{}|~{} x,v)})\\ \end{split} \tag{5}\]

Then the total preference optimization objective is

\[\small\begin{split}\mathcal{L}_{\text{oDPO}}&= \mathcal{L}_{\text{roDPO}}+\gamma\mathcal{L}_{\text{AnePO}}\\ \end{split} \tag{6}\]

where \(\gamma\) controls the influence of the anchored objective.

## 4 Experiment

### Experimental Setups

Training Data.The Silkie dataset Li et al. (2023b) contains 80K preference data, from which we selected 19K examples constructed by LLaAVA-Instruct-150K Liu et al. (2024c) for training. It is important to note that oDPO utilizes only the chosen responses constructed by Silkie and does not require the use of rejected responses.

Base Models.Following related work Zhou et al. (2024a), we applied oDPO on LLAVA-1.5 (7B and 13B) Liu et al. (2024c). To compare oDPO with standard DPO Rafailov et al. (2023), we also implemented standard DPO using the same training data. Apart from the differences in optimization objectives, all other settings are identical.

Implementation Details.We set the learning rate to 1e-7, used a cosine learning rate scheduler with a warmup ratio of 0.03, and set the default value of \(\gamma\) to 1. All models were trained for only one epoch, and all experiments were conducted on one A100 80GB GPU. More details can be found in Appendix C.

### Main Results

Performance on ROHE.We compare oDPO with other approaches Leng et al. (2024); Yue et al. (2024)

Figure 5: Overview of oDPO. The oDPO process is divided into two steps: constructing rejected images and performing preference optimization.

 

2024; Rafailov et al., 2023). Table 2 and Table 11 (provided in Appendix D) present the evaluation results. Although other approaches aim to reduce object hallucinations in LVLMs, they struggle to improve LVLMs' ability to determine the absence of removed objects. In contrast, oDPO effectively enhances their ability to determine the existence of visual objects through preference optimization based on visual objects, significantly mitigating hallucinations on ROHE.

Performance on Object Hallucination.To ensure that oDPO effectively mitigates object hallucinations in LVLMs, we conduct evaluations on four widely used object hallucination benchmarks: Object HalBench Rohrbach et al. (2018), MME-Hall Fu et al. (2023), AMBER Wang et al. (2023a) and MMHalBench Sun et al. (2024). Please refer to Appendix E for details. Table 3 demonstrates the effectiveness of oDPO in reducing hallucinations. Compared to other approaches, oDPO consistently exhibits stable and superior performance in various object hallucination tasks. It is worth noting that some approaches reduce the object coverage in image descriptions generated by LVLMs, which while potentially alleviating object hallucinations, also diminish the richness of descriptions. In contrast, oDPO reduces hallucinated objects while ensuring that LVLMs can richly describe the image content.

### Analysis and Discussion

How does oDPO perform on general vision-language tasks?We further evaluate oDPO on four popular general vision-language benchmarks: MME Fu et al. (2023), LLaVA-Wild Chen et al. (2024a), SQA-Img Lu et al. (2022), and MMStar Chen et al. (2024b). The results in Figure 6 show that oDPO outperforms the base model across these general benchmarks, suggesting that oDPO mitigates hallucinations without deteriorating the performance of LVLMs on other tasks.

How does oDPO perform when using different training data?As shown in Table 4, we explore

\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline  & vehicle & sports & accessory & animal & food & outdoor & kitchen & electronic & furniture & indoor & appliance & person & **total** \\ \hline LLAVA1:57B Liu et al. (2024c) & 46.84 & 25.51 & 16.62 & 74.57 & 53.93 & 22.79 & 20.26 & 26.34 & 36.91 & 28.66 & 12.50 & 70.39 & 39.21 \\ + YCD Lang et al. (2024) & 39.96 & 19.65 & 17.45 & 75.55 & 51.13 & 29.30 & 27.05 & 23.34 & 33.78 & 27.73 & 12.50 & 74.46 & 37.46 \\ e EOS Vote et al. (2024) & 48.33 & 25.65 & 13.02 & 71.39 & 50.23 & 19.49 & 26.17 & 25.70 & 35.12 & 23.36 & 11.22 & 51.30 & 88.24 \\ + DPo Rafailov et al. (2023) & 49.07 & 27.29 & 15.51 & 75.06 & 55.66 & 20.59 & 28.80 & 23.77 & 36.47 & 27.10 & 12.12 & 69.50 & 38.94 \\ + OPDO (ours) & **69.89** & **50.61** & **48.48** & **86.80** & **72.40** & **55.58** & **52.78** & **53.96** & **59.28** & **54.21** & **38.64** & **82.59** & **61.65** \\ \hline LLAVA1:513B Liu et al. (2024c) & 34.39 & 8.73 & 9.14 & 58.19 & 36.20 & 15.81 & 18.57 & 13.28 & 28.41 & 14.95 & 8.33 & 61.75 & 27.53 \\ + YCD Long et al. (2024) & 26.77 & 7.64 & 11.36 & 58.68 & 33.48 & 18.38 & 20.03 & 11.99 & 27.74 & 16.82 & 9.47 & 65.18 & 27.51 \\ + ODPo (features) & 35.50 & 9.96 & 8.86 & 60.88 & 36.20 & 14.34 & 19.30 & 14.13 & **25.86** & 16.20 & 9.85 & 62.39 & 28.34 \\ + oDPO (ours) & **58.74** & **24.97** & **24.45** & **77.02** & **52.94** & **34.19** & **37.28** & **31.05** & **42.73** & **33.02** & **20.45** & **75.86** & **44.71** \\ \hline \end{tabular}
\end{table}
Table 2: Results on ROHE. We report acc+ scores and provide the complete results in Appendix D. The best results are shown in **bold**.

\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline  & \multicolumn{3}{c}{Object BallBench} & \multicolumn{3}{c}{MME-Hall} & \multicolumn{3}{c}{AMBER} & \multicolumn{3}{c}{MMHalBench} \\ \cline{2-13}  & CHAIR\_i & CHARR\_i & Score \(\uparrow\) & CHAIR \(\downarrow\) & Cover \(\uparrow\) & HalfRate \(\downarrow\) & Cog \(\downarrow\) & Acct \(\uparrow\) & F1 \(\uparrow\) & Score \(\uparrow\) & HalfRate \(\downarrow\) \\ \hline ILLAVAA:15:7B Liu et al. (2024c) & 53.3 & 15.6 & 648.3 & 7.6 & 51.8 & 35.6 & 4.3 & 71.5 & 74.1 & 2.02 & 0.61 \\ + YCD Lang et al. (2024) & 53.3 & 15.7 & 604.7 & 6.9 & 50.6 & 32.2 & 3.7 & 72.0 & 74.8 & 2.12 & 0.54 \\ + EOS Yue et al. (2024) & 41.7 & 12.7 & 606.7 & 5.3 & 49.1 & **23.5** & **2.0** & 71.4 & 73.1 & 2.03 & 0.59 \\ + HAHO-DO Zhang et al. (2023) & 43.7 & 12.0 & 618.3 & 6.5 & 49.8 & 30.1 & 3.2 & 74.2 & 78.0 & 1.97 & 0.60 \\ + POVID Zhou et al. (2024) & 40.7 & 10.2 & 591.7 & 5.2 & 50.2 & 27.9 & 3.0 & 78.5 & 81.9 & 2.23 & 0.54 \\ + HALVA\({}^{\dagger}\) Sarkar et al. (2024) & 41.4 & 11.7 & **665.0** & 6.6 & 53.0 & 32.2 & 3.4 & – & 83.4 & 2.25 & 0.54 \\ + RLHF-Vi Yu et al. (2024) & - & - & - & 5.7 & 49.7 & 27.3 & 2.6 & – & 80.9 & 2.08 & 0.60 \\ + VPOP\({}^{\ddagger}\) Xie et al. (2024) & - & - & - & 5.6 & 49.7 & 27.3 & 2.7 & – & 81.6 & 2.16 & 0.56 \\ + mDPO Wang et al. (2024a) & 35.7 & 9.8 & - & **4.4** & 52.4 & 24.5 & 2.4 & - & - & 2.39 & 0.54 \\ + DPO Radilavoy et al. (2023) & 50.7 & 14.9 & 641.7 & 7.3 & **54.1** & 38.5 & 4.1 & 70.7 & 73.1 & 2.23 & 0.58 \\ + oDPO Ogun & **34.3** & **9.5** & 653.3 & 4.6 & 52.4 & 25.1 & 2.4 & **80.2** & **84.1** & **2.50** & **0.49** \\ \hline LAVA-U:1513B Liu et al. (2024c) & 49.3 & 14.6 & 643.3 & 6.8 & 52.0 & 31.7 & 3.5 & 71.3 & 73.1 & 2.38 & 0.53 \\ + SVD Long et al. (2024) & 47.7 & 13.2 & 601.7 & 6.7 & 51.3 & 31.0 & 3.5 & 71.5 & 73.5 & 2.40 & 0.51 \\ + DPO Rafailov et al. (2023) & 51.7 & 13.3 & 646.7 & 7.1 & **54.1** & 36.0 & 3.9 & 71.7 & 73.7 & 2.48 & 0.52 \\ + oDPO (ours) & **34.7** & **9.8** & **660.0** & **4.3** & 52.1 & **23.1** & **2.2** & **79.3** & **82.2** & **2.74** & **0.45** \\ \hline \end{tabular}
\end{table}
Table 3: Results on object hallucination. We report sentence-level and object-level scores (CHAIRs and CHAIRi) on Object HalBench Rohrbach et al. (2018), overall score on MMA-Hall Fu et al. (2023). For AMBER Wang et al. (2023a), we report CHAIR scores, object 

the performance of oDPO with different training data. oDPO is effective in different training data.

How does oDPO perform on LVLM that support high resolution?Table 5 shows the performance of oDPO on LLaVA-1.6-13B Liu et al. (2024), which compared to LLaVA-1.5 Liu et al. (2024), supports dynamic high resolution. Although oDPO slightly reduces object coverage and MME-Hal scores, it effectively mitigates object hallucination across different benchmarks. In contrast, the standard DPO not only fails to reduce object hallucination but also exacerbates it in some aspects.

How does \(\gamma\) affect the performance of oDPO?As shown in Figure 7, we investigate the impact of different \(\gamma\) values on the performance of oDPO. It is observed that a small \(\gamma\) value significantly reduces object hallucinations. However, this reduction is accompanied by a decline in performance on other tasks and a suppression of response diversity. To balance these effects, we set the \(\gamma\) value to 1, aiming to mitigate object hallucinations without compromising performance in other areas.

Why oDPO performs better than standard DPO and other baselines?During the pre-training process, there is little direct guidance for model to learn how to determine the existence of an object. Although RLHF methods based on textual preferences construct fine-grained preference pairs, they do not directly guide the model to learn why the chosen response aligns with the input image and why the preference for the rejected response should be reduced. In contrast, oDPO directly guides the model to increase its preference when the input image contains the key object, and to decrease it when the object is absent.

### Fine-grained Results

We further provide fine-grained results on MMHalBench Sun et al. (2024) in Table 6. Although oDPO slightly decreases scores in the relation and other categories, it surpasses standard DPO in all other categories. Notably, in the adversarial category, oDPO boosts the base model's score by 163%. These findings highlight the benefits of

\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline  & ROHE & MME-Hall & Object & HallBench & \multicolumn{4}{c}{AMBER} & \multicolumn{4}{c}{MBHLBench} \\ \cline{2-18}  & acc* & acc\(\uparrow\) & acc\(\uparrow\) & Score \(\uparrow\) & CHAIR\(\downarrow\) & CHARIR \(\downarrow\) & Covert \(\uparrow\) & HalfRate \(\downarrow\) & Cog \(\downarrow\) & Acc \(\uparrow\) & F1 \(\uparrow\) & Score \(\uparrow\) & HalfRate \(\downarrow\) \\ \hline LLaVA-1.5-7B Lin et al. (2024) & 391.982 & **98.683** & 53.3 & 15.6 & 7.6 & 51.8 & 35.6 & 4.3 & 71.5 & 74.1 & 2.02 & 0.61 \\ +oDPO (Shike-19K) & 61.65 & **93.49** & 653.3 & **34.3** & **9.5** & **4.6** & **53.4** & 25.1 & **2.4** & 80.2 & 84.1 & **2.50** & **0.49** \\ +oDPO (LLaVa-17K) & **63.70** & 98.42 & **661.7** & 43.0 & 12.1 & 5.0 & 51.0 & **24.8** & 2.7 & **80.5** & **84.5** & 2.48 & 0.51 \\ \hline LLaVA-1.5-13B Lin et al. (2024) & 275.93 & **98.98** & 643.3 & 49.3 & 14.6 & 6.8 & 52.0 & 31.7 & 3.5 & 71.3 & 73.1 & 2.38 & 0.53 \\ +opD (Smile-19K) & 44.71 & 99.58 & **660.0** & **34.7** & **9.8** & **4.3** & **52.1** & **23.1** & **2.2** & 79.3 & 82.2 & **2.74** & **0.45** \\ +oDFO (LiLAVA-17K) & **47.15** & 99.53 & **660.0** & 42.7 & 11.6 & 5.0 & 51.4 & 24.4 & 2.6 & **80.0** & **83.0** & 2.70 & 0.46 \\ \hline \end{tabular}
\end{table}
Table 4: The results of oDPO using different training data. LLaVA-17K: 17K examples randomly sampled from LLaVA-Instruct-150K Liu et al. (2024); Silkie-19K: 19K examples sampled from Silke Li et al. (2023b). The best results are denoted in **bold**.

Figure 6: Results on general vision-language tasks. We report the scores of base models (Base) and oDPO-enhanced models (oDPO) on four benchmarks: MME Fu et al. (2023), LLaVA-Wild Chen et al. (2024), SQA-Img Lu et al. (2022), and MMStar Chen et al. (2024).

\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l} \hline  & ROHE & MME-Hall & Object & HallBench & \multicolumn{4}{c}{AMBER} & \multicolumn{4}{c}{MBHLBench} \\ \cline{2-18}  & ac* & acc\(\uparrow\) & acc\(\uparrow\) & Score \(\uparrow\) & CHAIR\(\downarrow\) & CHARIR \(\downarrow\) & CIFAR \(\uparrow\) & HalfRate \(\downarrow\) & Cog \(\downarrow\) & Acc \(\uparrow\) & F1 \(\uparrow\) & Score \(\uparrow\) & HalfRate \(\downarrow\) \\ \hline LLaVA-1.6-13B Liu et al. (2024) & 55.89 & **98.46** & **660.0** & 30.0 & 10.3 & 8.6 & 62.0 & 50.5 & 4.2 & 81.2 & 84.9 & 3.09 & 0.46 \\ +DOP (Radilavov et al., 2023) & 43.59 & 98.13 & 648.3 & 40.3 & 8.8 & 7.6 & **63.1** & 49.2 & 4.5 & 69.9 & 71.1 & 3.40 & 0.42 \\ +oDPO (sens) & **63.06** & **90.75** & 650.0 & **27.7** & **7.2** & **5.7** & 59.0 & **33.9** & **2.9** & **82.7** & **86.8** & **3.42** & **0.33** \\ \hline \end{tabular}
\end{table}
Table 5: Results on LLaVA-1.6-13B. The best results are shown in **bold**.

Figure 7: Impact of different \(\gamma\) values. We report the results of two primary metrics specific to each of the four benchmarks: ROHE, MME Fu et al. (2023), Object HalBench Rohrbach et al. (2018), and AMBER Wang et al. (2023). The base model is LLaVA-1.5-7B.

 

oDPO across various fine-grained scenarios.

### Qualitative Study

Figure 8 presents qualitative examples. oDPO effectively mitigates hallucinations across different tasks. Furthermore, compared to the base model, the oDPO-enhanced model generally provides more detailed descriptions of the images, suggesting that oDPO effectively enhances LVLMs' visual understanding and reasoning capabilities.

### Results on More Complex Scenarios

To further evaluate oDPO's performance on more complex scenarios, we conduct additional experiments on AutoHallusion Wu et al. (2024) and ROPE Chen et al. (2024). The results in Figure 9 and Table 7 demonstrate that oDPO effectively alleviates hallucination issues of the base model across different scenarios.

## 5 Related Work

**Evaluations of Object Hallucinations in LVLMs.** Currently, the evaluation methods for object hallucinations are primarily divided into two categories Liu et al. (2024): evaluation in generation task and in discrimination task. Evaluation in generation task, typically uses hand-designed pipelines Rohrbach et al. (2018); Zhai et al. (2023); Lee et al. (2024) or LLM-based methods Liu et al. (2024); Sun et al. (2024); Gunjal et al. (2024); Wang et al. (2023) to locate the hallucinatory parts in the LVLM's responses and calculate the proportion and score of hallucinations. Evaluation in discrimination task aims to evaluate the performance of LVLMs in judging objects. They usually design

\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & overall & attribute & adversarial & comparison & counting & relation & environment & holistic & other \\ \hline LLaVA-15-7B Liu et al. (2024) & 2.02 & 3.00 & 1.17 & 1.83 & 2.25 & **2.00** & 3.08 & 1.75 & 1.08 \\ + DPO Rafailov et al. (2023) & 2.23 & 3.25 & 1.67 & 2.17 & 2.00 & 1.83 & 3.17 & **2.17** & **1.58** \\ + oDPO (ours) & **2.50** & **3.33** & **3.08** & **2.42** & **2.33** & 1.75 & **4.00** & **2.17** & 0.92 \\ \hline \end{tabular}
\end{table}
Table 6: Fine-grained results on MMHalBench Sun et al. (2024). The best results are denoted in **bold**.

\begin{table}
\begin{tabular}{c l c c c c c} \hline  & \multicolumn{3}{c}{**Seen**} & \multicolumn{3}{c}{**Unseen**} \\  & Wild \(\downarrow\) & Hom. & Het. \(\uparrow\) & Wald \(\uparrow\) & Hom. \(\uparrow\) & Hit. \(\uparrow\) \\ \cline{3-6}  & _DoPO_ & _MultiDomain_ & _MultiDomain_ & _MultiDomain_ & _MultiDomain_ & _MultiDomain_ \\ \hline LLAAV-15-7B Liu et al. (2024) & 24.94 & 58.05 & 7.76 & 16.35 & 37.71 & 4.72 \\ + DPO Radford et al. (2023) & 23.69 & 54.83 & 8.00 & 14.40 & 33.40 & 4.70 \\ + ODP Guo (ours) & **5.30** & **4.15** & **9.49** & **19.45** & **46.92** & **5.45** \\ \hline \hline \multirow{4}{*}{ILAAV-15-7B Liu et al. (2024)} & 30.28 & 61.15 & 1.308 & 25.32 & 52.49 & 10.24 \\  + OPD Radford et al. (2023) & 30.66 & 62.00 & 13.00 & 25.84 & 52.78 & 10.41 \\ + ODPG (ours) & **31.25** & **63.75** & **13.21** & **26.04** & **54.57** & **10.63** \\ \hline \hline \multirow{4}{*}{ILAAV-15-7B Liu et al. (2024)} & 2.50 & 5.98 & 0.95 & 2.22 & 3.77 & 1.25 \\  + OPD Radford et al. (2023) & 3.30 & 7.74 & 1.43 & 2.48 & 4.63 & **1.56** \\ + ODPO (ours) & **3.84** & **5.84** & **1.80** & **3.49** & **6.72** & 1.30 \\ \hline \hline \multirow{4}{*}{ILAAV-15-7B Liu et al. (2024)} & 33.32 & 7.89 & 1.68 & 3.32 & 6.63 & 1.56 \\  + ODTo Ritalkov et al. (2021) & 4.24 & 10.31 & 1.97 & 3.51 & 7.38 & 1.61 \\ + oDPO (ours) & **4.65** & **10.37** & **2.30** & **4.28** & **9.06** & **1.81** \\ \hline \end{tabular}
\end{table}
Table 7: Results of oDPO on ROPE Chen et al. (2024). The best results are denoted in **bold**.

Figure 8: Qualitative results of oDPO. The left, middle and right figures are from ROHE, MMHalBench Sun et al. (2024) and AMBER Wang et al. (2023), respectively. It can be observed that oDPO significantly reduces hallucination and enhances the model’s ability to describe detailed information in images across different tasks.

Figure 9: Results of oDPO on AutoHallusion Wu et al. (2024).

 

visual questions about objects (e.g., _"Is there a cat in the image?"_) and prompt LVLMs, expecting them to provide correct answers (_"yes"_ or _"no"_). They usually employ three different approaches to choose the objects for questioning: manual design Fu et al. (2023), handcrafted pipelines Li et al. (2023c); Wang et al. (2023a), or GPT generation Hu et al. (2023); Lovenia et al. (2023). ROHE also evaluates object hallucinations in discrimination task. It not only evaluates the ability of LVLMs to determine an object exists in the image but also assesses their ability to determine the absence of the same object after it has been removed.

**Mitigation of Object Hallucinations in LVLMs.** To mitigate object hallucinations in LVLMs, some studies have focused on constructing more robust datasets or designing specific training strategies during the pre-training stage Sun et al. (2024); Jiang et al. (2024); Liu et al. (2024); Yue et al. (2024). Other approaches have utilized specific decoding strategies Leng et al. (2024); Zhu et al. (2024); Wang et al. (2024b) or directly corrected the responses of LVLMs Zhou et al. (2024b); Wu et al. (2024a). Recently, researchers have performed preference alignment in LVLMs by collecting human preference data Sun et al. (2024); Yu et al. (2024) or collecting the preferences from advanced LLMs Zhao et al. (2023); Li et al. (2023b); Zhou et al. (2024a); Sarkar et al. (2024). Wang et al. (2024a) introduces a multimodal direct preference optimization objective that constructs the rejected image by cropping the original image. oDPO also uses the multimodal DPO objective, but it constructs the rejected image by removing objects from the original image. Additionally, oDPO does not use the rejected responses from the preference dataset; instead, it focuses on preference optimization based on visual objects and aims for LVLMs to learn to prefer the original image. This enhances their ability to determine the absence of removed objects and reduces hallucinations related to them.

## 6 Conclusion

In this paper, we introduce ROHE, which designed to evaluate object hallucinations by removing objects from images. Our evaluation results reveal that LVLMs still suffer from severe hallucinations, as they often struggle to determine the absence of removed objects. To address this, we propose oDPO, an object-based DPO objective designed to guide LVLMs to learn to determine the existence of objects. We conducted extensive experiments and the results demonstrate that oDPO not only enhances LVLMs' ability to determine the existence of objects but also improves their performance on various vision-language tasks, particularly in reducing object hallucinations.

### Limitations

Although we have conducted extensive exploration and experiments, this work still has many limitations. First, we only evaluated object hallucinations through binary question-answering, it does not allow us to assess the overall hallucination performance of LVLMs. Second, due to budget and resource constraints, we developed the benchmark only on the MSCOCO 2017 validation dataset Lin et al. (2014). Third, we have evaluated only some open-source LVLMs and have not yet assessed closed-source LVLMs or the latest LVLMs. In addition, due to the limitations of LaMa Suvorov et al. (2022), the synthesized images may contain unrealistic artifacts. Finally, owing to computational resource constraints, although we have conducted experiments on several baseline LVLMs and training datasets, it is challenging for us to explore the performance of oDPO on larger-scale LVLMs, e.g., LLaVA-1.6-34B Liu et al. (2024d).

### Ethics Statement

In this work, we use LaMa Suvorov et al. (2022) to generate images based on the MSCOCO dataset Lin et al. (2014). It is important to acknowledge that the generated images may contain counterfactual or fake information. Researchers can employ ROHE to evaluate object hallucination in LVLMs, but should be cautious about applying the fake images in the benchmark to other purposes to avoid causing social interference.

### Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62201072, Grant U23B2001, Grant 62171057, Grant 62101064, Grant 62001054, and Grant 62071067; in part by the Ministry of Education and China Mobile Joint Fund under Grant MCM20200202 and Grant MCM20180101; in part by the BUPT-China Mobile Research Institute Joint Innovation Center 



## References

* Anil et al. (2015) Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitter, Timothy P. Lillicrap, Angeliki Lazaridou, Ohan Firat, James Molloy, Michael Isard, Paul Ronald Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danileka, Beca Roelofs, Anais White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, and et al. 2023. Gemini: A family of highly capable multimodal models. _CoRR_, abs/2312.11805.
* Bai et al. (2023) Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. Qwen-vl: A frontier large vision-language model with versatile abilities. _CoRR_, abs/2308.12966.
* Bradley and Terry (1952) Ralph Allan Bradley and Milton E. Terry. 1952. Rank analysis of incomplete block designs: I. the method of paired comparisons. _Biometrika_, 39:324.
* Caron et al. (2021) Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jegou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. In _2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021_, pages 9630-9640. IEEE.
* Chen et al. (2024a) Delong Chen, Jianfeng Liu, Wenliang Dai, and Baoyuan Wang. 2024a. Visual instruction tuning with polite flamingo. In _Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada_, pages 17745-17753. AAAI Press.
* Chen et al. (2024b) Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, and Feng Zhao. 2024b. Are we on the right way for evaluating large vision-language models? _CoRR_, abs/2403.20330.
* Chen et al. (2024c) Xuweiyi Chen, Ziqiao Ma, Xuejun Zhang, Sihan Xu, Shengyi Qian, Jianing Yang, David F. Fouhey, and Joyce Chai. 2024c. Multi-object hallucination in vision-language models. _CoRR_, abs/2407.06192.
* Chiang et al. (2023) Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Liamin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgp quality.
* 16, 2023_.
* Fu et al. (2023) Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, and Rongrojn Ji. 2023. MME: A comprehensive evaluation benchmark for multimodal large language models. _CoRR_, abs/2306.13394.
* Gao et al. (2023) Leo Gao, John Schulman, and Jacob Hilton. 2023. Scaling laws for reward model overoptimization. In _International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA_, volume 202 of _Proceedings of Machine Learning Research_, pages 10835-10866. PMLR.
* Gunjal et al. (2024) Anisha Gunjal, Jihan Yin, and Erhan Bas. 2024. Detecting and preventing hallucinations in large vision language models. In _Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada_, pages 18135-18143. AAAI Press.
* Hu et al. (2022) Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. Lora: Low-rank adaptation of large language models. In _The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022_. OpenReview.net.
* Hu et al. (2023) Hongyu Hu, Jiyuan Zhang, Minyi Zhao, and Zhenbang Sun. 2023. CIEM: contrastive instruction evaluation method for better instruction tuning. _CoRR_, abs/2309.02301.
* Jiang et al. (2024) Chaoya Jiang, Haiyang Xu, Mengfan Dong, Jiaxing Chen, Wei Ye, Ming Yan, Qinghao Ye, Ji Zhang, Fei Huang, and Shikun Zhang. 2024. Hallucination augmented contrastive learning for multimodal large language model. In _IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024_, pages 27026-27036. IEEE.
* Lee et al. (2024) Seongyun Lee, Sue Hyun Park, Yongrae Jo, and Mingoon Seo. 2024. Volcano: Mitigating multimodal hallucination through self-feedback guided revision. In _Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

(Volume 1: Long Papers), NAACL 2024, Mexico City, Mexico, June 16-21, 2024_, pages 391-404. Association for Computational Linguistics.
* [Long et al.2024] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. 2024. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In _IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024_, pages 13872-13882. IEEE.
* [Li et al.2023a] Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. 2023a. BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In _International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA_, volume 202 of _Proceedings of Machine Learning Research_, pages 19730-19742. PMLR.
* [Li et al.2023b] Lei Li, Zhihui Xie, Mukai Li, Shunian Chen, Peiyi Wang, Liang Chen, Yazheng Yang, Benyou Wang, and Lingpeng Kong. 2023b. Silkie: Preference distillation for large visual language models. _CoRR_, abs/2312.10665.
* [Li et al.2023c] Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. 2023c. Evaluating object hallucination in large vision-language models. In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023_, pages 292-305. Association for Computational Linguistics.
* [Li et al.2024] Zhang Li, Biao Yang, Qiang Liu, Zhiyin Ma, Shuo Zhang, Jingxu Yang, Yabo Sun, Yuliang Liu, and Xiang Bai. 2024. Monkey: Image resolution and text label are important things for large multi-modal models. In _IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024_, pages 26753-26763. IEEE.
* ECCV 2014
- 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V_, volume 8693 of _Lecture Notes in Computer Science_, pages 740-755. Springer.
* [Lin et al.2023] Ziyi Lin, Chris Liu, Renrui Zhang, Peng Gao, Longtian Qiu, Han Xiao, Han Qiu, Chen Lin, Wenqi Shao, Keqin Chen, Jiaming Han, Siyuan Huang, Yichi Zhang, Xuming He, Hongsheng Li, and Yu Qiao. 2023. SPHINX: the joint mixing of weights, tasks, and visual embeddings for multi-modal large language models. _CoRR_, abs/2311.07575.
* [Liu et al.2024a] Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. 2024a. Mitigating hallucination in large multi-modal models via robust instruction tuning. In _The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024_. OpenReview.net.
* [Liu et al.2024b] Hanchao Liu, Wenyuan Xue, Yifei Chen, Dapeng Chen, Xiutian Zhao, Ke Wang, Liping Hou, Rongjun Li, and Wei Peng. 2024b. A survey on hallucination in large vision-language models. _CoRR_, abs/2402.00253.
* [Liu et al.2024c] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2024c. Improved baselines with visual instruction tuning. In _IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024_, pages 26286-26296. IEEE.
* [Liu et al.2024d] Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. 2024d. Llavanext: Improved reasoning, ocr, and world knowledge.
* [Lovenia et al.2023] Holy Lovenia, Wenliang Dai, Samuel Calhyawijaya, Ziwei Ji, and Pascale Fung. 2023. Negative object presence evaluation (NOPE) to measure object hallucination in vision-language models. _CoRR_, abs/2310.05338.
* December 9, 2022_.
* [OpenAI2023] OpenAI. 2023. GPT-4 technical report. _CoRR_, abs/2303.08774.
* [Oquab et al.2022] Maxime Oquab, Timothee Darcet, Theo Moutzkanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. 2024. Dinov2: Learning robust visual features without supervision. _Trans. Mach. Learn. Res._, 2024.
* [Paszke et al.2019] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. Pytorch: An imperative style, high-performance deep learning library. In _Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada_, pages 8024-8035 

Tianshuo Peng, Zuchao Li, Lefei Zhang, Hai Zhao, Ping Wang, and Bo Du. 2024. Multi-modal auto-regressive modeling via visual words. _CoRR_, abs/2403.07720.
* Pi et al. (2024) Renjie Pi, Tianyang Han, Wei Xiong, Jipeng Zhang, Runtao Liu, Rui Pan, and Tong Zhang. 2024. Strengthening multimodal large language model with bootstrapped preference optimization. _CoRR_, abs/2403.08730.
* Radford et al. (2021) Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In _Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event_, volume 139 of _Proceedings of Machine Learning Research_, pages 8748-8763. PMLR.
* 16, 2023_.
* Rasheed et al. (2024) Hanoona Abdul Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly, Abdelrahman M. Shaker, Salman H. Khan, Higham Cholakkal, Rao Muhammad Anwer, Eric P. Xing, Ming-Hsuan Yang, and Fahad Shahbaz Khan. 2024. GLamm: Pixel grounding large multimodal model. In _IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024_, pages 13009-13018. IEEE.
* November 4, 2018_, pages 4035-4045. Association for Computational Linguistics.
* Sarkar et al. (2024) Pritam Sarkar, Sayna Ebrahimi, Ali Etemad, Ahmad Beirami, Sercan O. Arik, and Tomas Pfister. 2024. Mitigating object hallucination via data augmented contrastive tuning. _CoRR_, abs/2405.18654.
* Sun et al. (2024) Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liangyan Gui, Yu-Xiong Wang, Yiming Yang, Kurt Keutzer, and Trevor Darrell. 2024. Aligning large multimodal models with factually augmented RLHF. In _Findings of the Association for Computational Linguistics, ACL 2024, Banggok, Thailand and virtual meeting, August 11-16, 2024_, pages 13088-13110. Association for Computational Linguistics.
* Suvorov et al. (2022) Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, and Victor Lempitsky. 2022. Resolution-robust large mask inpainting with fourier convolutions. In _IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, Waikoloa, HI, USA, January 3-8, 2022_, pages 3172-3182. IEEE.
* Tong et al. (2024) Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. 2024. Eyes wide shut? exploring the visual shortcomings of multimodal lms. In _IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024_, pages 9568-9578. IEEE.
* Touvron et al. (2024) Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bileel, Lukas Blecher, Cristian Cantomferre, Moya Chen, Guillem Cucuvull, David Esibou, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inanc, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Klounmann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaloy, Pushkar Mishra, Igor Molbybo, Yixin Nie, Andrew Poulton, Jeremy Rezenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Purxin Xu, Zheng Yan, Iliyan Zarvo, Yuchen Zhang, Angela Fan, Melanie Kambandour, Sharan Narang, Aurelieu Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023. LLama 2: Open foundation and fine-tuned chat models. _CoRR_, abs/2307.09288.
* Wang et al. (2024a) Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, and Munao Chen. 2024a. mdpo: Conditional preference optimization for multimodal large language models. _CoRR_, abs/2406.11839.
* Wang et al. (2023a) Junyang Wang, Yuhang Wang, Guohai Xu, Jing Zhang, Yukai Gu, Haitao Jia, Ming Yan, Ji Zhang, and Jitao Sang. 2023a. An llm-free multi-dimensional benchmark for mllms hallucination evaluation. _CoRR_, abs/2311.07397.
* Wang et al. (2023b) Junyang Wang, Yiyang Zhou, Guhohai Xu, Pengcheng Shi, Chenlin Zhao, Haiyang Xu, Qinghao Ye, Ming Yan, Ji Zhang, Jihua Zhu, Jitao Sang, and Haoyu Tang. 2023b. Evaluation and analysis of hallucination in large vision-language models. _CoRR_, abs/2308.15126.
* Wang et al. (2024b) Xinntong Wang, Jingheng Pan, Liang Ding, and Chris Biemann. 2024b. Mitigating hallucinations in large vision-language models with instruction contrastive decoding. In _Findings of the Association for Computational Linguistics, ACL 2024, Banggok, Thailand and virtual meeting, August 11-16, 2024_, pages 15840-15853. Association for Computational Lin 

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, and Jamie Brew. 2019. Huggingface's transformers: State-of-the-art natural language processing. _CoRR_, abs/1910.03771.
* Woo et al. (2023) Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie. 2023. Convnext V2: co-designing and scaling convnets with masked autoencoders. In _IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023_, pages 16133-16142. IEEE.
* Wu et al. (2024a) Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang, and Tieniu Tan. 2024a. Logical closed loop: Uncovering object hallucinations in large vision-language models. In _Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024_, pages 6944-6962. Association for Computational Linguistics.
* Wu et al. (2024b) Xiyang Wu, Tianruti Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan L. Boyd-Graber, Tianyi Zhou, and Dinesh Manocha. 2024b. Autohalusion: Automatic generation of hallucination benchmarks for vision-language models. In _Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024_, pages 8395-8419. Association for Computational Linguistics.
* Xie et al. (2024) Yuxi Xie, Guanzhen Li, Xiao Xu, and Min-Yen Kan. 2024. V-DPO: mitigating hallucination in large vision language models via vision-guided direct preference optimization. In _Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024_, pages 13258-13273. Association for Computational Linguistics.
* Young et al. (2024) Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhuo Huang, Xiaohui Hu, Xiaoyoi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yukuan Cai, Zhenyu Gu, Zhiyuan Liu, and Zonghong Dai. 2024. Yi: Open foundation models by 01.ai. _CoRR_, abs/2403.04652.
* Yu et al. (2024) Tianyu Yu, Yuan Yao, Haoye Zhang, Taiwan He, Yifeng Han, Ganqu Cui, Jinyi Hu, Zhiyuan Liu, Hai-Tao Zheng, and Maosong Sun. 2024. RLHF-V: towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback. In _IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024_, pages 13807-13816. IEEE.
* Yue et al. (2024) Zihao Yue, Liang Zhang, and Qin Jin. 2024. Less is more: Mitigating multimodal hallucination from an EOS decision perspective. In _Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangskol, Thailand, August 11-16, 2024_, pages 11766-11781. Association for Computational Linguistics.
* Zhai et al. (2023) Bohan Zhai, Shijia Yang, Xiangchen Zhao, Chenfeng Xu, Sheng Shen, Dongdi Zhao, Kurt Keutzer, Manning Li, Tan Yan, and Xiangjun Fan. 2023. Halleswitch: Rethinking and controlling object existence hallucinations in large vision language models for detailed caption. _CoRR_, abs/2310.01779.
* Zhao et al. (2023) Zhiyuan Zhao, Bin Wang, Linke Ouyang, Xiaoyi Dong, Jiaqi Wang, and Conghui He. 2023. Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization. _CoRR_, abs/2311.16839.
* Zhou et al. (2024a) Yiyang Zhou, Chenchang Cui, Rafael Rafailov, Chelsea Finn, and Huaxiu Yao. 2024a. Aligning modalities in vision large language models via preference finetuning. _CoRR_, abs/2402.11411.
* Zhou et al. (2024b) Yiyang Zhou, Chenchang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, and Huaxiu Yao. 2024b. Analyzing and mitigating object hallucination in large vision-language models. In _The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024_. OpenReview.net.
* Zhu et al. (2024)Lanyun Zhu, Deviji Ji, Tianrun Chen, Peng Xu, Jieping Ye, and Jun Liu. 2024. IBD: alleviating hallucinations in large vision-language models via image-biased decoding. _CoRR_, abs/2402.18476.

## Appendix A Examples of ROHE

Figure 10 and 11 show some examples of ROHE.

## Appendix B Details of the Evaluation on ROHE

All experiments in this work were conducted using the PyTorch framework Paszke et al. (2019) and incorporated capabilities from HuggingFace's Transformers library Wolf et al. (2019). The experiments were conducted using an NVIDIA A100 GPU and an Intel Xeon Silver 4210R CPU. We used the settings in Table 8. Here are the details of the LVLMs we evaluated:

\begin{table}
\begin{tabular}{l c} \hline \hline Hyperparameters & \\ \hline do\_sample & False \\ num\_beams & 1 \\ top\_p & 1 \\ top\_k & None \\ temperature & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hyperparameter settings of ROHE.

 

* **LLaVA**(Chen et al., 2024; Liu et al., 2024c,d): We evaluated LLaVA-1.5-7B, LLaVA-1.5-13B, LLaVA-1.6-7B, LLaVA-1.6-13B, and LLaVA-1.6-3dB. Notably, LLaVA-1.6 supports dynamic high-resolution capabilities for higher image resolutions. LLaVA-1.6-34B is based on HermesYi-34B [Young et al., 2024], and the other models are based on Vicuna [Chiang et al., 2023].
* **InstructBlip**(Dai et al., 2023): We evaluated InstructBlip-7B and InstructBlip-13B which are based on Vicuna [Chiang et al., 2023].
* **Qwen-VL-Chat**(Bai et al., 2023): We evaluated Qwen-VL-Chat which is based on Qwen-7B [Bai et al., 2023].
* **LLaVA-MOF**(Tong et al., 2024): This model is an improved version of LLAVA-1.5-13B [Liu et al., 2024c], enhancing the visual perception by mixing CLIP-VIT [Radford et al., 2021] and DINOV2-VIT [Oquab et al., 2024].
* **VW-LMM**(Peng et al., 2024): VW-LMM uses the same training dataset as LLaVA-1.5 [Liu et al., 2024c], but constructs visual words to introduce visual supervisory information. To compare results with LLaVA-1.5, we evaluated VW-LMM-Vicuna-7B.
* **Monkey-Chat**(Li et al., 2024): Monkey-Chat uses Qwen-7B [Bai et al., 2023] as its foundational model and is capable of processing images with resolutions up to \(1344\times 896\) pixels through a super-resolution method.
* **SPHINK**(Lin et al., 2023): SPHINX employs four visual encoders, CLIP-VIT [Radford et al., 2021], CLIP-ConvNext [Woo et al., 2023], DINOv2-VIT [Oquab et al., 2024], and Q-former [Li et al., 2023a], to extract visual features, thereby enhancing visual perception by combining visual features. We evaluated two versions, SPHINK and SPHINX-1k, where SPHINX takes a low-resolution image of \(224\times 224\) as input while SPHINX-1k handles an image resolution of \(448\times 448\) by averaging four sub-images into 1,445 visual tokens by cropping the images.

## Appendix C More Implementation Details of oDPO

All experiments were conducted using the PyTorch framework [Paszke et al., 2019] and incorporated capabilities from HuggingFace's Transformers library [Wolf et al., 2019]. The experiments were conducted using an NVIDIA A100 GPU and an Intel Xeon Silver 4210R CPU. We adapted LoRA fine-tuning [Hu et al., 2022]. The details of training hyperparameters used in oDPO is presented in Table 9.

## Appendix D Complete Results on ROHE

Table 11 provides the complete results on ROHE.

## Appendix E Details of Evaluation Benchmarks

* **Object HalBench**(Rohrbach et al., 2018): Object HalBench is a widely used method for evaluating object hallucination in image descriptions. It typically reports two object hallucination scores: sentence-level and object-level CHAIR scores, referred to as CHAIR\({}_{\text{s}}\) and CHAIR\({}_{\text{i}}\), respectively. They can be formulated as: \[\text{CHAIRs}=\frac{|\{\text{captions with hallucinated objects}\}|}{|\{\text{all captions}\}|},\] (7) \[\text{CHAiRi}_{\text{i}}=\frac{|\{\text{hallucinated objects}\}|}{|\{ \text{all mentioned objects}\}|}.\] (8) We use 300 images randomly sampled by Yu et al. (2024) from MSCOCO [Lin et al., 2014] along with their corresponding prompts as the evaluation examples. The detection of objects in the LVLMs' responses is conducted using an exact match approach.
* **MME-Hall**(Fu et al., 2023): MME-Hall is the hallucination subset of the MME benchmark [Fu et al., 2023], including four object-related subtasks: existence, count, position, and color. It effectively evaluates the hallucination in LVLMs on discrimination tasks. Each subtask has a total score of 200, making the overall score for MME-Hall is 800.

\begin{table}
\begin{tabular}{l c} \hline \hline Hyperparameters & \\ \hline lora rank & 128 \\lora alpha & 256 \\ mm projector lr & 1e-5 \\ batch size & 1 \\ learning rate & 1e-7 \\ warmup decay & 0. \\ warmup ratio & 0.03 \\ learning rate scheduler & Cosine \\ max length & 1024 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Training hyperparameters used in oDPO.

 

* **AMBER**[2013]: AMBER is an LLM-free object hallucination benchmark that effectively evaluates the hallucination performance of LVLMs on both generative and discrimination tasks. For generative tasks, we report CHAIR scores, object coverage (Cover), hallucination rate (HalRate), and cognition (Cog). For discrimination tasks, we report accuracy (Acc) and F1 scores.
* **MMHalBench**[204]: MMHalBench is an object hallucination evaluation benchmark that utilizes GPT-4 [56] to assist in scoring. It effectively evaluates the quality and degree of hallucination in LVLMs' responses. MMHalBench reports the overall score (with a maximum of 6) and the hallucination rate (HalRate). It is important to note that the default evaluation GPT model, gpt-4-0314, is currently inaccessible, so we use gpt-4-0613 for the evaluation.

## Appendix F Evaluation of Closed-Source Models and Grounding LVLMs

Apart from the grounding LVLMs like SPHINX, SPHINK-1k, and Qwen-VL-Chat already reported in Table 1, we have conducted further evaluations of GLaMM [79] (an open-source Grounding LVLM) and Qwen-VL-Plus [8] (a leading closed-source model known for its strong performance on grounding tasks). The results are shown in Table 10. It is important to note that for a fair comparison, we do not provide GLaMM with additional Region Input, which may be the reason for its suboptimal performance on ROHE. Although GLaMM performs excellently on grounding tasks, its performance on ROHE is not satisfactory. In contrast, Qwen-VL-Plus achieves remarkable performance.

\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{supercategory} & \multicolumn{2}{c}{GLaMM} & \multicolumn{2}{c}{Qwen-VL-Plus} \\ \cline{2-5}  & acc & acc+ & acc & acc+ \\ \hline vehicle & 100.0 & 5.58 & 92.94 & 71.38 \\ sports & 100.0 & 0.00 & 92.77 & 55.39 \\ accessory & 100.0 & 0.00 & 95.01 & 58.17 \\ animal & 100.0 & 3.91 & 99.27 & 86.80 \\ food & 100.0 & 2.26 & 95.48 & 61.54 \\ outdoor & 100.0 & 2.21 & 95.96 & 66.18 \\ kitchen & 100.0 & 0.00 & 82.75 & 65.64 \\ electronic & 100.0 & 2.57 & 88.65 & 67.67 \\ furniture & 99.11 & 12.30 & 84.12 & 61.74 \\ indoor & 100.0 & 2.49 & 85.36 & 61.37 \\ appliance & 98.86 & 11.74 & 89.02 & 52.27 \\ person & 99.75 & 21.47 & 97.20 & 82.59 \\ \hline
**total** & 99.84 & 6.03 & 91.41 & 67.17 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Evaluation results of leading closed-source models and grounding LVLMs on ROHE.

 



\begin{table}
\begin{tabular}{l l|c c c c c|c c c c}  & \multicolumn{6}{c}{LLaVA-1.5-7B} \\ \cline{3-12}  & & base & VCD & EOS & DPO & oDPO & base & VCD & DPO & oDPO \\ \hline vehicle & acc+ & 46.84 & 39.96 & 48.33 & 49.07 & **69.89** & 34.39 & 26.77 & 35.50 & **58.74** \\  & acc & 99.81 & 93.87 & **100.0** & **100.0** & 99.44 & **100.0** & 94.42 & **100.0** & 99.81 \\ sports & acc+ & 25.51 & 19.65 & 25.65 & 27.29 & **50.61** & 8.73 & 7.64 & 9.96 & **24.97** \\  & acc & **99.86** & 97.14 & **99.86** & **99.86** & 98.77 & **99.86** & 98.77 & 99.73 & 99.73 \\ accessory & acc+ & 16.62 & 17.45 & 13.02 & 15.51 & **48.48** & 9.14 & 11.36 & 8.86 & **24.65** \\  & acc & **100.0** & 98.34 & **100.0** & **100.0** & 99.45 & **100.0** & 99.17 & 100.0 & 99.72 \\ animal & acc+ & 74.57 & 75.55 & 71.39 & 75.06 & **86.80** & 58.19 & 58.68 & 60.88 & **77.02** \\  & acc & **100.0** & 97.80 & 99.76 & **100.0** & 99.76 & **99.51** & 98.29 & **99.51** & **99.51** \\ food & acc+ & 53.39 & 51.13 & 50.23 & 55.66 & **72.40** & 36.20 & 33.48 & 36.20 & **52.94** \\  & acc & **99.55** & 98.19 & **99.55** & **99.55** & 97.29 & **100.0** & 98.19 & **100.0** & 99.10 \\ outdoor & acc+ & 22.79 & 23.90 & 19.49 & 20.59 & **55.88** & 15.81 & 18.38 & 14.34 & **34.19** \\  & acc & **100.0** & 98.90 & **100.0** & **100.0** & 98.90 & **100.0** & 99.63 & **100.0** & **100.0** \\ kitchen & acc+ & 30.26 & 27.05 & 26.17 & 28.80 & **52.78** & 18.57 & 20.03 & 19.30 & **37.28** \\  & acc & 99.42 & 93.13 & **99.56** & **99.56** & 96.20 & **100.0** & 94.30 & **100.0** & 99.12 \\ electronic & acc+ & 26.34 & 23.34 & 25.70 & 23.77 & **53.96** & 13.28 & 11.99 & 14.13 & **31.05** \\  & acc & **100.0** & 97.00 & 99.79 & **100.0** & 98.50 & **100.0** & 97.64 & **100.0** & **100.0** \\ furniture & acc+ & 36.91 & 33.78 & 35.12 & 36.47 & **59.28** & 28.41 & 27.74 & 28.86 & **42.73** \\  & acc & 99.55 & 94.18 & **99.78** & **99.78** & 98.21 & 99.78 & 95.08 & **100.0** & 98.66 \\ indoor & acc+ & 28.66 & 27.73 & 23.36 & 27.10 & **54.21** & 14.95 & 16.82 & 16.20 & **33.02** \\  & acc & **100.0** & 97.20 & **100.0** & **100.0** & 99.07 & **100.0** & 98.75 & **100.0** & **100.0** \\ appliance & acc+ & 12.50 & 12.50 & 12.12 & 12.12 & **38.64** & 8.33 & 9.47 & 9.85 & **20.45** \\  & acc & **100.0** & 97.73 & **100.0** & **100.0** & 97.73 & **100.0** & 99.24 & **100.0** & 99.62 \\ person & acc+ & 70.39 & 74.46 & 75.10 & 69.50 & **82.59** & 61.75 & 65.18 & 62.39 & **75.86** \\  & acc & 99.87 & 95.93 & 99.62 & **100.0** & 98.86 & 99.75 & 96.19 & **99.87** & 99.75 \\
**total** & acc+ & 39.21 & 37.46 & 38.24 & 38.94 & **61.65** & 27.53 & 27.51 & 28.34 & **44.71** \\  & acc & 99.82 & 96.18 & 99.80 & **99.89** & 98.49 & 99.89 & 97.06 & **99.91** & 99.58 \\ \hline \end{tabular}
\end{table}
Table 11: Complete results on ROHE. The best results are shown in **bold**.



Figure 10: Examples of ROHE (Part I). The positive images (with objects) are labeled as \({\bf w}\) and the negative images (without objects) are labeled as \({\bf w/o}\).

 

Figure 11: Examples of ROHE (Part II). The positive images (with objects) are labeled as \({\bf w}\) and the negative images (without objects) are labeled as \({\bf w/o}\).