Title: Toward a Stable, Fair, and Comprehensive Evaluation of Object Hallucination in Large Vision-Language Models
Venue: NeurIPS 2024
Accepted: yes
Keywords: Large Vision-Language Models

Multimodal large language models

Multimodal

Object hallucination

Evaluation

Image caption
Abstract: Given different instructions, large vision-language models (LVLMs) exhibit different degrees of object hallucinations, posing a significant challenge to the evaluation of object hallucinations. Overcoming this challenge, existing object hallucination evaluation methods average the results obtained from a set of instructions. However, these methods fail to provide consistent evaluation across instruction sets that generate image descriptions of significantly different lengths. In this paper, we present the first systematic investigation of the effect of instructions on object hallucinations in LVLMs, with a specific focus on the role played by image description lengths. A valuable finding is that instructions indirectly affect hallucinations through the length of image descriptions. The longer the image description, the higher the object hallucination degree. Accordingly, we fit an informative length-hallucination curve, upon which a fine-grained evaluation framework named LeHaCE is introduced for evaluating object hallucinations at any given image description length. LeHaCE evaluates the object hallucination degree at a uniform image description length to mitigate the effect of description lengths, promoting stability and fairness. Moreover, LeHaCE incorporates the curve slope as an innovative hallucination evaluation metric, reflecting the extent to which the object hallucination degree is affected by the image description length, achieving a more comprehensive evaluation. Experimental results demonstrate that LeHaCE provides a more stable, fair, and comprehensive evaluation of object hallucinations in LVLMs compared to existing methods.

Toward a Stable, Fair, and Comprehensive Evaluation of Object Hallucination in Large Vision-Language Models

Abstract

Given different instructions, large vision-language models (LVLMs) exhibit different degrees of object hallucinations, posing a significant challenge to the evaluation of object hallucinations. Overcoming this challenge, existing object hallucination evaluation methods average the results obtained from a set of instructions. However, these methods fail to provide consistent evaluation across instruction sets that generate image descriptions of significantly different lengths. In this paper, we present the first systematic investigation into the effect of instructions on object hallucinations in LVLMs, with a specific focus on the role played by image description lengths. A valuable finding is that instructions indirectly affect hallucinations through the length of image descriptions. The longer the image description, the higher the object hallucination degree. Accordingly, we fit an informative length-hallucination curve, upon which a fine-grained evaluation framework named LeHaCE is introduced for evaluating object hallucinations at any given image description length. LeHaCE evaluates the object hallucination degree at a uniform image description length to mitigate the effect of description lengths, promoting stability and fairness. Moreover, LeHaCE incorporates the curve slope as an innovative hallucination evaluation metric, reflecting the extent to which the object hallucination degree is affected by the image description length, achieving a more comprehensive evaluation. Experimental results demonstrate that LeHaCE provides a more stable, fair, and comprehensive evaluation of object hallucinations in LVLMs compared to existing methods.

1 Introduction

Drawing inspiration from the remarkable language capabilities exhibited by large language models (LLMs) [1-3], large vision-language models (LVLMs) [2, 4-7] have been well-developed, achieving significant advancements in complex multimodal tasks. However, the practical application of LVLMs is heavily hindered by hallucination phenomena [8, 9], which refer to situations where objects in image descriptions generated by LVLMs are inconsistent with the provided visual content. Considerable efforts have been dedicated to both evaluation [9-11] and mitigation [12-14] of hallucination phenomena, leading to notable advancements.

A significant challenge in object hallucination evaluation arises from the effect of instructions on object hallucinations [9]. Overcoming this challenge, existing object hallucination evaluation methods typically adopt an average-based framework, which averages the results obtained from a set of instructions. However, as shown in Figure 1, this framework fails to provide consistent evaluation across instruction sets that generate image descriptions of significantly varying lengths. Specifically,

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InstructBLIP

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Otter

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while evaluation results of LVLMs remain consistent across certain instruction sets (e.g., set 1 and set 2), inconsistencies arise when comparing instruction sets with significantly different average image description lengths (e.g., set 2 and set 3).

In this paper, we present the first systematic investigation into the effect of instructions on object hallucinations in LVLMs, with a specific focus on the role played by the length of image descriptions. Technically, we evaluate lengths and object hallucination degrees (measured by CHAIR scores) of the image descriptions generated by LVLMs under different instructions (see Section 3.1 for more details). The experimental results are shown in Figures 2 & 3, from which we can observe that the degree of object hallucination is primarily influenced by the length of image descriptions, with instructions only indirectly affecting hallucinations through their effect on description lengths. The longer the image description, the higher the object hallucination degree, and there is a clear linear relation between them. Hence, it is imperative to take into account the length of image descriptions in hallucination evaluation. Unfortunately, the average-based framework can only select instructions, without the ability to directly control the length of image descriptions.

Motivated by the findings, we propose a fine-grained evaluation framework called LeHaCE, which fits an informative length-hallucination curve to evaluate object hallucinations at any given image description length within a large range. LeHaCE evaluates the object hallucination degree at a uniform image description length to mitigate the effect of image description length, ensuring stable evaluations for the same LVLM across different instruction sets and fair comparisons among different LVLMs. Moreover, LeHaCE incorporates the curve slope as an innovative hallucination evaluation metric, reflecting the extent to which the object hallucination degree is influenced by the image description length, achieving a more comprehensive evaluation. Experiment results on 12 representative LVLMs show that LeHaCE can evaluate object hallucinations of LVLMs in a more stable, fair, and comprehensive way.

The main contributions of this paper are summarized as follows:

We conduct the first systematic investigation into the effect of instructions on object hallucinations in LVLMs and find that the degree of object hallucinations is primarily influenced by the length of image descriptions, with instructions only indirectly affecting hallucinations through their effect on image description lengths.

We propose an object hallucination evaluation framework called LeHaCE, which fits an informative length-hallucination curve to evaluate object hallucination at a uniform image description length, realizing a more stable and fair evaluation.

We employ the curve slope as an innovative hallucination evaluation metric, reflecting the extent to which the object hallucination degree is affected by the image description length, achieving a more comprehensive evaluation.

2 Related work

2.1 Large Vision-Language Models

Inspired by the success of LLMs in NLP [1-3], researchers have extended LLMs to multimodal tasks [15-28], proposing numerous LVLMs and achieving new advancements [7, 14, 29-35]. These LVLMs align the multi-modal encoders with LLM through multitask fine-tuning and instruction fine-tuning on multi-modal datasets, enabling LLM to acquire multi-modal perception and instructionfollowing capabilities. Specifically, to integrate multimodal features, Flamingo [29] proposes a cross-attention structure to achieve arbitrary interleaved multi-modal feature fusion. BLIP-2 [35] introduces Q-Former to bridge the visual backbone model and LLM. mPLUG-Owl2 [36] introduces a modality adaptive module to facilitate the fusion between different modules. To enhance generalization and improve instruction-following capabilities, some methods [4-6, 12, 37] propose multi-task fine-tuning and instruction fine-tuning for LVLMs. Among them, LRV-instruction [12], MiniGPT-4 [5], LLaVA [6] and SViT [37] employ ChatGPT to augment instruction data. To mitigate the risk of catastrophic forgetting of language knowledge during the training process, mPLUG-Owl [38] and LLaVA-1.5 [6] perform joint training on pure language and visual-language instructional data. More recently, mPLUG-DocOwl [31], InternLM-XComposer [32], Kosmos-2 [33], Shikra [34], Cantor [39], BuboGPT [30], and Qwen-VL [7] further enhance the capabilities of LVLMs in optical character recognition, document understanding, multi-modal interleaved composition and visual grounding.

2.2 Hallucination in LVLMs

Works on the hallucination in LVLMs focus on two aspects: evaluation and mitigation. For the hallucination evaluation, POPE [9] designs a polling-based query method to avoid the influence of instructions on hallucination evaluation. By presenting LVLMs with brief "yes" or "no" questions regarding the target of detection, the evaluation of hallucination is transformed into a simple binary classification task. NOPE [10] designs a novel benchmark to evaluate the performance of LVLMs in recognizing the non-existence of objects in visual questions. AMBER [11] designs a multi-dimensional LVLMs hallucination evaluation benchmark without LLMs, targeting existence, attribute, and relation hallucination. For the hallucination mitigation, LRV-Instruction [40] creates a balanced set of positive and negative instructions to perform robust visual instruction adjustment for LVLMs. VIGC [14] employs an iterative approach to generate detailed and accurate answers gradually. Woodpecker [13] proposes a postprocessing method that utilizes expert models to locate and correct hallucinations from generated text.

While existing methods [9] observe that the hallucination degree of LVLMs is unstable across different instructions, this phenomenon has not been thoroughly investigated to date. This work presents the first comprehensive study investigating the influence of instructions on the hallucination rate of LVLMs. Building upon our findings, we propose LeHaCE framework, which can evaluate hallucination of LVLMs in a more stable and comprehensive manner. Contrary to polling-based query methods, LeHaCE can directly evaluate the hallucination rate of image descriptions generated by LVLMs, which is more in line with the practical application scenarios of LVLMs.

3 Hallucination of LVLMs Under Different Instructions

This section provides the investigation into the effect of instructions on hallucinations, with a specific focus on the role played by the length of image descriptions. The experimental settings are presented initially, followed by a comprehensive analysis of the experimental results

3.1 Experimental Settings

In this investigation, twelve popular LVLMs are included, namely Gemini-Pro-Vision pro [2], QwenVL [7], MiniGPT-4 [5], LLaVA [6], InstructBLIP [4], LLaMA-Adapter-v2 [41], mPLUG-Owl2 [36], mPLUG-Owl [38], InternLM-XComposer [32], VPGTrans [42], Otter [43] and Lynx [44]. All LVLMs are prompted by 25 different instructions to generate image descriptions for 256 images in MSCOCO [45]. All descriptions are generated using beam search with a beam size of 5. For the instructions, we utilize those from [4] and additionally propose several others, as detailed in the appendix. We use CHAIR [8] as the evaluation metric for hallucinations, which has two variants: CHAIRI and CHAIRS. Given the ground truth objects in the image, CHAIRI calculates the proportion

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InstructBLIP

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y=0.054x + 6.135

LLaVA

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mPLUG-Owl

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Otter

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InternLM-XComposer

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mPLUG-Owl2

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Lynx

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Qwen-VL

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InstructBLIP

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Otter

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VPGTrans

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InternLM-XComposer

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mPLUG-Owl2

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Lynx

Average description Length (words)

Linear Fit

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of objects that appear in the descriptions but not in the image, while CHAIRS is the proportion of descriptions that include hallucination. Formally, CHAIRI and CHAIRS can be expressed as follows:

For more experimental settings, we use the Pearson correlation coefficient to measure the correlation between the average length and the hallucination rate of image descriptions. Lengths are measured in word count.

3.2 Experimental Analysis

The results are presented in Figure 2 & 3, from which we get two key observations: 1) Figure 2 shows the relationship between the hallucination rate and the average image description length, we can observe that the hallucination rate increases with the average image description length and there is a clear linear correlation between them. Specifically, the Pearson correlation coefficient between hallucination rates and the average image description lengths exceeds 0.6 for all LVLMs, with 10 LVLMs exceeding 0.8 and 5 LVLMs exceeding 0.9. 2) Figure 3 shows the impact of instructions on the length of image descriptions generated by LVLMs, from which we can observe that the length of image descriptions generated by the same LVLM with

different instructions can vary significantly, e.g., Gemini-Pro-Vision with Instruction 11 (101 words in average) v.s. Gemini-Pro-Vision with Instruction 18 (20 words in average). Furthermore, the length of image descriptions generated by different LVLMs with the same instruction can also differ greatly, e.g., MiniGPT-4 (11 words in average) v.s. Gemini-ProVision (97 words in average) with Instruction 17.

Based on the aforementioned two observations, we can draw the following conclusions: 1) The degree of object hallucinations is primarily influenced by the length of image descriptions, with instructions only indirectly affecting hallucinations through their effect on image description lengths. Hence, it is imperative to take into account the length of image descriptions in hallucination evaluation. However, controlling the length of image descriptions generated by LVLMs is challenging 2 , given that even subtle semantic differences between instructions can significantly impact the output length of LVLMs (shown in Figure 3). 2) In addition to the hallucination degree , the rate at

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which hallucination degree increase with description length is also a meaningful indicator for characterizing the nature of LVLMs hallucinations. Considering both the hallucination degree and the growth rate of hallucination degree can provide a more comprehensive evaluation for hallucinations in LVLMs. For example, as shown in the Figure 2, although InstructBLIP has the lowest hallucination degrees in short image descriptions, it exhibits high instability with a rapid increase in hallucination degrees, resulting in high hallucination in long image descriptions.

4 Length-Hallucination Curve Based Hallucination Evaluation Framework

In this section, we first introduce the average-based hallucination evaluation framework and discuss its limitations. Then, we elaborate on the proposed LeHaCE framework and evaluate representative LVLMs with LeHaCE. Finally, the stability of LeHaCE is analysed.

4.1 Average-Based Hallucination Evaluation Framework

The average-based hallucination evaluation framework mitigates the challenge caused by instructions by averaging the hallucination rates over different instructions. Formally, the hallucination rates and average lengths of the image descriptions generated by the LVLM under N instructions are denoted as { glyph[lscript] i , hr i } N i =1 . The average hallucination rate ¯ hr and average length ¯ glyph[lscript] of image descriptions over all instructions can be calculated as follows: ¯ hr = 1 N ∑ N i =1 hr i and ¯ glyph[lscript] = 1 N ∑ N i =1 glyph[lscript] i . The average-based hallucination evaluation framework utilizes ¯ hr to evaluate the hallucination of LVLMs.

However, due to substantial variations in the average lengths of image descriptions generated by different instruction sets, the average-based framework struggles to mitigate the effect of image description lengths on object hallucinations, resulting in unstable and unfair evaluations. Specifically, as shown in Figure 4 (left), when the average-based framework evaluates an LVLM under different instruction sets, the inconsistent average image description lengths lead to unstable evaluation. Moreover, Figure 4 (right) shows that when the average-based framework evaluates different LVLMs under the same instructions, the inconsistent average image description lengths lead to unfair evaluation.

4.2 Length-Hallucination Curve Based Hallucination Evaluation Framework

Section 3.2 reveals the significant effect of image description lengths on the hallucination degree. To mitigate this effect, it is crucial to control the description length during the hallucination evaluation.

where β and α are:

Unequal average lengths make

ABF

unstable

LeHaCE

is more stable

LeHaCE

compare at

the same length

Slope: 0.15

Slope: 0.05

Unequal average

lengths make

ABF

unfair

However, controlling the length of generated image descriptions is challenging because LVLMs are highly sensitive to instructions. To address this challenge, we fit a length-hallucination curve to evaluate LVLMs at any desired length. Specifically, based on the clear linear correlation observed in Section 3.2, we assume a linear correlation between image description lengths and hallucination rates of LVLMs. Figure 4 intuitively illustrates the LeHaCE framework.

Formally, we use { glyph[lscript] i , hr i } N i =1 to represent the average lengths and hallucination rates of image descriptions generated by the LVLM under N instructions. The linear regression curve of { glyph[lscript] i , hr i } N i =1 , which we refer to as the Length-Hallucination Curve (LHC), can be formalized as follows:

Length-hallucination curve summarizes the trend between the hallucination rate and the description length. The regression coefficient β represents the rate at which the hallucination rate increases with the growth of description length. LeHaCE uses the LHC to evaluate the hallucination in LVLMs. LeHaCE consists of two metrics:

where LeHaCE ( glyph[lscript] ) measures the hallucination rate at the specified length glyph[lscript] , and LeHaCE GR measures the rate at which the hallucination rate increases with the increase in description length. Note that LeHaCE can be built upon any hallucination degree evaluation metric, enhancing their stability, fairness, and comprehensiveness. In this paper, we use CHAIR as the metric for measuring the hallucination degree.

Compared to the average-based hallucination evaluation framework, LeHaCE has three advantages. As intuitively shown in Figure 4, LeHaCE can evaluate the hallucination degree of LVLMs at a uniform image description length, thereby mitigating the influence of description length on hallucination degree and resulting in a more stable and fair evaluation. Moreover, LeHaCE can evaluate the hallucination degree at multiple lengths and the growth rate of hallucination degree, leading to a more comprehensive evaluation.

4.3 Evaluation on MSCOCO and NoCaps

We evaluate twelve LVLMs with LeHaCE at lengths of 20, 40, 60, and 80 words. This evaluation is conducted on subsets of the MSCOCO [45] test set and the NoCaps [46] validation set, each compris-

ing randomly selected 256 images. The length-hallucination curve in LeHaCE is fitted on the CHAIR scores of image descriptions generated by 25 different instructions. To calculate CHAIR scores on NoCaps, we follow the setting proposed in [8, 47]. All descriptions are generated using beam search with a beam size of 5. The experiments are conducted with PyTorch on Nvidia GeForce RTX 3090 GPUs.

The results are shown in Table 1, which demonstrate that LeHaCE can evaluate the object hallucination degree of LVLMs at given image description lengths, as well as the growth rate of the hallucination degree, providing a fair and comprehensive evaluation. Specifically, 1) For short descriptions, InstructBLIP achieves the best performance on both the MSCOCO and NoCaps datasets. However, its higher growth rate of hallucination degree leads to poor performance on longer descriptions. 2) For medium-length and long descriptions, Gemini-Pro-Vision and Qwen-VL exhibit the best performance on the MSCOCO and NoCaps datasets, respectively. This is attributed to their relatively small growth rate in hallucination degree. 3) Gemini-Pro-Vision and LLaVA exhibit the lowest growth rate in hallucination degree on the MSCOCO and NoCaps datasets, respectively.

In Table 1, LVLMs exhibit higher degrees of hallucination on the NoCaps dataset compared to the MSCOCO dataset. This is attributed to the fact that LVLMs typically use the MSCOCO for training, making the NoCaps dataset an out-of-distribution dataset. The results show that the distributional differences not only increase the hallucination degree of LVLMs at various description lengths but also amplify the growth rate of hallucination degree.

4.4 Stability of LeHaCE

As mentioned above, LeHaCE evaluates the hallucination degree of LVLMs in a more stable manner. This subsection verifies the stability of the proposed LeHaCE framework. Specifically, LVLMs are prompted by three sets of different instructions to generate three sets of image descriptions. Each instruction set consists of multiple instructions randomly drawn from a pool of 25 instructions, with no overlap between instructions in different sets. The image descriptions generated by different instructions in each set are evaluated using the LeHaCE framework and the average-based framework, respectively. The stability of the LeHaCE and the average-based frameworks on the three sets of image descriptions is evaluated using the Relative Standard Deviation (RSD) , which is defined as the ratio of the standard deviation σ to the mean µ , RDS = σ/µ . The lower the RSD, the more stable

the results. For the number of instructions in each instruction set, we conduct extensive experiments under five different conditions: 3, 4, 5, 6, 7, and 8. The experiments are carried out 10 times using distinct instruction sets, and the final results are determined by averaging the outcomes of these 10 experiments.

The results are shown in Table 2, from which we can observe that LeHaCE demonstrates superior stability compared to the average-based framework in nearly all cases. Notably, 1) On the MSCOCO dataset, for the CHAIRI metric, LeHaCE consistently outperforms the average-based framework across all twelve LVLMs when the number of instructions reaches five or more. Similarly, for the CHAIRS metric, LeHaCE exhibits superior performance across all twelve LVLMs when the number of instructions reaches four or more. 2) On the NoCaps dataset, when the number of instructions reaches four or more, LeHaCE consistently outperforms the average-based framework across all twelve LVLMs on both CHAIRI and CHAIRS metrics. In Table 2, we observe that when the number of instructions is very low, such as three, the stability of LeHaCE is compromised due to the difficulty in accurately fitting the length-hallucination curve. However, with just four or five instructions, LeHaCE consistently exhibits superior stability.

To verify the validity of the linear assumption we make in the LeHaCE method, we evaluate the stability of LeHaCE with different fitting methods. Specifically, we assess the RSD of LeHaCE on the MSCOCO dataset when applying linear, quadratic, and cubic fitting. As shown in Table 3, the results indicate that linear fitting significantly outperforms polynomial fitting, particularly when the instruction count is low.

20

40

60

80

100

120

Average Description Length (words)

0.00

0.05

0.10

0.15

0.20

0.25

Avg. RSD of

CHAIRI

LLaVA

20

40

60

80

100

120

Average Description Length (words)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Avg. RSD of

CHAIRS

20

40

60

80

100

120

Average Description Length (words)

0.00

0.05

0.10

0.15

0.20

0.25

Avg. RSD of

CHAIRI

Qwen-VL

20

40

60

80

100

120

Average Description Length (words)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Avg. RSD of

CHAIRS

with AEF

with LeHaCE

5 Insts.

6 Insts.

7 Insts.

8 Insts.

For the stability of LeHaCE at different lengths, the results are shown in Figure 5, from which we can see that LeHaCE significantly improves the stability of the CHAIR metrics across a wide range of description lengths. All of these experimental results validate the superior stability of LeHaCE.

5 Conclusion and Limitations

Conclusion : In this paper, we find the degree of object hallucinations is primarily influenced by the length of image descriptions, with instructions only indirectly affecting hallucinations through their effect on image description lengths. The degree of object hallucination and the length of image descriptions exhibit a clear positive linear correlation. Based on our findings, a stable, fair and comprehensive object hallucination evaluation framework named LeHaCE is introduced. Extensive experimental results validate the superiority of LeHaCE over existing frameworks.

Limitations : Despite exhaustive investigations, this work still has potential limitations. 1) We focus on object hallucination, leaving other types of hallucinations for future work. 2) Due to computational constraints, we evaluate LVLMs on only a subset of each dataset. Nevertheless, we conduct thorough experiments across various datasets to validate our findings and method. 3) Due to high API fees, we only explore one proprietary business LVLM in our experiments. However, we conduct in-depth analyses on eleven open-source LVLMs, validating the broad applicability of our method. 4) In the typical practice of evaluating hallucination levels in LVLMs, multiple instructions are usually used to enhance the stability of the evaluation results. Although LeHaCE cannot be used with just one instruction, this limitation does not affect its ability to provide stable evaluations.

6 Acknowledgments

This work was supported in part by the National Key R&D Program of China (2021YFF0900500), and the National Natural Science Foundation of China (NSFC) under grants U22B2035 and 62441202.

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7 Technical Appendices

7.1 Instructions

Weprimarily referred to the instructions from [4] and additionally designed some instructions, totaling 25 in number.

I 1 :'Describe the image in one sentence.',

I 2 :'Summarize the image in a single sentence.',

I 3 :'Give a one-sentence depiction of the image.',

I 4 :'Provide a concise sentence describing the image.',

I 5 :'Give a brief summary of the image in a single sentence.',

I 6 :'Describe this image in short.',

I 7 :'Describe this image in a few words.',

I 8 :'Provide a brief caption for this image.',

I 9 :'Provide a short caption for this image.',

I 10 :'Briefly describe the content of the image.',

I 11 :'Describe this image.',

I 12 :'What does the image show?',

I 13 :'What can you see in the image?',

I 14 :'What is described in the image?',

I 15 :'Provide a caption for this image.',

I 16 :'Describe the objects in this image.',

I 17 :'Can you provide a description of the image?',

I 18 :'What objects or subjects are present in the image?',

I 19 :'Describe this image in detail.',

I 20 :'Describe this image in extremely detail.',

I 21 :'Provide a detailed description of this image.',

I 22 :'Can you describe the scene in the image in great detail?',

I 23 :'Give a thorough account of what is depicted in this image.',

I 24 :'Provide an elaborate and comprehensive analysis of this image.',

I 25 :'Give a comprehensive and in-depth description of what is shown in this image.',

7.2 Further Exploration

Why does the hallucination rate of MLLMs increase with the increase in image description length? The underlying reasons behind this phenomenon are difficult to determine, as the output of MLLMs is influenced by multiple factors such as visual encoders, language models, and training data. In this section, we aim to shed light on this phenomenon by delving into an analysis of common hallucination patterns in long image descriptions.

As shown in Figure 7, We found that hallucinations are more likely to occur after some words or phrases that indicate enumeration or introduce additional information, such as "in addition", "addition to", "additionally", "include", "including", "such as", "as well" and "also". We refer to these words/phrases as "hallucinogenic words". As shown in Figure 6 left, we performed a statistical analysis on a subset of MSCOCO dataset comprising 256 images to investigate the hallucination rate of image descriptions containing hallucinogenic words. The experimental results demonstrate a notable increase in the hallucination rate of image descriptions that incorporate hallucinogenic words, compared to descriptions that lack such words. This phenomenon was consistently observed across all twelve MLLMs.

MiniGPT-4

InstructBLIP

LLaVA

InternLM-XC

VPGTrans

Otter

LLaMA-Adapter-v2

Lynx

Qwen-VL

mPLUG-Owl2

mPLUG-Owl

Gemini

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

CHAIRS

NaN

Descriptions including "in addition", "addition to" and "additionally"

Descriptions including "include" and "including"

Descriptions including "such as"

Descriptions including "as well"

Descriptions including "also"

Other Descriptions

Therefore, we propose a hypothesis that MLLMs are more likely to employ hallucinogenic words in generating lengthy and detailed image descriptions, resulting in a higher hallucination rate. To validate our hypothesis, we explored the relationship between the occurrence frequency of hallucinogenic words and the average length of image descriptions. The image descriptions with varying average lengths were generated by different instructions. The results are depicted in Figure 6 right. As the length of the description increases, the number of incorrect descriptions containing hallucinogenic words increases and constitutes a significant portion of all incorrect descriptions. This validates our hypothesis."

We further explored whether the hallucination rate of MLLMs can be reduced by disabling hallucinogenic words. Specifically, we prohibited the use of previously mentioned hallucinogenic words ("in addition," "addition to," "additionally," "include," "includes," "including," "such as," "as well," "also") during the generation process in the MLLMs, ensuring that the generated image descriptions did not contain these hallucinogenic words. The results, presented in Table 4, clearly demonstrate that disabling hallucinogenic words can significantly decrease the hallucination rate without compromising the quality of image descriptions. Furthermore, it helps alleviate the tendency for the hallucination rate to increase as the description length grows. These experimental findings not only highlight the substantial impact of hallucinogenic words on the hallucination rate of MLLMs but also offer valuable insights for mitigating hallucination in MLLMs.

7.3 More Experimental Results

The average length and CHAIR scores of image descriptions generated by 12 MLLMs (Gemini-ProVision pro [2], Qwen-VL [7], MiniGPT-4 [5], LLaVA [6], InstructBLIP [4], LLaMA-Adapter-v2 [41], mPLUG-Owl2 [36], mPLUG-Owl [38], InternLM-XComposer [32], VPGTrans [42], Otter [43] and Lynx [44]) under 25 instructions on subsets of MSCOCO [45] and NoCap [46] are shown in Table 5 and Table 6, respectively.

The hallucination degree of each MLLM with hallucinogenic words disabled are shown in Table 4 and Table 8. Disabling hallucinogenic terms can effectively alleviate hallucinations in MLLMs.

7.4 More Case Studies

Figure 7 showcases examples of image descriptions generated by MLLMs before and after disabling hallucinogenic words.

Ins

MiniGPT-4

InstructBLIP

Lynx

LLaVA

Otter

VPGTrans

CI

CS

CI

CS

CI

CS

CI

CS

CI

CS

CI

CS

I1

17.29

3.76

6.64

43.52

5.63

14.84

34.97

6.86

17.58

18.70

7.40

14.84

26.48

10.78

20.31

15.92

3.75

6.25

I2

17.62

2.99

5.08

11.39

1.18

1.56

38.17

4.70

12.50

19.41

8.02

16.02

32.30

12.35

26.17

15.20

5.17

7.81

I3

20.83

5.54

9.38

10.96

0.97

1.56

36.69

6.10

15.23

18.12

5.33

10.16

27.01

10.95

20.70

15.30

4.45

6.25

I4

25.26

5.06

8.59

11.00

0.96

1.56

35.68

4.91

13.28

18.69

6.79

14.06

51.97

13.33

35.94

16.55

5.02

7.42

I5

21.76

5.31

9.77

10.82

0.95

1.56

38.19

6.50

18.36

21.14

6.91

14.06

35.20

12.04

25.39

15.65

4.94

7.42

I6

51.10

8.70

22.27

84.66

9.50

33.59

38.46

7.37

19.92

46.25

9.87

26.95

69.41

18.35

48.83

49.00

8.06

20.31

I7

39.39

6.36

16.02

76.02

7.81

28.52

40.21

6.75

17.19

31.38

6.98

16.41

58.12

14.33

40.63

24.34

7.31

12.89

I8

48.46

7.75

19.53

11.01

0.96

1.56

37.14

6.36

17.19

45.61

9.00

21.09

54.59

13.33

33.98

23.71

5.97

8.59

I9

42.79

6.85

16.80

10.86

1.20

1.95

36.79

5.56

14.84

45.55

9.08

21.48

53.70

13.48

33.59

16.83

4.20

5.47

I10

50.16

7.95

21.88

10.92

1.21

1.95

44.59

7.10

19.14

27.44

6.92

14.06

57.01

14.34

41.80

46.24

7.78

17.97

I11

63.67

7.67

23.44

90.46

10.02

35.55

46.65

6.85

19.92

57.75

8.96

27.34

78.25

21.68

62.11

68.75

8.19

22.27

I12

34.10

6.74

14.06

24.25

4.10

8.98

70.71

10.15

29.69

19.06

6.88

13.28

1.63

1.57

1.17

23.51

6.58

12.50

I13

41.19

6.93

16.41

40.76

4.94

13.28

68.39

11.36

31.25

26.33

7.99

16.41

51.03

16.36

40.23

39.59

7.91

17.97

I14

41.82

7.75

19.53

26.76

4.24

9.38

66.39

10.57

28.13

20.92

5.60

10.94

16.19

8.53

10.94

26.46

7.18

12.50

40.84

7.84

17.97

11.11

0.95

1.56

41.04

6.67

17.58

60.71

7.42

18.75

63.95

15.54

35.94

19.73

5.18

7.03

I16

55.95

9.05

21.48

81.48

8.72

34.77

50.32

8.10

23.05

33.21

10.80

23.05

60.63

20.29

55.47

58.06

9.44

24.61

I17

67.46

7.79

22.66

11.02

0.96

1.56

58.48

9.82

26.95

41.94

8.23

20.70

59.64

14.71

39.06

46.72

8.25

10.55

I18

29.29

6.62

11.33

18.74

3.12

5.86

61.49

13.40

32.81

18.98

8.58

19.14

23.50

15.92

32.81

21.18

6.46

10.94

I19

75.31

7.31

24.22

98.68

14.29

52.34

60.34

8.68

26.95

84.97

10.48

35.55

81.89

20.26

61.33

93.45

8.83

28.52

I20

87.63

8.42

28.13

100.46

16.05

55.86

61.69

9.23

27.73

94.15

11.56

37.11

86.79

21.36

63.28

102.59

10.16

33.59

I21

77.58

9.04

27.34

94.07

14.57

51.56

53.25

9.06

25.78

89.18

11.34

36.33

82.18

21.09

66.80

92.48

9.80

29.69

I22

80.14

11.14

32.42

100.25

16.70

58.20

84.23

14.88

39.45

81.75

12.71

37.89

70.52

13.41

41.02

85.76

10.29

28.13

I23

74.70

9.87

26.95

12.10

1.34

1.95

72.46

11.17

32.03

87.27

9.66

30.08

80.48

21.49

58.20

79.47

9.05

29.30

I24

94.29

8.51

29.69

96.74

11.44

43.36

78.71

11.99

35.55

112.24

12.01

37.89

90.93

26.89

80.47

101.60

8.54

31.25

I25

78.88

9.85

30.47

77.02

8.46

30.86

53.86

8.02

21.48

96.59

10.62

37.50

83.79

21.50

60.16

85.68

9.40

30.47

Ins

LLaMA-Adapter-v2

mPLUG-Owl

Gemini-Pro-Vision

InternLM-XComposer

Qwen-VL

mPLUG-Owl2

CI

CS

CI

CS

CI

CS

CI

CS

CI

CS

CI

CS

I1

30.59

8.56

18.36

22.71

7.19

12.50

15.13

4.00

6.25

14.17

4.23

7.03

18.10

2.26

4.69

9.96

2.37

2.73

I2

23.19

7.02

15.23

18.71

5.26

9.77

14.06

3.90

5.86

13.44

3.90

6.25

18.04

3.08

5.86

10.04

2.33

3.13

I3

20.00

7.22

13.67

15.98

4.36

7.42

14.70

3.84

6.25

13.98

3.93

6.64

18.89

3.00

5.86

10.04

2.37

3.13

I4

18.61

6.70

12.89

15.36

3.89

6.25

14.54

3.19

5.08

14.93

4.46

7.42

18.34

3.02

5.86

9.93

2.35

3.13

I5

32.91

9.76

21.09

23.90

6.81

12.89

15.41

2.62

4.30

13.90

4.31

7.42

18.00

2.55

4.69

10.00

2.10

2.73

I6

79.85

19.07

52.73

87.25

21.27

57.42

19.84

2.92

5.47

22.51

7.07

11.33

49.76

6.03

17.58

64.30

12.32

33.98

I7

72.24

16.46

44.92

72.70

18.32

48.83

10.77

1.70

2.34

22.23

4.77

9.38

27.00

4.22

9.77

41.52

8.96

21.09

I8

60.97

11.34

31.25

32.95

12.30

20.70

14.33

4.65

6.64

19.11

6.57

9.77

25.70

4.98

9.77

9.73

2.43

3.13

I9

46.49

10.43

26.17

16.91

6.21

9.38

12.78

3.14

3.52

17.54

4.83

7.03

19.36

5.63

9.38

9.72

2.39

3.13

I10

44.54

8.68

22.27

74.50

17.38

45.31

26.22

3.50

8.20

19.41

4.17

6.64

49.01

5.89

19.14

70.53

13.27

39.06

I11

87.73

19.38

58.98

101.23

21.65

59.77

42.68

6.18

15.23

59.15

9.73

25.78

75.04

8.47

28.91

91.07

17.04

57.03

I12

20.29

5.01

8.98

82.36

18.70

47.66

28.59

6.07

12.11

40.42

6.94

17.58

19.64

2.50

4.69

16.80

3.03

5.08

I13

29.50

4.96

11.33

70.57

17.19

41.41

33.11

5.48

14.06

38.90

8.28

18.36

28.87

3.74

6.64

30.76

4.45

10.94

I14

18.46

4.75

8.98

88.11

18.30

52.34

31.86

5.46

12.11

27.32

6.46

11.72

42.22

5.58

15.63

34.97

5.77

13.67

69.86

11.92

34.77

46.09

14.23

26.17

18.38

4.43

5.08

17.88

5.94

9.77

48.83

7.73

20.31

9.80

2.40

3.13

I16

53.31

12.15

31.25

64.19

18.58

40.63

45.50

6.45

16.02

56.87

10.90

32.03

66.16

7.47

26.95

70.91

12.04

39.06

I17

59.07

8.18

25.78

96.80

21.08

61.33

50.84

7.69

18.36

37.50

8.99

18.36

66.72

8.30

26.95

83.73

14.85

50.39

I18

19.68

9.59

20.70

19.64

8.86

13.67

30.73

6.83

12.89

31.91

8.78

20.31

17.07

3.00

6.25

16.44

2.82

5.08

I19

89.76

19.98

60.55

101.93

22.28

64.84

62.08

6.05

19.92

66.83

10.52

32.81

87.10

9.58

36.72

93.40

16.99

63.67

I20

90.54

20.21

59.77

103.02

21.63

64.45

85.19

7.93

25.39

71.45

11.06

32.03

92.94

9.78

39.45

96.84

18.89

67.19

I21

88.58

19.37

58.98

100.25

21.52

61.72

63.95

6.35

18.75

72.29

11.64

35.55

86.27

10.87

39.06

90.73

15.76

59.77

I22

84.65

8.48

31.25

105.22

22.39

60.55

79.56

9.56

31.25

49.08

7.29

19.53

87.15

7.75

28.13

80.87

13.73

44.92

I23

85.40

13.11

38.67

103.27

21.98

61.72

74.96

5.97

18.75

69.35

11.94

35.55

85.61

12.44

44.92

89.39

15.99

55.47

I24

92.66

22.14

60.94

106.13

21.34

66.41

134.82

7.18

25.39

60.96

9.95

27.34

105.59

10.84

45.70

99.26

16.47

60.55

I25

83.82

13.31

40.23

102.21

21.93

60.94

81.99

6.51

21.09

69.60

11.75

35.16

95.62

10.95

42.19

92.32

15.90

53.91

7.5 Broader Impacts

The LeHaCE framework provides a stable, fair, and comprehensive way to evaluate object hallucinations in large vision-language models. It helps in assessing the usability of large vision-language models, thereby helping to prevent safety incidents.

We acknowledge the potential ethical concerns associated with the use of the MSCOCO dataset, particularly regarding data privacy, copyright, and consent, as the images in this dataset were collected from Flickr without explicit user consent. However, it is important to note that our study's methodology and findings are independent of the dataset used. Our research focuses on evaluating the relationship between instruction length and hallucinations in Large Vision-Language Models (LVLMs), and does not rely on or alter the underlying data in the MSCOCO dataset. Nonetheless, we recognize the ethical implications of using such datasets and recommend future research to continue exploring these issues in greater depth.

Instruction

MiniGPT-4

MiniGPT-4

w/o HW

Can you describe the scene in the image in great detail?

Yes, I can describe the scene in the image in great detail. The image shows a pizza with a variety of

toppings, including whipped cream, bananas, and chocolate chips. The pizza is on a wooden platter

and appears to have been served on a table with a white tablecloth. There are also some utensils, such

as forks and knives, on the table. The overall atmosphere of the scene appears to be casual and relaxed.

Yes, I can describe the scene in the image in great detail. The image shows a dessert plate with a piece

of pizza on it. The pizza has a crispy crust and is topped with whipped cream, bananas, and chocolate

chips. There are also some crumbs on the plate. The plate is on a wooden table with a white tablecloth.

There are two glasses of water on the table next to the plate. The room is dimly lit, and there is a

chandelier hanging from the ceiling.

Instruction

MiniGPT-4

MiniGPT-4

w/o HW

Describe this image in a few words.

This image shows a man sitting at a desk with a laptop computer in front of him. The man is wearing

glasses and has his hands on the keyboard. There are several other electronic devices on the desk,

including a cell phone and a headset. The background is cluttered with wires and other electronic

equipment.

The image shows a man sitting at a desk with a laptop computer in front of him. The man is wearing a

blue shirt and has glasses on. There are several other electronic devices on the desk and on the wall

behind him.

In the image, a blender is placed on a wooden table. The blender contains a variety of fruits and

vegetables, which are blended together to create a refreshing drink. The blender appears to be in the

process of blending the ingredients, as some of the fruits and vegetables are visible at different stages

of blending. The blender is positioned close to the edge of the table, allowing for easy access to the

ingredients and the finished product. The wooden table provides a natural and rustic setting for the

blender, adding to the overall ambiance of the scene.

Instruction

InstructBLIP

InstructBLIP

w/o HW

Can you describe the scene in the image in great detail?

In the image, a blender is placed on a wooden table. The blender contains a variety of ingredients,

including fruits and vegetables, such as apples, oranges, bananas, and carrots. These ingredients are

mixed together in the blender, creating a refreshing and healthy drink. The blender is positioned in the

center of the table, surrounded by various fruits and vegetables. There is also a bowl nearby, possibly

containing additional ingredients for the blended drink.

Instruction

InstructBLIP

InstructBLIP

w/o HW

Can you describe the scene in the image in great detail?

The image depicts a man in a suit standing next to a pile of luggage, which is decorated with colorful

ribbons. He appears to be posing for a photo with the luggage as a backdrop. There are several other

people in the scene, some of whom are seated on benches or standing around the area.  In addition to

the man and the luggage, there are various objects scattered throughout the scene, including a handbag,

a cell phone, and a bottle of water. A chair can be seen in the foreground, close to the man and the

luggage.

The image depicts a man in a suit standing next to a pile of luggage. He appears to be posing for a

photo with the luggage, which is decorated with colorful ribbons and bows. There are several other

people in the scene, some of whom are seated on benches or standing around the area. One person is

closer to the man with the luggage, while others are positioned further away from him. In total, there

are at least six people visible in the scene.

Instruction

LLaVA

LLaVA

w/o HW

Describe the objects in this image.

The image features a busy street with a large group of people riding motorcycles. There are several

motorcycles parked on the side of the road, and a few people are riding them. Additionally, there are

cars and a truck on the street, indicating a mix of different types of vehicles. The street is lined with

trees, which adds to the overall atmosphere of the scene.

In the image there are several motorcycles and a car driving down a street. The street is lined with trees

and has a sidewalk where people are walking.

The image features a pizza with a generous amount of ice cream on top of it. The pizza is placed on a

wooden table or countertop. There are several people present in the scene who are likely enjoying the

pizza and the ice cream. The pizza is topped with whipped cream and chocolate sauce. The

combination of pizza and ice cream is an unconventional and creative dessert choice that is likely to be

a delightful treat for those who enjoy both pizza and ice cream.

Instruction

LLaVA

LLaVA

w/o HW

Describe the objects in this image.

The image features a pizza with ice cream on top of it, served on a wooden table. The pizza is topped

with a generous amount of ice cream, which is dripping down the sides of the pizza. The ice cream is

likely to be a combination of flavors, contributing to the overall delicious appearance of the dish. The

table is set with utensils, such as a fork and a spoon, and there are also cups and bowls on the table,

suggesting that the pizza might be accompanied by additional beverages or side dishes.

Instruction

w/o HW

Describe this image in detail.

The image depicts a lush green field where a group of giraffes is enjoying their time. There are five

giraffes visible in the scene, some of them standing close to each other while others are spread out.

One of the giraffes appears to be walking or running across the field, adding movement and liveliness

to the scene. In addition to the giraffes, there are two birds visible at the far left and far right edges of

the image, adding diversity to the wildlife present in this environment.

The image depicts a lush green field where a group of giraffes is enjoying their time. There are five

giraffes visible in the scene, some of them standing close to each other while others are spread out.

One of the giraffes appears to be walking or running across the field, adding movement and liveliness

to the scene.In addition to the giraffes, there are two birds visible at the far left and far right edges of

the image, adding diversity to the wildlife present in this environment.

Instruction

w/o HW

Describe the image in one sentence.

The image shows a counter filled with a variety of groceries, including lettuce, broccoli, cheese, and

other ingredients.

The image shows a counter covered with a variety of fresh vegetables, salad, cheese, and other

ingredients.

Ins

MiniGPT-4

InstructBLIP

Lynx

LLaVA

Otter

VPGTrans

CI

CS

CI

CS

CI

CS

CI

CS

CI

CS

CI

CS

I1

21.48

12.13

17.19

46.27

13.03

36.72

35.52

15.84

42.19

19.49

11.09

18.75

25.50

17.78

33.59

15.34

10.85

16.80

I2

24.50

13.58

21.88

11.50

4.29

5.86

39.05

17.65

48.05

20.04

12.23

23.05

31.52

19.63

35.55

15.16

8.85

12.89

I3

26.64

15.97

24.61

10.98

4.50

6.25

35.79

14.97

41.41

18.55

12.12

21.88

26.04

17.66

31.64

16.37

12.65

19.92

I4

34.26

16.03

29.69

11.09

4.51

6.25

35.30

16.16

42.19

18.84

11.03

19.53

49.61

21.83

46.09

17.63

10.98

17.19

I5

33.45

13.93

28.52

11.05

5.66

7.81

38.95

17.60

46.88

21.60

12.21

24.61

34.21

19.79

39.06

15.51

11.25

16.41

I6

59.70

19.80

53.13

80.65

17.69

55.08

40.11

16.78

47.66

45.34

14.64

35.94

68.98

24.04

56.64

47.39

17.76

45.70

I7

51.91

18.70

46.48

76.69

16.35

53.13

40.16

16.01

42.97

31.57

13.25

29.30

54.95

20.24

48.05

25.17

13.93

26.95

I8

60.38

18.72

51.56

10.98

4.23

5.86

35.79

16.47

43.36

46.28

17.59

40.63

53.29

19.40

40.23

24.84

11.75

19.14

I9

56.07

18.48

48.05

10.84

4.55

6.64

35.71

15.75

42.19

45.19

14.94

35.16

52.82

20.44

42.19

19.90

9.32

13.28

I10

60.73

21.23

52.73

10.81

4.85

6.64

43.50

17.53

48.83

28.38

15.55

31.25

55.16

19.78

44.53

46.40

17.65

47.66

I11

72.61

20.90

58.20

88.72

17.68

55.86

45.53

19.08

50.39

58.91

17.23

50.00

74.64

26.13

66.02

67.08

16.81

52.34

I12

47.92

18.85

43.36

23.92

8.53

17.97

69.55

23.03

60.94

19.81

11.52

20.31

3.64

7.46

5.86

23.54

13.41

23.44

I13

53.16

20.12

48.05

40.66

10.90

32.03

66.67

20.96

56.25

27.24

15.23

30.08

47.86

21.59

48.44

40.57

16.06

36.72

I14

52.89

18.69

46.09

25.81

9.16

18.75

62.31

18.86

53.13

21.47

10.57

21.09

17.83

13.02

17.97

27.65

14.37

27.73

55.90

17.41

42.19

11.07

4.40

6.25

38.64

18.38

48.05

60.23

14.45

33.98

61.43

22.70

48.44

22.37

10.47

15.63

I16

64.24

21.43

55.47

77.42

16.38

51.17

45.67

19.44

50.78

33.66

16.15

37.50

60.77

24.13

58.20

58.08

18.43

49.22

I17

74.12

21.68

57.03

10.96

5.00

7.03

52.23

18.50

50.00

42.40

14.33

38.28

57.95

19.90

48.83

48.74

18.33

26.95

I18

41.50

18.43

39.84

17.88

6.10

10.94

63.18

23.39

62.11

20.17

14.15

27.73

23.75

23.24

43.36

21.86

15.38

22.66

I19

85.50

21.39

60.16

98.16

22.37

63.67

57.25

21.29

58.59

85.00

18.01

60.55

78.92

26.19

69.14

89.88

18.36

59.77

I20

92.09

21.91

62.11

97.32

23.13

66.02

58.61

19.74

53.91

93.33

18.49

63.28

82.53

26.52

66.02

99.56

19.29

64.06

I21

87.03

21.90

61.33

92.97

19.75

57.03

51.97

20.51

56.64

88.46

18.23

57.42

80.18

26.37

66.80

92.18

17.99

58.98

I22

84.68

23.02

64.45

97.89

20.72

60.55

76.86

23.08

62.50

83.05

20.32

63.28

67.57

19.85

49.22

83.74

19.68

54.30

I23

81.96

20.93

60.55

11.85

4.08

6.25

70.57

22.57

63.67

86.93

21.58

58.59

77.14

24.24

62.11

78.73

17.89

55.08

I24

98.97

20.35

61.72

95.41

19.07

60.16

76.41

22.29

64.45

110.76

20.01

59.38

89.67

30.58

77.34

98.40

16.61

59.38

I25

85.08

21.36

63.67

76.37

15.28

51.95

51.12

18.85

49.61

95.95

17.24

54.69

80.16

26.41

67.58

85.92

18.66

61.72

Ins

LLaMA-Adapter-v2

mPLUG-Owl

Gemini-Pro-Vision

InternLM-XComposer

Qwen-VL

mPLUG-Owl2

CI

CS

CI

CS

CI

CS

CI

CS

CI

CS

CI

CS

I1

31.18

15.16

32.42

20.84

11.99

17.19

15.11

11.75

16.41

13.75

9.83

16.02

16.10

7.26

12.11

10.10

5.18

5.86

I2

22.96

14.04

25.00

17.77

12.18

18.36

14.24

8.36

11.33

13.16

8.99

15.63

16.28

8.35

12.50

10.00

5.52

6.25

I3

20.46

13.20

23.05

15.47

10.41

15.63

14.86

8.25

10.94

13.88

10.04

17.58

19.55

8.99

16.80

10.15

5.39

6.25

I4

19.14

12.11

20.70

15.17

10.50

13.67

14.49

10.39

14.45

14.41

9.50

16.02

18.11

8.13

14.06

9.85

5.54

6.25

I5

31.72

16.16

32.42

21.13

13.91

20.31

15.34

9.62

13.67

13.59

9.40

16.02

15.72

7.28

11.33

10.04

5.47

6.25

I6

79.44

25.74

62.89

79.79

21.98

56.64

18.66

13.11

19.14

18.95

12.76

21.88

25.48

10.61

17.58

32.86

12.77

22.27

I7

70.22

24.55

57.03

62.93

20.53

48.05

10.95

6.39

7.03

18.32

12.99

21.88

17.64

8.57

13.28

11.09

6.05

7.03

I8

58.44

20.65

49.22

29.20

16.16

26.95

14.89

8.82

11.33

16.70

12.09

19.92

19.23

5.95

8.98

9.57

4.79

5.08

I9

46.78

17.34

39.06

16.68

12.35

17.58

13.56

9.88

10.94

15.84

10.94

17.97

18.65

6.71

9.38

9.48

4.21

4.69

I10

43.18

16.77

36.33

61.12

18.17

42.97

24.44

14.17

28.13

17.54

11.35

20.70

29.62

13.06

24.61

51.38

14.11

31.64

I11

85.95

25.01

67.19

96.82

26.47

64.06

40.54

15.95

41.80

52.59

14.98

39.84

53.85

14.61

35.55

76.91

17.50

52.34

I12

20.60

11.07

18.36

66.96

20.69

45.70

28.32

14.89

27.34

30.93

11.06

24.61

17.44

6.31

9.38

13.86

4.71

5.47

I13

30.45

13.15

28.52

56.76

18.64

42.97

29.25

16.16

28.52

34.13

10.86

28.13

25.22

6.76

13.28

19.04

4.70

7.03

I14

18.62

10.85

16.80

73.85

19.16

50.00

29.42

15.77

30.47

19.32

7.88

12.50

42.02

11.67

26.95

18.46

5.40

7.42

71.89

20.68

53.91

40.79

18.89

34.77

20.76

15.57

21.48

16.32

11.20

17.97

22.72

7.11

12.11

9.66

5.48

6.25

I16

56.18

18.08

49.61

49.82

22.37

42.97

40.91

18.18

47.66

52.25

15.86

41.02

59.89

13.72

39.06

58.22

14.74

39.84

I17

56.27

15.85

43.36

91.68

24.00

57.81

48.17

17.88

46.88

28.23

13.26

26.95

55.39

14.17

35.94

68.76

16.54

46.09

I18

19.98

14.67

28.91

16.64

9.15

12.50

30.06

18.27

39.84

29.03

11.79

25.39

18.59

8.42

14.45

14.90

3.59

5.08

I19

89.07

26.50

67.97

97.53

24.29

60.55

57.96

20.35

54.69

66.71

14.91

46.09

75.82

16.22

48.44

85.39

20.61

58.59

I20

88.86

26.23

66.41

99.19

23.19

57.81

74.82

20.17

64.45

71.59

15.37

50.00

76.08

15.10

42.58

91.28

20.75

61.72

I21

87.02

25.31

70.70

94.21

24.12

59.77

59.99

20.80

57.81

72.08

15.53

50.39

75.26

14.91

45.70

80.65

19.61

54.69

I22

82.08

16.74

51.17

102.12

25.81

61.72

72.84

21.61

62.11

49.79

11.83

32.03

79.31

14.14

46.88

69.08

16.39

42.97

I23

84.57

22.55

60.94

99.18

23.19

61.33

68.37

20.94

60.16

67.72

15.68

47.27

75.40

16.15

46.88

79.66

17.67

52.34

I24

90.38

28.01

74.22

103.79

22.92

63.28

131.89

18.08

59.38

65.69

14.82

43.75

108.95

15.32

52.73

93.00

20.48

59.77

I25

83.27

20.28

56.64

98.92

24.16

62.89

80.01

20.16

60.16

71.17

14.97

46.48

86.81

15.70

51.95

81.84

18.13

52.34

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{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the stable evaluation of object hallucinations, which is a crucial challenge in large vision-language models. The authors provide the first systematic analysis of the underlying mechanism through which instructions affect hallucinations, based on comprehensive experiments. They report a linear correlation between the length of descriptions and the levels of object hallucinations. Furthermore, the authors propose a curve-based framework that incorporates description lengths to enable a stable evaluation of hallucinations. What I find particularly novel is that the slope of the curve is incorporated as a metric, which achieves a more comprehensive evaluation.\n\nSTRENGTHS:\n1. This work might provide valuable insights to the community. Firstly, while the impact of instructions on hallucinations is widely recognized, this work unveils a crucial aspect by demonstrating that instructions exert their influence through the modification of description lengths. This finding illuminates the previously unexplored mechanism underlying instruction-affected hallucinations. Secondly, they employ a curve-based evaluation method instead of relying solely on a single metric, which goes a new way in addressing hallucination evaluation. Thus, this work has the potential to inspire further research and exploration in hallucination evaluation. \n2. The proposed curve-based hallucination evaluation method in this paper is intuitively reasonable, and the author provides substantial experimental evidence to support the motivation behind this method. The experimental results are clearly presented, and the corresponding analyses further enhance the persuasiveness of this work. Overall, the combination of the intuitive approach, extensive experiments, clear presentation of results, and insightful analyses makes this work convincing.\n\nWEAKNESSES:\n1. The proposed method realizes consistent evaluation by calculating the hallucination rate at a uniform length. However, the length distributions of descriptions generated by different LVLMs exhibit variations. In other words, some models tend to produce shorter descriptions while others generate longer ones. In light of this, I have concerns regarding the ability of this method to maintain its effectiveness under such circumstances.\n2. In my view, the hallucination evaluation of a LVLM in practical requires a large instruction set that could simulate real-world applications of the LVLM. If the authors can build such a large instruction set as the benchmark, it would yield a significant contribution to the community.\n3. The authors claim that their proposed evaluation method is fairer compared to other evaluation methods. However, the paper appears to lack experimental results to support this assertion.\n4. The analysis of the stability of the “LeHaCE_GR” is lacking.\n5. The selection of instructions may have a substantial impact on the fitted curve. It would be beneficial for the authors to provide further discussion on this aspect.\n\nQUESTIONS:\n1. Considering that shorter descriptions tend to have fewer hallucinations, have the authors explored the possibility of generating multiple concise descriptions with distinct focuses for the same image, and subsequently merging them into a comprehensive and detailed description?\n2. What factors determine the slope of the length-hallucination curve for the model?\n3. Since the authors introduce the slope of the length-hallucination curve as a valuable evaluation metric, it raises the question of what the intercept of the curve signifies. Is it feasible to incorporate the intercept into the evaluation framework?\n4. Why does the average length of the image description generated by the Otter model, specifically under instruction I12, amount to only 2? Is there any misunderstanding here?\n\nLIMITATIONS:\nThe paper briefly mentioned limitations.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work aims to establish a stable, fair, and comprehensive evaluation method for object hallucinations in large vision-language models. The authors discovered a positive correlation between the length of image descriptions and the degree of object hallucination. Building upon this observation, they developed a hallucination evaluation method named LeHaCE by fitting a length-hallucination curve. LeHaCE enables the evaluation at any given image description length, ensuring stability and fairness in the evaluation process. Additionally, LeHaCE involves the curve slope as a metric to evaluate the influence of image description length on the degree of object hallucination, thereby achieving a comprehensive evaluation. The motivation behind this work is reasonable, and the authors provide many experiments to support their claims. However, it is worth considering that the use of the linear fitting scheme, although straightforward, does somewhat diminish the novelty of the proposed method.\n\nSTRENGTHS:\nThe experimental analysis conducted on instructions and hallucination is compelling and provides strong support for the main argument that the hallucination degree is positively correlated with the length of the description. While previous research (Yifan et al., 2023) has already shown the influence of instructions on hallucinations, this work takes it a step further by proposing that instructions indirectly influence hallucinations through the length of image descriptions. This sheds light on the reason behind the limitations of previous approaches that relied on average-based methods. Overall, this paper offers valuable insights into the evaluation of consistent hallucinations.\n\nWEAKNESSES:\n1. Although the rationale behind the length-hallucination curve is compelling, it is fitted using a relatively simplistic linear approach. Exploring more flexible and intricate fitting approaches is worth considering, as it has the potential to achieve higher fitting accuracy and more effective hallucination evaluation.\n2. Since the proposed method relies on a fitted curve, it needs at least two instructions to evaluate LVLMs and cannot be used with just one instruction.The authors should discuss this limitation.\n3. Lack of indepth discussion on the shortcomings of the proposed method. For instance, as shown in Table 2, why does LeHaCE exhibit poor stability on a few LVLMs when the number of instructions is three?\n4. It seems that the selection of instructions might affect the stability of LeHaCE. It would be helpful to include more discussion on this aspect.\n5. The current paper seems to have lots of results and experiments. As a reader, it is not very easy for me to get the main conclusion for each experiment. It would be good to highlight the conclusions so that the readers can understand the point easier.\n6. Some typos need to be corrected: Line 79: lrv-instruction -> LRV-instruction. Line 92 Nope -> NOPE. Line 81 chatgpt -> ChatGPT. Table 2: Minigpt-4 -> MiniGPT-4.\n\nQUESTIONS:\n1. Does the complexity of the image content, such as the number of objects, influence the extent of hallucination in the model? It would be valuable to investigate additional factors that impact hallucination degrees.\n2. Intuitively, the average-based framework can also be effective as long as there are enough instructions, such as 200 instructions. I'm wondering if this viewpoint is accurate?\n3. Is the relative standard deviation an appropriate approach to evaluate stability, considering that stability in this context essentially refers to the consistency of multiple evaluation results?\n4. Why does this work exclusively focus on object hallucinations? Is this a choice made by the authors or a limitation of the proposed method?\n5. In Figure 5, why does LeHaCE show higher instability on LLaVA and Qwen-VL when the image description length is less than 20 words?\n\nLIMITATIONS:\nThe paper includes a simple discussion of the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper identifies a pitfall regarding the length of image descriptions in the current average-based LVLM hallucination evaluation framework. To address this, they propose a new Length-Hallucination Curve Based evaluation framework to enhance the fairness of evaluations. The paper observes that the degree of object hallucinations is primarily influenced by the length of image descriptions, with instructions indirectly affecting hallucinations through their impact on description lengths. They suggest using a linear regression curve for evaluation and develop two metrics based on this curve. Extensive experiments on multiple LVLMs with different instruction sets demonstrate the stability of their proposed new evaluation metrics.\n\nSTRENGTHS:\n- The observation is intuitive and validate by extensive experiments\n\n- The paper is clearly written and easy to follow\n\n- The evaluation is comprehensive in terms of numerous instructions and LVLMs\n\nWEAKNESSES:\n- Although paper observe the linear relation between the length of the image description and objection hallucination, there are still unanswered questions regarding the justification of the claim. Please see questions below.\n\n- Some minor inconsistent typo, for example, the AEF and ABF in Figure 4.\n\n- The evaluation only use CHAIR scores and scores of other aspects is not evaluated, for example, the detail or the coverage of the real objects in the description as in AMBER.\n\nQUESTIONS:\n- The paper grouped 25 different instructions to 5 instruction set. What’s the grouping strategy? How do the author group these instructions? \n\n- The paper claimed that object hallucination is primarily influenced by the length of image descriptions, with instructions only indirectly affecting hallucinations through their effect on description lengths. How is this claim being validated? Specifically, how do the author validate that the length of the image description is the primary cause and is not also affecting the hallucinations indirectly through their effect on some hidden factors ? The observation could be due to the spurious correlation.\n\n- Does the increased length of the image description also capture more real objects, or does it mainly consist of rephrasing and hallucinatory sentences?\n\nLIMITATIONS:\nYes, the author adequately addressed the limitations and potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work presents comprehensive experiments to study the relationship between description lengths and hallucinations in LVLMs. Based on the observed positive correlation, authors propose an approach of fitting a length-hallucination curve to evaluate object hallucinations. Speciffically, the curve allows for fair comparisons that are not influenced by varying lengths, through providing the hallucination degree corresponding to any given description length. Furthermore, the curve slope reflects the extent to which a LVLM's hallucination degree is affected by description lengths. The evaluation, considering both the value and slope, demonstrates stability and comprehensiveness, as supported by the conducted experiments. The authors' thorough and meticulous research on this issue is highly convincing, and the proposed method effectively showcases its effectiveness.\n\nSTRENGTHS:\nHallucinations evaluation is a realistic and crucial task in the field of LVLMs, as hallucinations usually introduce misleading conclusions or even have disastrous outcomes. In this context, the authors perform a detailed experimental analysis on the impact of instructions on hallucinations, providing convincing evidence to support their motivation. Moreover, the proposed curve-based method is a simple yet effective approach, which is well-motivated by the observed linear correlation between description lengths and hallucination rates. The paper is well-written and effectively communicates its main contributions and techniques. Overall, the paper exhibits technical solidity.\n\nWEAKNESSES:\n1. The authors conduct experiments using only the beam search setting. Although I understand that beam search is widely used in hallucination evaluation of LVLMs/LLMs, it remains uncertain whether the observed correlation between the hallucination degree and the description length holds true under different decoding strategies. Thus, I recommend that the authors explore additional commonly used decoding strategies, such as greedy decoding, to provide a more comprehensive analysis. \n2. The paper lacks a study about the influence of the instruction number on the length-hallucination curve. The fitted curve is directly affected by the number of samples, which corresponds to the number of instructions provided. It is therefore essential to thoroughly investigate the minimum number of instructions necessary for the proposed method. \n3. The authors mention in the paper that the proposed method can \"evaluate object hallucinations at any given image description length.\" In reality, when the given length deviates too much from the existing data, the fitting is likely to fail, leading to inaccurate results. The authors should use more cautious wording.\n4. In my opinion, the impact of length might be mitigated by simply controlling the maximum generation lengths.The authors only mention this method in a footnote and believe it does not align with the actual usage scenarios of LVLMs. More in-depth discussions should be provided.\n5. Some minor errors need to be corrected. For example, in line 42, \"Figure 2&3\" should be \"Figures 2&3\".\n6. It appears inappropriate to represent a variable using only two letters. Consider replacing \"hr\" with \"h_r\".\n\nQUESTIONS:\n1. Why is the proposed method limited to large vision-language models? Could it be extended to large language models as well? It would be beneficial for the authors to provide a clear explanation or justification for this limitation. \n2. Similarly, are the finding and method presented in this paper applicable to other forms of hallucination beyond object hallucinations, or other tasks, such as VQA?\n3. What could potentially explain the phenomenon observed in Figure 2, where longer output lengths result in higher object hallucination degrees?\n4. How are the 25 instructions used in experiments designed? Are they generated randomly or based on specific rules? Besides, why is it 25, and what difference would there be if there are more or less instructions?\n\nLIMITATIONS:\nThe authors adequately addressed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-11"}