AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?

## Abstract

Abstract Large Vision-Language Models (LVLMs) have become essential for advancing the integration of visual and linguistic information, facilitating a wide range of complex applications and tasks. However, the evaluation of LVLMs presents significant challenges as the evaluation benchmark always demands lots of human cost for its construction, and remains static, lacking flexibility once constructed. Even though automatic evaluation has been explored in textual modality, the visual modality remains under-explored. As a result, in this work, we address a question: “Can LVLMs serve as a path to automatic benchmarking?”. We introduce AutoBench-V , an automated framework for serving evaluation on demand, i.e. , benchmarking LVLMs based on specific aspects of model capability. Upon receiving an evaluation capability, AutoBench-V leverages text-to-image models to generate relevant image samples and then utilizes LVLMs to orchestrate visual question-answering (VQA) tasks, completing the evaluation process efficiently and flexibly. Through an extensive evaluation of seven popular LVLMs across five demanded user inputs ( i.e. , evaluation capabilities), the framework shows effectiveness and reliability. We observe the following: (1) Our constructed benchmark accurately reflects varying task difficulties; (2) As task difficulty rises, the performance gap between models widens; (3) While models exhibit strong performance in abstract level understanding, they underperform in details reasoning tasks; and (4) Constructing a dataset with varying levels of difficulties is critical for a comprehensive and exhaustive evaluation. Overall, AutoBench-V not only successfully utilizes LVLMs for automated benchmarking but also reveals that LVLMs as judges have significant potential in various domains.

## 1 Introduction

The flourishing of Large Language Models (LLMs) (Touvron et al., ; Achiam et al., ; Liu et al., ; Anthropic, ) has paved the way for significant advancements in the various downstream applications (Vaswani et al., ; Huang et al., ; Liu et al., ). As the capabilities of LLMs grew, researchers began to explore the integration of visual information understanding capabilities into LLMs, giving rise to the development of Large Vision-Language models (LVLMs) (Achiam et al., ; Liu et al., ). These models are trained on extensive paired image-text datasets, enabling them to perform sophisticated multimodal reasoning by effectively integrating visual and textual information (Zou et al., ; Ghandi et al., ; Karras et al., ; Agrawal et al., ; Chen et al., ).

With the widespread adoption of LVLMs, evaluating these models has become increasingly important, for understanding their limitations and reliability better. Recent research (Xu et al., ; Liu et al., ; Ying et al., ; Li et al., ; ; Yin et al., ) emphasize the urgent need for comprehensive and sophisticated evaluation standards that accurately assess LVLMs’ abilities across various modalities. Various benchmarks are aiming to evaluate a range of capabilities of LVLMs including 3D understanding (Yin et al., ), perception and cognition capacity (Liu et al., ; Fu et al., ), multi-discipline understanding and reasoning (Yue et al., ). Even though these works have solidly evaluated certain aspects of LVLMs’ capabilities, they lack the flexibility to support on-demand evaluation across various capability aspects. Recent studies have explored the usage of generative AI in automating evaluation, which offers flexibility in varying evaluation dimensions and
reduces the human cost of benchmark dataset construction (Wu et al., ; Zhu et al., ; Li et al., ). While these studies focus on the automatic evaluation of LLMs, we aim to extend this to visual modality by answering addressing this question: “Can LVLMs serve as a path to automatic benchmarking?”

Automating the evaluation of LVLMs presents several key challenges. First, the targeted capabilities to be evaluated must be clearly identified based on the input demand. This is the foundation that relevant images and appropriate visual question-answering (VQA) tasks can be generated to accurately assess the LVLMs’ performance in those specific aspects. Second, the generated images and VQA tasks should be relevant and accurately reflect the evaluation target. Third,
the risk of answer leakage from Examiner LVLM during question generation should be mitigated.
This issue arises when the model responsible for generating questions exhibits self-enhancement bias (Ye et al., ; Zheng et al., ), wherein the model being evaluated is also employed to generate the evaluation cases.

Figure: Figure 1: Five key evaluation dimensions supported by AutoBench-V, along with their fine-grained sub-aspects, accompanied by questions and images to assist in understanding.
Refer to caption: x1.png

To address the above challenges, we propose AutoBench-V, which supports automated evaluation of LVLMs based on a user demand regarding specific aspects of model capability (*e.g.*, Spatial Understanding). Initially, the input demand is processed by an examiner LVLM, which categorizes it into several overarching aspects. Each aspect is further divided into several fine-grained components, for which image descriptions of varying difficulty levels are generated. To ensure that the descriptions align with their corresponding images, a self-validation mechanism is applied using VQA (Agrawal et al., ). Furthermore, an error control mechanism is implemented to prevent a negative impact on the generation of questions and reference answers. The generated questions and images are then presented to the evaluated LVLM to generate responses, which are assessed against reference answers (Liu et al., ). The pipeline of AutoBench-V is shown in Figure 2.

By leveraging AutoBench-V, we conduct extensive evaluation of seven popular LVLMs across five demanded evaluation capabilities (see Figure 1).
The results show that LVLMs exhibit declining performance as task difficulty rises, with varied performances over distinctive LVLMs. While excelling in high-level understanding, they struggle with detailed reasoning, revealing a key area for improvement in future research. We also carried out several human evaluation experiments on the generated cases, which yielded positive results, demonstrating the reliability of our approach. To summarize, our key contributions are three-fold:

$\triangleright$ An automated LVLM evaluation framework. This proposed AutoBench-V is the first automated framework for benchmarking LVLMs’ capability. The framework leverages text-to-image models to generate images for evaluation and employs GPT-4o as an examiner to conduct VQA evaluations. This automation significantly reduces human involvement, enhancing the efficiency and objectivity of the evaluation process.

$\triangleright$ Extensive experiments validating the framework’s effectiveness. We conducted comprehensive experiments, including main evaluations on multiple models, examiner superiority tests, option position bias analysis, and human assessments. The results confirm the framework’s robustness and effectiveness in evaluating LVLMs.

$\triangleright$ In-depth analysis of LVLMs’ performance across diverse visual tasks. Through systematic evaluation with varied user inputs, we find that LVLMs demonstrate strong proficiency in abstract conceptual understanding while exhibiting comparatively lower performance in concrete visual reasoning tasks. These insights offer a perspective on the current state of LVLM technology, highlighting areas with potential for future development and exploration.

Figure: Figure 2: An overview of the AutoBench-V pipeline, illustrating the automated evaluation process. It starts from user input intake, then aspect generation, followed by the generation of corresponding images and questions, and finally outputs the evaluation score of LVLMs.
Refer to caption: x2.png

## 2 Related Works

Benchmark for LVLMs. The emergence of the LVLMs greatly promoted the development of the multimodal model, demonstrating exceptional progress in their multimodal perception and reasoning capabilities. This makes the past, focused on isolated task performance benchmarks (Karpathy & Fei-Fei, ; Agrawal et al., ) insufficient to provide a comprehensive evaluation. Subsequent studies have introduced benchmarks for assessing LVLMs across a range of multimodal tasks (Goyal et al., ; Lin et al., ; Russakovsky et al., ). However, these benchmarks often fall short in providing fine-grained assessments of abilities and robust evaluation metrics. Hence, recent works (Liu et al., ; Ying et al., ; Fu et al., ; Yu et al., ; ) highlight the critical need for developing advanced, comprehensive benchmarks to more accurately assess LVLMs’ multimodal understanding and reasoning capabilities. However, these benchmarks still have different kinds of limitations. For example, LVLM-eHub (Xu et al., ) and LAMM (Yin et al., ) have utilized several classical datasets that are widely recognized but not sufficiently novel for current advancements, overlooking the possibility of data leakage during LVLM training. Hence, MMStar (Chen et al., ) aims to solve the unnecessity of visual content and unintentional data leakage that exists in LVLM training via constructing an elite vision-indispensable dataset.

Compared to previous work, AutoBench-V not only automates the entire benchmarking process for LVLMs—significantly reducing human workload and minimizing subjective biases—but also scales up and customizes the evaluation process to address fine-grained user needs.

Automatic benchmarks.
The significant early advancements in LLMs have driven the development of various benchmarks designed to automate evaluation processes. For example, LMExamQA (Bai et al., ) employs the concept of a Language-Model-as-an-Examiner to create a comprehensive and scalable evaluation framework. In addition, DyVal (Zhu et al., ) and DyVal2 (Zhu et al., ) both highlight the importance of dynamic assessment, with DyVal focusing on reasoning tasks and DyVal2 adopting a broader psychometric approach. AutoBencher (Li et al., ) automates the generation of novel, challenging, and salient datasets for evaluating LLMs, further expanding the scope of automated benchmarking. Other efforts, such as UniGen (Wu et al., ) and Task Me Anything (Zhang et al., ), focus on developing more tailored and relevant benchmarks for assessing LLM/LVLMs performance across diverse tasks.

Figure: Figure 3: A comprehensive overview of the AutoBench-V framework.
Refer to caption: x3.png

## 3 AutoBench-V

In this section, we introduce AutoBench-V, a framework designed for automating the process of benchmarking LVLMs, empowered by a LVLM $\mathcal{M}_{v}$ and a text-to-image model $\mathcal{M}_{d}$. As shown in Figure 3, AutoBench-V consists of four modules (Xiao et al., ): user-oriented aspect generation, guided description generation, image generation by self-validation, and test case generation & evaluation.

### 3.1 User-Oriented Aspect Generation

User input. The user input can specify an evaluation target focused on certain aspects of LVLMs’ capability. AutoBench-V covers the following key evaluation aspects, which are the most crucial for assessing the capabilities of LVLMs:
Basic Understanding, Spatial Understanding (Li et al., ), Semantic Understanding (Meng et al., ), Reasoning Capacity (Liu et al., ), and Atmospheric Understanding (Geetha et al., ). Notably, the user input is not limited to the above kinds, and can be customized as needed.

Hierarchical aspect generation. For each user input, we derive a set of aspects representing specific capability items. For example, as shown in Figure 1, contextual comprehension is an aspect under Basic Understanding. However, directly generating aspects from user input can lead to excessive repetition, reducing both diversity and reliability by overlapping in semantics and repeatedly evaluating the same capability. To mitigate this, we propose hierarchical aspect generation inspired by the previous study (Qin et al., ) to constrain the aspect generation process. Formally, given the user input $q$, we first generate $n$ general aspects $\{A^{(g)}_{1},A^{(g)}_{2},\dots,A^{(g)}_{n}\}$ by $\mathcal{M}_{v}$, which can be formulated as:
$\{A^{(g)}_{1},A^{(g)}_{2},\dots,A^{(g)}_{n}\}=\mathcal{M}_{v}(q)$. These general aspects represent high-level evaluation dimensions based on $q$. Next, for each general aspect $A^{(g)}_{i}$, we further generate $m$ fine-grained aspects $\{A^{(f)}_{i1},A^{(f)}_{i2},\dots,A^{(f)}_{im}\}$, where each fine-grained aspect provides more specific criteria related to the general aspect. The fine-grained aspects are also generated by $\mathcal{M}_{v}$ and depend on both the user input $q$ and the corresponding general aspect $A^{(g)}_{i}$.
The fine-grained aspect of generation can be represented as $\{A^{(f)}_{i1},A^{(f)}_{i2},\dots,A^{(f)}_{im}\}=\mathcal{M}_{v}(q,A^{(g)}_{i})$. Thus, the hierarchical aspect generation yields a structured set of evaluation aspects (*i.e.*, fine-frained aspect)
$\mathcal{A}=\bigcup_{i=1}^{n}\left(\{A^{(g)}_{i}\}\cup\bigcup_{j=1}^{m}\{A^{(f
)}_{ij}\}\right)$, where $|\mathcal{A}|$ = $mn$.

### 3.2 Guided Description Generation

Guidelines formulation.
To avoid the generation of irrelevant, abstract, or vague details that could lead to discrepancies in image descriptions, we introduce a guideline generation step (Zhao et al., ; Viswanathan et al., ). Before generating image descriptions, the LVLM model $\mathcal{M}_{v}$ formulates a guideline $\mathcal{D}_{ij}$ (*e.g.*, in Background vs Foreground aspect, it is essential to distinguish between the elements present in the background and those in the foreground) for each fine-grained aspect $A^{(f)}_{ij}$. This guideline acts as a guideline for $\mathcal{M}_{v}$, ensuring that the generated descriptions are coherent, clear, and specific to the fine-grained aspects under evaluation. The process can be expressed as $\mathcal{D}_{ij}=\mathcal{M}_{v}(A^{(f)}_{ij})$. The generated guideline $\mathcal{D}_{ij}$ is then utilized to guide the subsequent image description $\mathcal{T}_{ij}$.

Image description with difficulty grading.
To enable a more comprehensive evaluation, we introduce a difficulty-grading mechanism for the image descriptions, which includes the evaluation cases from different difficulties. This is achieved by classifying the generated image descriptions into three difficulty levels: easy, medium, and hard. We show the examples across different difficulties in Figure 4. The difficulty level $d$ is determined by key factors such as background complexity, element relationships, and the intricacy of textures. The generation of $\omega$ image descriptions $\{\mathcal{T}_{ij1}^{d},\mathcal{T}_{ij2}^{d},\dots,\mathcal{T}_{ij\omega}^{d}\}$ for $A^{(f)}_{ij}$ at a specific difficulty level $d$ can be defined as: $\mathcal{\bigcup}_{k=1}^{\omega}\{\mathcal{T}_{ijk}^{d}\}=\mathcal{M}_{v}(q,A^
{(f)}_{ij},\mathcal{D}_{ij},d)$, where $d\in\{\text{easy},\text{medium},\text{hard}\}$. This grading strategy allows for a nuanced understanding of the model’s capabilities across a range of challenges with details provided in Appendix C.

Figure: Figure 4: Images examples corresponding to different user inputs at varying difficulty levels.
Refer to caption: x4.png

Diverse description generation strategy.
A key challenge when generating image descriptions at the same difficulty level is minimizing repetitive elements and backgrounds, which can reduce the diversity and generalization of the evaluation. For example, given a user input $q$ related to spatial understanding, the model $\mathcal{M}_{v}$ might tend to produce descriptions centered around urban landscapes, potentially compromising the variety of test cases. To address this, we introduce a description optimization strategy using a semantic graph (Quillian, ) to enhance the diversity of image prompts generated by $\mathcal{M}_{v}$, with significant results referred to in Appendix Figure 10. For a visualization of specific words, see Appendix Figure 11 and Figure 12. The process is iterative, and during the $e^{\prime}$th iteration of prompt generation, a topic word $t_{e}$ and a set of $|c|$ related keywords $K_{e}=\{k_{e1},k_{e2},\dots,k_{ec}\}$ are selected. These keywords are added as nodes to the semantic graph $G$, where nodes are connected by edges representing semantic relationships between them.

Formally, let $G_{e}=(V_{e},E_{e})$ be the semantic graph generated at iteration $e$, and let $S_{e}=\left(V_{e-1}\cup\{t_{e}\}\cup K_{e}\right)$. Then $V_{e}=S_{e}\setminus f(S_{e})$ represents the node set of topic words and keywords, and $E_{e}$ is the set of edges capturing the relationships between them. After each round of prompt generation, we apply a degree-based exclusion mechanism, where the number of excluded nodes is determined by a function $f(S_{e})$. This function defines the number of top-degree nodes to be excluded, allowing flexibility in adjusting how many frequently used words are removed as the iterations progress. The function $f(S_{e})$ could be a simple function such as $f(S_{e})=\underset{V^{\prime}\subseteq S_{e},|V^{\prime}|=e}{\text{arg max}}
\sum_{v\in V^{\prime}}\deg(v)$, where $\deg(v)$ represents the degree of node $v\in V_{i}$, or it could take a more complex form based on specific conditions. We mitigate redundancy and promote diversity in the generated prompts by excluding these high-degree nodes, which correspond to the most commonly used words. The function $f(S_{e})$ offers the flexibility to control how aggressively the exclusion process operates based on the round number $e$.

Overall, the generation of an image description $\mathcal{T}_{ij}^{e}$ can be formalized as follows:

$$ $\bigcup_{k=1}^{\omega}\{\mathcal{T}_{ijk}^{de}\}={M}_{v}(q,A^{(f)}_{ij}, \mathcal{D}_{ij},V_{e},d),$ $$

where $V_{e}$ represents the refined and diverse set of topic words and keywords after the exclusion mechanism has been applied. We show the detailed procedure in Algorithm Algorithm 1.

Figure: Algorithm 1 Diverse Description Generation Strategy

### 3.3 Image Generation By Self-Validation

Self-validation. The image descriptions $\mathcal{T}_{ij}^{d}$ and their corresponding aspects $A^{(f)}_{ij}$ are subsequently provided to the text-to-image model for image generation. At this stage, a potential issue is the possibility of generated images $\mathcal{I}_{ij}^{d}$ not aligning with the descriptions, due to hallucinations inherent in the text-to-image model (Lee et al., ). To tackle this issue, drawing inspiration from TIFA (Hu et al., ), we employed a self-validation process to evaluate the consistency of images with their descriptions via VQA.

In the self-validation process $\mathcal{F}$, for each image $\mathcal{I}_{ij}^{d}$, based on its image description, $\mathcal{M}_{v}$ is prompted to generate a set of simple questions $\Phi_{ij}^{d}=\{\phi_{ij1}^{d},\phi_{ij2}^{d},\dots,\phi_{ijp}^{d}\}$ (*e.g.*,“Is there a wooden chair in the image?”), where $p$ denotes the question number to evaluate the alignment. The function $\mathcal{F}$ takes the image $\mathcal{I}_{ij}^{d}$, its description $\mathcal{T}_{ij}^{d}$, and the set of questions $\Phi_{ij}^{d}$ as inputs and outputs an alignment score $S_{ij}^{d}$, which is calculated as the ratio of correctly answered questions to the total number of questions:

$$ $S_{ij}^{d}=\mathcal{F}(\mathcal{I}_{ij}^{d},\mathcal{T}_{ij}^{d},\Phi_{ij}^{d})$ $$

We set a threshold $\zeta$, where: (i) If $S_{ij}^{d}<\zeta$, the image $\mathcal{I}_{ij}$ will be reworked in line with the description until it meets the required standard; (ii) If $\zeta\leq S_{ij}^{d}<1$, the image meets the basic criteria but contains an error $\mathcal{E}_{ij}^{d}$, which will be documented; and (iii) If $S_{ij}^{d}=1$, the image is considered to fully align with the description and is deemed acceptable.

### 3.4 Test Case Generation & Evaluation

Q&A generation with error control. To enhance the accuracy of question generation, particularly when addressing potential flaws in images, we propose error control. Despite thorough self-validation, it’s not guaranteed that every image will be flawless. Furthermore, when generating problems, we aim to avoid introducing biases stemming from the visual capabilities of examiner LVLM (Zhang et al., ). Therefore, when generating questions, we will only include the image description $\mathcal{T}_{ij}^{d}$ and any identified defects $\mathcal{E}_{ij}^{d}$ in the input to the examiner $\mathcal{M}_{v}$. The function $\mathcal{M}_{v}$ generates the question $Q_{ij}^{d}$ based on the image description and errors:

$$ $Q_{ij}^{d}=\mathcal{M}_{v}(\mathcal{T}_{ij}^{d},\mathcal{E}_{ij}^{d}).$ $$

This will enable the creation of a diverse set of questions, along with reference answers, that specifically target the defective elements. For each image, we will provide a related question $Q_{ij}^{d}$ (*e.g.*, multiple-choice or true/false). These questions, along with the accompanying images, will be presented to the LVLMs under evaluation for their response.

Evaluation. The response $\mathcal{P}_{ij}^{d}$ from the tested LVLMs was compared to the reference answer $A_{ij}^{d}$ to determine accuracy. If $\mathcal{P}_{ij}^{d}$ matched $A_{ij}^{d}$, it was marked correct ($\text{Acc}_{ij}^{d}=1$); otherwise, it was marked incorrect ($\text{Acc}_{ij}^{d}=0$). The overall accuracy $\text{Acc}^{d}_{\text{total}}$ was calculated as the average accuracy over all $N$ questions.

## 4 Experiment

In this section, we evaluate seven of the latest models using Autobench-V and perform human evaluations to validate our experimental findings. First, we demonstrate how Autobench-V significantly reduces potential answer leakage and self-enhancement bias, as evidenced by the experimental results in Figure 5. Next, based on Figure 6, Table 1, Table 2, and LABEL:tab:average_scores_transposed, we analyze the impact of various evaluation factors (*e.g.*, user input) and question difficulty on model performance, which reveals several insightful findings. We then present the model rankings across five user input categories with varying difficulty levels in Figure 8, followed by a discussion on human evaluations regarding alignment during Autobench-V’s generation process. Lastly, we investigate position bias in the evaluation process, as illustrated in Figure 9.

**Table 1: Effectiveness of hierarchical aspect generation under various hyperparameter settings.**
| m=3, n=5 | m=3, n=6 | m=3, n=7 | m=4, n=5 | m=4, n=6 | m=4, n=7 |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Raw | +Hierarchy | Raw | +Hierarchy | Raw | +Hierarchy | Raw | +Hierarchy | Raw | +Hierarchy | Raw | +Hierarchy |
| 0.767 | 0.778 (1.4% $\uparrow$) | 0.773 | 0.780 (1.0% $\uparrow$) | 0.779 | 0.825 (5.9% $\uparrow$) | 0.780 | 0.790 (1.3% $\uparrow$) | 0.786 | 0.849 (10.2% $\uparrow$) | 0.798 | 0.842 (5.5% $\uparrow$) |

### 4.1 Experimental Setup

Selected models. In evaluating LVLMs, we selected seven representative models: GPT-4o, GPT-4o mini (Achiam et al., ), Claude-3.5-Sonnet, Claude-3-Haiku (Anthropic, ), Gemini-1.5-Flash (DeepMind, ), GLM-4v (GLM et al., ), and the open-source Qwen2-VL (Bai et al., ), detailed in Table 7. These advanced models exhibit exceptional image understanding. Some well-known open-source models, such as Llava-1.6 (Liu et al., ) and MiniGPT-4 (Zhu et al., ), were tested and found to perform poorly. Additionally, their capabilities differ significantly from other models, so they are not discussed in our evaluation. We chose GPT-4o as the examiner model for generating image descriptions, questions, and answers due to its strong overall performance. The descriptions were then passed to Flux-pro (blackforestlabs, ), a text-to-image model known for outstanding image generation. We also experimented with other text-to-image models (Rombach et al., ; Podell et al., ; Betker et al., ). However, their performance was suboptimal.This combination enables effective automated generation of image-based questions, crucial for the evaluation process (Ying et al., ; Fu et al., ; Xu et al., ).

Experimental setting. We set $\displaystyle n=4$ for the number of general aspects and $\displaystyle m=6$ for the number of fine-grained aspects, as this configuration yields the highest diversity in the generated aspects as illustrated in Table 1, allowing for a broader range of scenes and elements. We set $\omega$ = 10, namely 10 pictures for each fine-grained aspect. Therefore, we evaluate 720 images for each user input (with each user input having three difficulty levels). For easy difficulty, we set the self-validation threshold $\zeta_{e}=1$ since the scenes are simpler and contain fewer elements, which justifies a higher threshold. For the medium and hard difficulty levels, the images contain more elements, so we lower the thresholds to $\zeta_{m}=\zeta_{h}=0.8$ to avoid compromising efficiency. The error control mechanism that follows ensures the appropriateness of this threshold.

**Table 2: Average performance (Accuracy) of all models at different difficulty levels.**
| Model | Easy | Medium | Hard |
| --- | --- | --- | --- |
| GPT-4o | 90.43% | 79.81% | 75.02% |
| GPT-4o mini | 88.01% | 76.98% | 70.70% |
| Gemini-1.5-Flash | 88.07% | 74.64% | 70.85% |
| Claude-3.5-Sonnet | 89.28% | 75.49% | 63.67% |
| Claude-3-Haiku | 86.82% | 73.40% | 67.42% |
| GLM-4v | 90.43% | 77.29% | 64.93% |
| Qwen2-VL | 89.57% | 79.03% | 71.89% |

Figure: Figure 5: Performance of various LVLMs across three difficulty levels when provided only with image descriptions, without the corresponding images.
Refer to caption: x5.png

Figure: Figure 6: Overall performance of different models at easy, medium, and hard levels.
Refer to caption: x6.png

### 4.2 Examiner Priority

To mitigate the potential for answer leakage associated with self-enhancement bias (*e.g.*, when the model being evaluated is also utilized for generating the evaluation cases) in the examiner LLM, we enhance the fairness of the assessment by having AutoBench-v generate questions from image descriptions rather than directly from images. This approach separates the visual information from the generation process, reduceing the risk of self-enhancement bias (Ye et al., ) that could occur if questions were derived from the examiner model’s (GPT-4o) visual capabilities, which might cause unfair comparison. By employing only textual descriptions for generation, we eliminate the influence of GPT-4o’s specific visual processing abilities, thereby ensuring a more equitable evaluation.

To validate the fairness of this method, we conducted an experiment in which models were presented with image descriptions alongside corresponding questions while avoiding direct access to the images. As demonstrated in Figure 5, the results revealed consistent performance across all models, with minimal variance (0.4% for easy questions and 2.4% for hard questions). This consistency suggests that, in the absence of visual input, models’ textual understanding ability is almost equal, which means the benchmark effectively assesses visual comprehension and does not show obvious bias towards the examiner LVLM (*i.e.*, GPT-4o).

Figure: Figure 7: Score variation of models from easy to hard across different user inputs. As task difficulty increases, the performance disparity between models becomes more pronounced.
Refer to caption: x7.png

### 4.3 Main Results

As shown in Figure 6, through the evaluation of various models on AutoBench-V, we can observe several findings that can bring insights for future work. More detailed results are in Table 6.

**Table 3: Average accuracy for various user inputs at different difficulty levels.**
| User Input | Easy | Medium | Hard |
| --- | --- | --- | --- |
| Basic. | 90.59% | 75.90% | 63.33% |
| Spatial. | 82.46% | 69.14% | 63.00% |
| Seman. | 91.28% | 79.84% | 74.52% |
| Reason. | 86.50% | 74.67% | 68.97% |
| Atmos. | 94.76% | 83.33% | 75.66% |

Model performance decreases as task difficulty increases, with GPT-4o showing the strongest average performance across tasks. This trend is consistent across all models, with scores steadily declining as the difficulty increases from easy to hard, as shown in Figure 13. For example, GPT-4o’s average score drops from 90.43% at the easy level to 75.02% at the hard level. Despite the overall decline, GPT-4o maintains its leading position across all difficulty levels. Additionally, the result highlights that the most notable shift occurs between easy and medium. Although a few samples show improved scores with increased difficulty mildly, the majority trend still experiences a decline, reinforcing the validity of our difficulty grading mechanism.

As task difficulty increases, the performance disparity between models becomes more pronounced. As illustrated in Figure 7, the performance decline across models varies with increasing task difficulty. Models like Claude-3.5-Sonnet and GLM-4v experience more pronounced drops. In difficult semantic tasks, GPT-4o maintains a strong score of 79.36%, compared to Claude-3.5-Sonnet and GLM-4v, which achieve 66.97% and 66.82%, respectively, highlighting GPT-4o’s superior ability to handle complex abstractions. The standard deviations across models at the three difficulty levels are 1.26%, 2.14%, and 3.74%, indicating increased disparity as task difficulty rises. Notably, models like GPT-4o and GLM-4v show more consistent performance with smaller score variations, suggesting stability across difficulty levels. In contrast, models like Claude-3.5 and Qwen2-VL exhibit greater score fluctuations, indicating higher sensitivity to difficulty changes.

Models demonstrate superior performance in semantic and atmospheric understanding while lagging in spatial and reasoning tasks. As illustrated in LABEL:tab:average_scores_transposed, our results reveal a consistent pattern across difficulty levels. Models excel in semantic and atmospheric understanding, maintaining high accuracy even at the hard level of 74.52% and 75.66% respectively. In contrast, spatial and reasoning prove more challenging, with accuracy dropping to 63.00% at the hard level. These findings indicate that while LVLMs have developed strong capabilities in comprehending semantic content and scene atmosphere, they still struggle with tasks involving spatial relationships and complex visual reasoning. To address these limitations, we suggest future research could explore training strategies that emphasize enhancing spatial reasoning and complex visual problem-solving capabilities in LVLMs.

### 4.4 Model Rank Overview

Figure: Figure 8: The model performance ranking given five user inputs under different difficulty levels.
Refer to caption: x8.png

Figure 8 reveals distinct performance patterns among different models under various difficulty. Notably, models like GPT-4o, while not exhibiting a significant advantage in simpler tasks, demonstrate outstanding performance in more challenging scenarios (*e.g.*,hard questions). Conversely, models such as GLM-4v perform well on easier tasks but show diminished capabilities as task difficulty increases. This indicates an imbalance in the model’s capabilities across different difficulty levels, highlighting the importance of cross-difficulty evaluation. It also demonstrates that AutoBench-V is effective in revealing such imbalances.

### 4.5 Human Evaluation

We conducted human evaluations in two aspects: the effectiveness of guided description generation and the alignment between questions and reference answers. See the Appendix D for details on the human evaluation. We ultimately represented the results of the human evaluation using the alignment rate (the proportion of aligned samples out of the total).

**Table 4: Alignment rate of guided description generation.**
| Task | Easy | Medium | Hard |
| --- | --- | --- | --- |
| Before Guide | 94.32% | 82.30% | 77.14% |
| After Guide | 95.20% | 88.13% | 84.55% |
| $\Delta$$\uparrow$ | 0.88% | 5.83% | 7.41% |

Guided description generation. We developed description generation guidelines for each fine-grained aspect to reduce vagueness in image descriptions, ensuring better alignment with themes and preventing discrepancies. A human evaluation showed that these guidelines significantly improved question-answer alignment, especially in more challenging tasks, as shown in LABEL:Q&A.

Question-Answer alignment. After implementing the generation guidelines, we conducted a human evaluation to assess the accuracy of the questions and answers generated by the examiner model. As shown in LABEL:Q&A, the evaluation resulted in high scores, confirming the model’s effectiveness in producing well-aligned question-answer pairs for image-based tasks.

### 4.6 Position Bias

Figure: Figure 9: Comparison of answer distribution under position bias conditions. Correct answers at A or D v.s. correct answers are evenly distributed at A,B,C,D.
Refer to caption: x9.png

Since the reference answers generated by LLMs tend to cluster around option A, we manually set the correct options to be evenly distributed. To investigate the necessity of this approach, we conducted experiments to examine potential position bias (Zheng et al., ). We evaluated scenarios where all correct answers were placed in either options A and D, comparing the resulting scores with the evenly distributed case (*i.e.*, 25% for each option), as shown in Figure 9. The deviation rate was calculated using the following formula: $R=\frac{S_{X}-S_{U}}{S_{U}}$, where $S_{X}$ is the model score for condition $X$ (either $A$ or $D$), and $S_{U}$ is the score for the scenario when options are evenly distributed.

The position bias becomes more evident with increasing question difficulty. For instance, at the hard level, GLM-4V showed a significant bias, with deviation rates of $R_{A}=-19\%$ and $R_{D}=-8\%$, suggesting a notable bias when correct answers were concentrated in options A or D, compared to the uniform distribution scenario. Thus, our approach of manually setting an even distribution of answers to avoid position bias is justified and necessary.

## 5 Conclusion

In this work, we introduce AutoBench-V, a fully automated framework designed for benchmarking LVLMs. The framework integrates a series of innovative modules that ensure diversity and reliability in dataset generation, as well as impartiality in model evaluation. Through extensive experiments, we have demonstrated the robustness and unbiased nature of the evaluation process facilitated by AutoBench-V. The insights gleaned from our research provide a solid foundation for future investigations in this field.

## Appendix A Details of Experiment Setting

Model Selection. The details of models selected in our experiments are shown in Table 7.

Computing Resource. For our experiments, all open-source vision-language model inferences were performed locally using NVIDIA GeForce RTX 4090 GPU with 24GB VRAM.

Alignment Evaluation. Inspired by tifa (Hu et al., ), we generated consistency tests for images across 12 aspects:object, human, animal, food, activity, attribute, counting, color, material, spatial, location, shape, other. For details on the score distribution of the consistency tests without threshold, please refer to Table 5.

**Table 5: Alignment score ($S_{i}^{d}$) distribution without setting threshold.**
| Level | $\leq$ 40% | 40%$\sim$60% | 60%$\sim$80% | $\textgreater$ 80% |
| --- | --- | --- | --- | --- |
| Spatial Understanding. |  |  |  |  |
| Easy | 0.51% | 2.54% | 7.61% | 89.34% |
| Medium | 0.53% | 3.17% | 19.05% | 77.25% |
| Hard | 1.03% | 5.64% | 16.92% | 76.40% |
| Basic Understanding. |  |  |  |  |
| Easy | 1.94% | 2.90% | 14.01% | 81.16% |
| Medium | 3.38% | 5.80% | 14.98% | 75.85% |
| Hard | 2.86% | 8.57% | 24.76% | 63.81% |
| Semantic Understanding. |  |  |  |  |
| Easy | 0.99% | 3.94% | 8.37% | 86.70% |
| Medium | 0.49% | 4.39% | 19.02% | 76.10% |
| Hard | 1.93% | 4.35% | 17.39% | 76.33% |
| Reasoning Capacity. |  |  |  |  |
| Easy | 1.93% | 3.86% | 14.49% | 79.71% |
| Medium | 2.53% | 6.06% | 23.23% | 68.18% |
| Hard | 6.22% | 11.92% | 28.50% | 53.37% |
| Atmospheric Understanding. |  |  |  |  |
| Easy | 0.48% | 2.41% | 11.11% | 85.99% |
| Medium | 1.38% | 2.76% | 17.97% | 77.88% |
| Hard | 1.79% | 6.55% | 24.40% | 67.26% |

Figure: Figure 10: Visualization of image topic words. Topic words are converted into vectors using bge-large-en-v1.5 (Xiao et al., ), then perform dimensionality reduction via t-SNE (Van der Maaten & Hinton, ). Topic word distribution without semantic graph (a)(c) and with semantic graph (b)(d). It can be seen that with the semantic graph the diversity of topic words increases and the over-reliance on high-degree words is reduced.
Refer to caption: x10.png

Figure: Figure 11: Topic words visualization using semantic graph under basic understanding.
Refer to caption: x11.png

Figure: Figure 12: Topic words visulization without using semantic graph under basic undertstanding.
Refer to caption: x12.png

**Table 6: Performance (Accuracy) details of all models on five user inputs and three difficulty levels.**
| Model | User Input$\uparrow$ |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Basic. | Spatial. | Seman. | Reason. | Atmos. | Average |  |
| Easy |  |  |  |  |  |  |
| GPT-4o | 90.18% | 86.09% | 93.81% | 88.13% | 93.94% | 90.43% |
| GPT-4o mini | 90.18% | 81.28% | 91.24% | 81.92% | 95.45% | 88.01% |
| Gemini-1.5-Flash | 89.29% | 81.82% | 91.19% | 85.31% | 92.75% | 88.07% |
| Claude-3.5-Sonnet | 91.07% | 83.96% | 91.75% | 85.31% | 94.33% | 89.28% |
| Claude-3-Haiku | 89.29% | 80.21% | 90.72% | 82.49% | 91.41% | 86.82% |
| GLM-4v | 91.96% | 83.96% | 87.01% | 92.78% | 96.45% | 90.43% |
| Qwen2-VL | 90.18% | 81.82% | 92.27% | 87.57% | 96.00% | 89.57% |
| Medium |  |  |  |  |  |  |
| GPT-4o | 76.87% | 72.25% | 83.64% | 81.95% | 84.35% | 79.81% |
| GPT-4o mini | 76.19% | 67.26% | 79.09% | 77.56% | 84.78% | 76.98% |
| Gemini-1.5-Flash | 73.47% | 66.96% | 78.18% | 70.44% | 84.14% | 74.64% |
| Claude-3.5-Sonnet | 75.51% | 67.84% | 77.73% | 74.63% | 81.74% | 75.49% |
| Claude-3-Haiku | 72.11% | 64.76% | 78.90% | 67.31% | 83.91% | 73.40% |
| GLM-4v | 74.83% | 74.89% | 79.91% | 74.63% | 82.17% | 77.29% |
| Qwen2-VL | 82.31% | 74.01% | 80.45% | 73.17% | 85.21% | 79.03% |
| Hard |  |  |  |  |  |  |
| GPT-4o | 69.12% | 68.28% | 79.36% | 76.50% | 81.82% | 75.02% |
| GPT-4o mini | 63.43% | 61.23% | 81.65% | 69.94% | 77.27% | 70.70% |
| Gemini-1.5-Flash | 66.91% | 65.20% | 77.10% | 68.16% | 76.88% | 70.85% |
| Claude-3.5-Sonnet | 59.56% | 64.60% | 66.97% | 58.47% | 68.75% | 63.67% |
| Claude-3-Haiku | 59.56% | 58.15% | 73.39% | 71.58% | 74.43% | 67.42% |
| GLM-4v | 61.03% | 60.79% | 66.82% | 66.12% | 69.89% | 64.93% |
| Qwen2-VL | 64.71% | 64.76% | 79.36% | 71.03% | 79.59% | 71.89% |

**Table 7: Model names, Creators, whether it is open source, and their purpose.**
| Model | Creator | Open-Source | Purpose |
| --- | --- | --- | --- |
| GPT-4o | OpenAI | \faTimesCircleO | Examiner&Candidate |
| GPT-4o mini | \faTimesCircleO | Candidate |  |
| Gemini-1.5-Flash | Google | \faTimesCircleO | Candidate |
| Claude-3.5-sonnet | Anthropic | \faTimesCircleO | Candidate |
| Claude-3-haiku | \faTimesCircleO | Candidate |  |
| GLM-4v | Zhipu AI Inc. | \faTimesCircleO | Candidate |
| Qwen2-VL | Alibaba | \faCheckCircleO | Candidate |
| Flux-pro | Black Forest Labs | \faTimesCircleO | Image generation |

Figure: Figure 13: We randomly sampled 75 question instances for each user input, with 25 questions from each difficulty level. We then visualized the response patterns of various models to these questions (the red means). The rows in each user input, from top to bottom, represent the response situations of Claude-3.5-sonnet, Claude-3-haiku, Gemini-1.5-flash, GLM-4v, GPT-4o, GPT-4o mini, and Qwen2-VL, respectively.
Refer to caption: x13.png

Figure: Figure 14: Visualization of all image descriptions in AutoBench-V.
Refer to caption: extracted/5962432/figure/description.png

## Appendix B Details of User Input

In this section, we provide a comprehensive overview of the levels at which we categorize user inputs based on linguistic aspects. Our goal is to offer a comprehensive and broad representation of user requirements for LVLMs. However, as it is challenging to exhaustively cover every aspect, we base our categorization on aspects derived from the literature (Li et al., ; Meng et al., ; Liu et al., ). These aspects are considered representative and comprehensive examples of the capabilities of LVLMs and other aspects like in (Chen et al., ; Xu et al., ) can be handled in a similar manner, without requiring additional fine-tuning or adjustments, as our framework is highly extensible, allowing users to propose their own aspects as needed.

### B.1 Basic Understanding

Definition and Goal: Basic Understanding refers to the recognition and identification of individual objects, characters, and scenes within an image. The goal is to accurately detect and label relevant elements, providing a foundation for more advanced tasks such as object tracking and scene interpretation (Wu et al., ; Xue et al., ).

Requirement. This task demands the ability to detect specific objects and differentiate between various types of objects. Additionally, it involves understanding the broader context of the scene and identifying real-life settings to enable accurate interpretation of the image’s overall content.

### B.2 Spatial Understanding

Definition and Goal.
Spatial Understanding refers to the interpretation of the spatial arrangement and positioning of objects within an image (Cai et al., ; Guo et al., ). The goal is to comprehend both two-dimensional and three-dimensional relationships, determining which objects are in the foreground or background, assessing their relative sizes and orientations, and understanding how they are positioned within the scene.

Requirement.
This task demands the ability to perceive depth, estimate distances between objects, and analyze how objects interact within the physical space of the image, providing a more accurate understanding of the spatial structure and context.

### B.3 Semantic Understanding

Definition and Goal.
Semantic Understanding involves interpreting the higher-level meaning and relationships within an image (Meng et al., ). The goal is to move beyond simple object identification to understand the roles and interactions between objects, such as recognizing that a person is riding a bike or that two people are engaged in conversation. This level of understanding aims to capture the context and intent behind the scene, identifying how elements relate to each other to form a coherent narrative or message.

Requirement.
This task requires discerning the interactions and relationships between objects, understanding their roles within the scene, and interpreting the overall context to accurately derive the narrative or intended message conveyed by the image.

### B.4 Atmospheric Understanding

Definition and Goal.
Atmospheric Understanding focuses on grasping the mood, tone, and emotional ambiance conveyed by an image. The goal is to interpret not just what is depicted or how elements are arranged, but also how the scene feels and the emotional resonance it conveys to the viewer. For instance, an image of children laughing under warm sunlight in a lush park combines their expressions, bright colors, and soft lighting to create a joyful and carefree atmosphere.

Requirement.
This task requires the ability to capture and interpret subtle emotional cues and tonal qualities of the scene, distinguishing the overall mood and emotional impact of the image from more analytical aspects like semantic or spatial understanding.

### B.5 Reasoning Capacity

Definition and Goal.
Reasoning Capacity involves interpreting and analyzing the relationships and logical connections between different elements within an image (Zhou et al., ; You et al., ). The goal is to infer potential outcomes, understand causal relationships, and make predictions about what might happen next based on visual cues. For example, if a person is holding an umbrella and the sky is dark, reasoning capacity would suggest that it might rain soon. This level also includes understanding abstract relationships, such as social dynamics or the intent behind actions, and making judgments about what is likely or possible given the visual information.

Requirement.
This task requires the ability to analyze logical connections between elements, infer outcomes, and understand causal relationships, as well as to interpret abstract concepts and make predictions based on the visual context.

## Appendix C Details of Difficulty Grading

This section describes in detail the difficulty levels for the pictures and questions used in prompts respectively. The following is the instruction guiding the examiner model to generate image descriptions and questions of varying difficulty levels.

### C.1 Image Description

Easy difficulty. Generate images with very simple elements, focusing on single, easily recognizable objects placed against a plain or neutral background. The descriptions should be straightforward and unambiguous, *e.g.*, “a red apple on a white background.” The focus is on clarity and simplicity, with minimal detail or interaction.

Medium difficulty. Introduce scenes where the required elements interact with their environment naturally but uncomplicatedly. The setting may include multiple common objects and a familiar context, but the composition remains clear and not overly complex, *e.g.*, “a cup on a table in a well-lit kitchen.” The background and context are present but not overwhelming, and there are no intricate details or unusual perspectives.

Hard difficulty. Craft descriptions that involve multiple elements interacting with each other, set in a more complex environment. Use varied perspectives, detailed textures, or lighting conditions that add layers of difficulty, *e.g.*, “a reflection of a cat looking out of a rain-soaked window, with a cityscape in the background at dusk.” The focus is on creating a rich and intricate scene that challenges the model’s ability to render interactions, depth, and subtleties in lighting and perspective.

Moreover, we standardized the description of the observer’s perspective in the image description to prevent directional issues from causing confusion. For instance, ambiguities could arise in interpreting relative directions such as left and right, as these can vary significantly depending on the observer’s viewpoint.

### C.2 Question

Easy difficulty. Focus on questions that require identifying simple, prominent, and explicit details within the image. These questions should be straightforward, relying solely on basic observation without the need for inference or interpretation. For example, you might ask about the color of a specific object, the presence of a single item, or the shape of an easily recognizable feature. The key is to keep the questions direct and simple, ensuring that the answer is obvious and immediately visible in the image.

Medium difficulty. Design questions that necessitate a moderate level of observation and inference. These questions should involve understanding relationships between elements, recognizing interactions, or identifying less prominent features that are still clear but not immediately obvious. Examples could include questions about the relative position of objects, identifying an action taking place, or understanding the context of a scene. The goal is to require some level of thought beyond basic observation, challenging the model to understand the scene’s composition or narrative without being overly complex.

Hard difficulty. Create questions that require the model to notice and interpret more detailed aspects of the image. These questions should involve recognizing multiple elements working together, understanding more complex interactions, or identifying details that are present but not immediately obvious. For example, you might ask about the positioning of objects relative to each other in a more crowded scene, subtle changes in lighting or color that affect the appearance of objects, or identifying an element that is not the main focus but is still visible in the background. The aim is to challenge the model to go beyond surface-level details, but without making the task too abstract or overly difficult.

## Appendix D Human Evaluation

### D.1 Details of Human Evaluation

The evaluation was carried out by a panel of five evaluators: three undergraduate students and two PhD students, all possessing professional English skills. Sample annotation screenshots from the human evaluation process are presented in Figure 20 and Figure 21. To ensure unbiased results, each evaluator independently assessed all samples. A sample was considered aligned if it received a majority vote (*i.e.*, more than half of the evaluators agreed on its alignment).

### D.2 Human Evaluation Guidelines

In this section, we outline the guidelines followed during human evaluations to ensure reliability and validity.

For Description Generation Guideline, the evaluators need to consider the following three points:

$\triangleright$ Alignment with Image: The main criterion is how well the generated description reflects the visual content. Descriptions must accurately correspond to the image, avoiding vague or abstract expressions, as well as hallucination (Huang et al., ). Each description should provide clear, specific details that align with the image content and the defined fine-grained aspects.

$\triangleright$ Specificity and Clarity: Ensure that descriptions are specific, directly related to the image, and free from ambiguous or overly generalized language.

$\triangleright$ Relevance to Aspects: Assess whether the description aligns with the corresponding themes and expected content. Descriptions must clearly communicate the intended visual elements and avoid any misalignment between the image and the description.

For Question-Answer Alignment, there are two points that the evaluators should consider inspired by the previous study (Liu et al., ):

$\triangleright$ Clarity and Accuracy: Each question must be clear, unambiguous, and directly derived from the image. The answers should correspond to observable details or logical inferences from the image, with only one correct answer for each question. There should be no irrelevant or misleading information in the questions or answers.

$\triangleright$ Consistency with Image: Verify that both the question and answer are directly based on the image’s content. The evaluation should ensure that there is a logical and clear relationship between the visual cues and the generated question-answer pair, particularly for tasks involving higher difficulty.

## Appendix E Error Study

Through extensive experimental analysis, we have categorized the common problems encountered by LVLMs in VQA tasks into two main types: image comprehension errors and image reasoning errors. Regarding the first category, LVLMs often fail to truly understand the details in an image. For instance, in Figure 15, the model failed to notice the kite flying in the upper right corner of the image and incorrectly identified a non-existent action of a squirrel climbing a tree. In Figure 17, the model mistakenly perceived the firefighter as holding the crowbar with only one hand instead of two. In Figure 18, the model failed to recognize that the apple’s stem was slightly tilted to the left. These errors demonstrate the model’s inadequate comprehension of image details.

Image reasoning errors occur when models accurately perceive the image content but falter in their reasoning process, leading to incorrect answers. For instance, in Figure 16, the model correctly recognizes that the image does not depict a cheering crowd. However, during subsequent reasoning, it convinces itself otherwise, ultimately selecting option C while neglecting to analyze other choices in its explanation. Figure 19 exemplifies a similar issue: the model correctly identifies that the child is wearing a yellow shirt but after mentioning the red of the kite it erroneously selects B. Red is its final answer. These examples highlight a disconnect between visual perception and logical reasoning in LVLMs, where initial accurate observations can be overridden by flawed deductive processes.

Figure: Figure 15: Error study of Claude-3-Haiku under basic understanding.
Refer to caption: x14.png

Figure: Figure 16: Error study of Claude-3.5-Sonnet under spatial understanding.
Refer to caption: x15.png

Figure: Figure 17: Error study of Qwen2-VL under semantic understanding.
Refer to caption: x16.png

Figure: Figure 18: Error study of GLM-4v under reasoning capacity.
Refer to caption: x17.png

Figure: Figure 19: Error study of Gemini-1.5-Flash under atmospheric understanding.
Refer to caption: x18.png

Figure: Figure 20: Screenshot of Human Evaluation (Example 1).
Refer to caption: extracted/5962432/figure/screenshot.png

Figure: Figure 21: Screenshot of Human Evaluation (Example 2).
Refer to caption: extracted/5962432/figure/screenshot2.png

## Appendix F Prompt Template

Figure: Figure 22: Prompt template for generating aspects. Here we use the task of basic understanding as an example.

Figure: Figure 23: Prompt template for generating guideline.

Figure: Figure 24: Prompt template for generating image descriptions with difficulty grading.

Figure: Figure 25: Prompt template for generating questions.

Figure: Figure 26: Prompt template for LVLM to answer.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 2
questions: 1. **Data Quality**: How can the quality of generated data be controlled while automatically constructing datasets? Is there a difference in quality across different data categories?
2. **"Unnecessary Visual Content" Issue**: Does the AutoBench-V incorporate effective strategies to address the issue of "unnecessary visual content" as discussed in [MMStar](https://arxiv.org/pdf/2403.20330) and [NaturalBench](https://arxiv.org/pdf/2410.14669?)? The serious issue can lead to questions being answered correctly without the image.
soundness: 3
strengths: The idea in this paper is excellent as it can reduce the human cost involved in constructing datasets and provides multiple evaluation categories.
summary: The paper presents a novel automated framework for evaluating Large Vision-Language Models and conducts a detailed experimental analysis on seven popular LVLMs.
weaknesses: The paper should include more discussion on quality control for the dataset, such as avoiding issues with generating incoherent or low-quality images and questions.

---

confidence: 4
contribution: 2
initial_rating: 1
presentation: 1
questions: Please provide some random samples of your dataset.

In chapter 3.4 the defects here might come from either the text-to-image model not properly generating that part of the text, or from the VLM not being able to answer despite the image being correct. How do you separate those two cases?
soundness: 2
strengths: The generation procedure is interesting and the generated samples look reasonable. The models are evaluated in detail.
summary: Authors suggest an automatic way to evaluate LVLMs by using GPT-4 to generate image descriptions, questions and answers, and FLUX to generate images from those descriptions. The created benchmark contains five different evaluation dimensions and the authors evaluate 7 different VLMs on their benchmark.
weaknesses: Unfortunately the presentation quality is absolutely not ready for publication. The very first sentence of the introduction already contains several errors: Authors cite the “Attention is all you need” from 2017 as a 2023 paper. LLaVA is cited as an LLM even though it is a vision model. And the formulation suggests that the LLM works from 2023 pave the way for NLP works from 2020, which cannot hold.

This continues in the related work section, where this paper is cited as past work: (1) “Deep visual-semantic alignments for generating image descriptions” with then a subsequent study (2): “Microsoft COCO: Common Objects in Context**”** mentioned. However in reality it is the other way around, where paper (1) uses the COCO dataset (2) for evaluation. Then, ImageNet is cited as a “benchmark for assessing LVLMs”, however it is an image classification dataset. Finally ImageNet is cited as an arxiv paper instead of properly citing it as a International Journal of Computer Vision paper. Clearly these citations were not thoroughly examined.

Citations in the references are all lowercased, probably due to incorrect usage of bibtex. English quality is low which makes the paper difficult to read, e.g. the sentences in lines 85-87, and 136-137 could be written more cleary. The paper contains typos like “fine-frained” aspect on line 215.

Method: The benchmark covers “atmospheric understanding”, however the cited paper does not contain the word atmospheric but talks about emotion recognition (one of the 4 aspects under atmospheric understanding). Figure 1 does not properly show which question belongs to which aspect, leaving the reader guessing. Also, the hierarchical structure seems random, e.g. it contains “emotional and psychological” on the left under semantic understanding, and “emotional cues” under “atmospheric understanding”. This hints to problems of automatically generating this hierarchy of aspects using GPT-4. Since it is only a few aspects, those could have been generated manually.

The guidelines chapter is not understandable to me. Sentences like “This guideline acts as a guideline…” do not help. Details are missing: How is the exact procedure? The descriptions of the method are more prosaic than scientific. The reader has to find and read the prompt in figure 23, which is not referenced to in the chapter.

Similarly in the next paragraph, image description with difficulty grading. Even after reading Appendix C.1 it is unclear which prompts are used, and how any of this would not suffer from the common weaknesses of VLMs (e.g. hallucinations, or ignoring some parts of the text when generating images).

In the semantic graph part, the authors describe what the function f could be, but miss describing what it actually is in the end.

A big problem in VQA is that questions can be ambiguous. E.g. in figure 198 the question is formulated in a way that the answer “the stem appears to be pointing upwards” is reasonable but does not match the ground truth.

In figure 19 the ground truth is just wrong, since the child is not wearing a red clothing. Just from these two examples, the generated benchmark seems to be too low quality to provide meaningful results.

Summary: The presentation quality must be improved substantially which would suggest a resubmission. The benchmark quality is at least questionable but in the currently presented form cannot be judged well. Not properly citing related work is a KO criterium.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 4
questions: To yield fairer benchmark baselines, I highly suggest the authors amend this **being the player and the referee at the same time** issue by employing multiple Examiners in the AutoBench-V pipeline using the same prompts from the Appendix. As in (Ye et al., 2024), please first make sure each candidate LVLM be evaluated across multiple Examiners that are *isolated* from the candidate model itself.

Also, may I know how suboptimal are the other T2I models such as Stable Diffusion as the authors have mentioned in Section 4.1? The model size could also be an important contributing factor to the performance on AutoBench_V.


Ye et al. Justice or prejudice? quantifying biases in llm-as-a-judge. 2024. https://arxiv.org/abs/2410.02736
soundness: 1
strengths: The authors have shown tremendous efforts to build up the comprehensiveness of AutoBench-V's design. It is a fluent experience to read through the manuscript in order to interpret the flow of the pipeline.
summary: This work proposes an LVLM-as-Judge pipeline, AutoBench-V, that automatically produces VQA examples for LVLM evaluation. AutoBench-V can be configured to cover multiple aspects for vision-language understanding, and to produce test cases in diverse difficulties.
weaknesses: However, my biggest concern lies in the experiments. In fact, there is one critical issue that greatly undermines my overall impression regarding the value of this work - 

Since all the prompts and the test cases are generated using GPT4o on-the-go, which is the sole Examiner/Judge in the paper's setting, **aren't the baseline performances in Table 2 by GPT-4o technically obtained by testing on the training set?** 

Even if the prevention actions of self-enhancement leakage are done on the image description text encoder factor as stated in Section 4.2, the ad hoc-generated visual examples used in the test cases, as well as the guidelines used along the pipeline, are not fully deprived of the confounding factor that is the prior brought by GPT-4o. Thus, the top-benchmarking performances by GPT-4o, and to a lesser extent GPT4o-mini, in Table 2, are very unconvincing results to me.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: Please refer weaknesses for details.
soundness: 3
strengths: 1. This study represents the automatic evaluation of multimodal large language models and has a certain degree of noverty.
2. The paper is elegantly written, and the automatic evaluation process designed in the methodology section is clearly written.
summary: This paper introduces AUTOBENCH-V, an innovative framework for automatically testing LVLMs. AUTOBENCH-V combines text-to-image generation with the visual question-answering task performed by LVLMs. The framework generates images according to specific evaluation criteria and evaluates LVLM performance through customized visual questions, combining self-verification and error control mechanisms to ensure the correctness of the generated visual question-answer pairs.
weaknesses: 1. "they lack the flexibility xxx" in 053 may involve overclaim. For some charts and flowcharts (ChartQA, ScienceQA), it is difficult to draw these pictures using the T2I model, but this ability is also need to be considered. Many benchmarks consider inputting charts, documents and other pictures (MMT-Bench[1]). Although the author construct many types of tasks, all of these input pictures seem to be natural images. So I don't think this can completely become the reason for the author to claim that he is more flexible than other benchmarks.

2. The questions are all generated through LLM, lacking visual information. Although LLM is very common in constructing QA data, visual information is also very critical in multimodal evaluation. Simply using LLM to generate questions will ignore this aspect (for example, questions like: what is the object in the upper left corner of the picture; what is the red square object in the picture)

3. The author only evaluated seven LVLMs and there is only one open source model. Compared with other benchmarks (such as MME[2]), most of them evaluate 20+ LVLMs, which affects the second point stated by the author, contribution in line 123: "Extensive experiments...", which seems not to be extensive.

4. The author's contribution mentioned that the construction of AutoBench-V greatly reduced the cost of manually constructing benchmarks, which may be an overclaim. Although the author conducted a manual evaluation of the correctness of the automatically constructed benchmark, I prefer to see the manual comparison results of the automatically constructed benchmark and the manually collected benchmarks (like MME[2], MMBench[3], MEGABench[4]) in terms of quality, diversity, etc. This result better illustrates this contribution (in other words, it is not difficult to automatically build a benchmark, but it is difficult to have similar quality to the manually collected benchmarks)

5. The benchmark built by AutoBench-V may be too simple. GPT-4o has an accuracy of 75% on the hard type defined by the author and an accuracy of 90% on the easy type, which is too high compared to other benchmarks, e.g. GPT-4o only has 54% on MEGA-Bench[4].


[1] MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

[2] MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

[3] MMBench: Is Your Multi-modal Model an All-around Player?

[4] MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks