Venue: NAACL 2025

# MLLM-Bench: Evaluating Multimodal LLMs

with Per-sample Criteria

Wentao Ge , Shunian Chen1, Guiming Hardy Chen1, Nuo Chen, Junying Chen,

Zhihong Chen2, Wenya Xie, Shuo Yan, Chenghao Zhu, Ziyue Lin, Dingjie Song,

Xidong Wang, Anningzhe Gao, Zhiyi Zhang, Jianquan Li, Xiang Wan, Benyou Wang3

Shenzhen Research Institute of Big Data

The Chinese University of Hong Kong, Shenzhen

{wentaoge,shunianchen,guimingchen,zhihongchen}@link.cuhk.edu.cn wangbenyou@cuhk.edu.cn

Footnote 1: The first three authors contribute to this work equally.

###### Abstract

Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating _objective_ queries without considering _real-world_ user experiences, inadequately addressing the nuances of creative and associative multimodal tasks. However, the _open-ended_ and _subjective_ nature of such tasks poses a significant challenge to the evaluation methodology, where it is difficult to define the ground-truth answers for them. To this end, in our paper, we propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with _per-sample criteria_ using potent MLLM as the judge. To validate the feasibility and effectiveness of this paradigm, we design a benchmark, dubbed **MLLM-Bench**, by curating the evaluation samples across six comprehensive cognitive levels. We benchmark 26 popular MLLMs in a pairwise-comparison fashion, showing diverse performance across models. Moreover, the validity of our benchmark manifests itself in reaching 88.02% agreement with human evaluation. We contend that the proposed paradigm explores the potential of MLLMs as effective evaluation tools with the help of per-sample criteria. Our code and data are publicly released at [https://github.com/FreedomIntelligence/MLLM-Bench](https://github.com/FreedomIntelligence/MLLM-Bench).

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Notably, rather than providing a standard answer to a posed question, we offer one to three specific _evaluation criteria for each question_. These criteria are then applied in conjunction with the posed question and the model's response to assess performance more accurately (see an example in Table 1).

The contributions of this paper are two-fold: 1) **A new paradigm to evaluate MLLMs** utilizing **powerful MLLMs** along with **per-sample criteria**, which shifts from closed-form-answer evaluations for MLLMs to a flexible, criteria-based approach, particularly suited for open-ended tasks. It recognizes and acknowledges a spectrum of valid responses and evaluates the answer quality based on how well they align with these criteria, assessing models beyond the single "correct" answer limitation. 2) **A benchmark dataset** with a comprehensive taxonomy that categorizes tasks and scenarios for MLLMs, with a strong emphasis on ethical considerations. In the benchmark, we conduct a **systematic benchmarking of existing MLLMs**. The benchmarking is unique since it might be well-aligned to user experience in real-world applications where questions are usually without unique groundtruth.

## 2 Background: Evaluations for MLLMs

It is challenging to comprehensively assess the capabilities of MLLMs . Current benchmarks primarily fall into several categories: (1) Multiple-choice questions (evaluating the perception and cognition abilities of MLLMs): MME Fu et al. (2023), SEED Li et al. (2023), and TouchStone Bai et al. (2023b); (2) Arena-like evaluation (user-based evaluation of different capabilities): LVLM-eHub Xu et al. (2023), VisIT-Bench Bitton et al. (2023); (3) Hallucination assessment (focusing on a key issue currently faced by MLLMs - hallucinations): POPE Li et al. (2023e) and HallusionBench Liu et al. (2023a). The works most related to us are (i) MMBench Liu et al. (2023d) and MM-Vet Yu et al. (2023), using GPT-4 as the evaluator to quantitatively measure the performance of different MLLMs; (ii) a concurrent work Zhang et al. (2023) uses GPT-4V to evaluate text-to-image generation.

Open- and Closed-ended BenchmarksExisting benchmarks are categorized into either open-ended or closed-ended with different evaluation approaches. Although close-ended benchmarks such as MMLU Hendrycks et al. (2020), C-Eval Huang et al. (2023) for LLMs and MME Fu et al. (2023) and SEED Li et al. (2023a) for MLLMs are convenient to evaluate on, they often suffer from data contamination issue. The results of close-ended benchmarks are especially for proprietary LLMs whose training data are all _in-house_. On the other hand, open-ended benchmarks (e.g., MT-Bench Zheng et al. (2023) and Alpaca-Eval Li et al. (2023d))

\begin{table}
\begin{tabular}{l l}  & **Question**: Based on the visual cues, estimate the approximate height of the woman. \\  & **Answer 1 (LLAvA-v1.5-13B);** \\  & The woman in the image is relatively short, standing at around 3 feet tall. \\  & & **GPT-4-Turbo Judgent** \\ Upon reviewing the provided responses in relation to the visual cues from the image and the given evaluation criteria: \\ Answer 1 suggests that the woman is “around 3 feet tall.” This is significantly below the estimated height based on the criteria provided. Given that Yao Ming is 292 cm and Shaquille O’Neal is 216 cm, and considering the woman’s height relative to them, the estimate of 3 feet (approximately 91 cm) is not accurate. \\ Answer 2 estimates the woman’s height to be “around 5 feet or less”. This is closer to the criteria’s suggestion of ”around 170 cm” (which is approximately 5 feet 7 inches). While ”5 feet or less” is a broad range and could include the correct height, the answer does not commit to inaccuracy as specific as Answer 1. \\ Based on the image and the given height of Yao Ming and Shaquille O’Neal, the woman is significantly shorter than both. However, she is not as short as 3 feet, as suggested by Answer 1. Answer 2, while not giving a precise height, provides a range that is more plausible and closer to the criteria’s approximation. \\ Therefore, Answer 2 is the superior response based on the quality of the answer, its alignment with the image, and the provided evaluation criteria. \\ \end{tabular}
\end{table}
Table 1: Pair-wise evaluation using per-sample criteria for MLLMs. The per-sample criteria is only available to the GPT-4 Turbo judge and are not accessible to evaluated MLLMs such as LLAvA-v1.5-13B and LVIS-invinct4v-LLaVA-7B for answer generation. **GPT-4-Turbo Judgent** shows the judgment rationale of GPT-4-Turbo and is for demonstration purposes. Our experiment only requires the judge to output the judgment result (see details in Section 5.1).

 

test models via free-form generation, which is more consistent with real-world use cases and relies heavily on LLMs' generation ability. The issue of data contamination in open-ended benchmarks is less severe since there are no standard answers, and such contamination offers minimal assistance in benchmark hacking.

LLMs for MLLM EvaluationMMBench Liu et al. (2023), TouchStone Bai et al. (2023b), and MM-Vet Yu et al. (2023) employ LLM-based evaluation frameworks, leveraging advanced LLMs (_e.g.,_ GPT-4) to assess MLLMs. This approach encounters significant limitations due to the inability of text-only LLMs to perceive visual contexts directly. The idea of adopting GPT-4-Turbo, a potent MLLM, directly as a judge in this paper, marks a significant advancement in the field.

## 3 Motivation of MLLM Evaluation with Per-sample Criteria

### Motivations

Why MLLM-as-the-judge evaluation needs additional criteria?While potent MLLMs are potential evaluators, their assessment outcomes may not always align perfectly with factual accuracy or human standards. This discrepancy highlights the necessity for a more nuanced approach to evaluation: per-sample criteria. Per-sample criteria are designed to provide specific benchmarks and guidelines for each assessment task, aiding MLLM judge for MLLM evaluation. This approach is particularly valuable for tasks where the judge's capabilities might fall short, ensuring that evaluations remain robust even in areas of potential weakness. Unlike a single reference answer, per-sample criteria afford a broader and more flexible basis for assessment, making them ideally suited for evaluating open-ended questions. As we adopt GPT-4-Turbo as the judge in this paper, an example where GPT-4-Turbo falls short is shown in Appendix E.1.

Why criteria should be sample-specific?Different samples present unique challenges and requirements, making a one-size-fits-all approach to evaluation inadequate. Table 2 exemplifies the critical per-sample criteria essential for the nuanced evaluation of multi-modal large language models (MLLMs), especially in _Soft Reference_, _Range_, and _Evaluation Guideline_ categories. These criteria underscore the need for evaluations that adapt to the task's context and intricacies, showcasing the limitations of previous methodologies in comprehensively assessing MLLM capabilities.

### Benefits of Per-sample Criteria

Generalization of Referenced-based EvaluationFor questions with _objective_ answers, the criteria offer specific reference answers, as demonstrated in the first, third, and fifth samples in Table 4. In cases where the instructions require a _subjective_ description or yield ambiguous answers, the criteria supply essential information for formulating responses, exemplified by the second,fourth, and sixth samples in the same table. This approach enables the evaluation model (GPT-4-Turbo) to assess the quality of outputs using a well-defined standard.

Mitigation of Data ContaminationOne of the advantages of our per-sample criteria is its potential to alleviate data contamination problems. While we will continuously update our dataset, there is no guarantee that the samples have not been seen or used. To this end, we choose to withhold the per-sample criteria when publishing our dataset. Namely, the released version will only include the images and instructions. We believe that this approach significantly reduces the risk of contamination since even if models have been exposed to the images, they do not necessarily generate desired responses.

\begin{table}
\begin{tabular}{p{56.9pt} p{85.4pt} p{71.1pt}} \hline \hline
**Criteria Type** & **Description** & **Example criteria (images omitted)** \\ \hline Exact Referrence & Necessates precise answers, such as accurately determining an item’s location. & _Answer specifies cactus location as row 2, column 6._ \\ \hline Soft Reference & Requires identifying and translating contextually relevant texts, demanding linguistic address and current knowledge. & _Identify and translate text related to the novel coronavirus pneumonia in Wuhan._ \\ \hline Range & Allows for variability within defined limits, like estimating a person’s height, introducing flexibility. & _The height range of the woman should be 165cm to 175cm._ \\ \hline Evaluation Guide-line effects on responses. & Involes interpretative effects on response. & _Assess if the answer considers the environment’s condition, like submerged paths._ \\ \hline \hline  & & \\ \end{tabular}
\end{table}
Table 2: Typical per-sample criteria. The criteria are sampled from this benchmark.

 



## 4 MLLM-Bench Dataset

### Taxonomy of Capabilities

Due to the absence of a standardized framework for categorizing the capabilities of multimodal large language models, and acknowledging that vision-language models emulate human cognitive processes to a certain extent, we have chosen to adopt the revised Bloom's Taxonomy [20] as the framework for this benchmark. In reference to Bloom's Taxonomy, we manually conclude 42 capabilities of MLLMs across a hierarchy spanning six cognitive levels and create 10 questions for each capability. The six capability levels are shown below in Table 3. For each of the capabilities, we create 10 questions, resulting in a total of 420 image-instruction pairs, see details in Appendix A.

### Data Annotation

#### 4.2.1 Data Annotators

We have recruited six volunteers, all of whom are undergraduates, graduate students, or research assistants at a university with an all-English curriculum. Each volunteer is paid according to the local salary (i.e., equivalent to roughly 10 dollars per hour) and tasked with gathering data pertinent to a distinct capability level, thereby guaranteeing consistency within each specific category. The data collection phase spanned a duration of two weeks; they can complete it whenever convenient. Before data collection and annotation, they are instructed to follow a guideline. See details in Appendix B.1.

#### 4.2.2 Pipeline

The data collection and annotation module comprises four stages:

**I: Image Collection**: Volunteers gather the most recent images that are pertinent to the capabilities under examination, either from social networks or by capturing them in real life.

**II: Instruction Construction**: Utilizing GPT-4-Turbo, volunteers craft assessment instructions that are congruent with the requisite model capabilities, the context of the collected images, and the manually written prompts. This stage also includes a consistency check between the instructions and the corresponding images.

**III: Question Type Annotation**: Recognizing that our benchmark evaluates model performance on both traditional closed-ended questions and more exploratory open-ended tasks, volunteers are required to annotate the type of each question. This includes categorizing them as _open-ended_, _closed-ended_, or _compound_. Compound questions contain elements of both open-ended and closed-ended queries, offering a more comprehensive challenge to the models being tested.

**IV: Per-Sample Criteria Annotation**: Volunteers are asked to provide evaluation criteria based on the image, instructions, capability requirements, and question type for each item of data. These criteria include _exact references_, _soft references_, _acceptable ranges_, and _evaluation guidelines_.

#### 4.2.3 Guidelines for Annotators

Data ProtocolAs illustrated in Table 3, each entry in the MLLM-Bench dataset comprises three key components: 1) a contemporary **image** with a friendly license, 2) a **question** posed as it would naturally arise in real-world situations and 3) 1-3 customized **per-sample criteria** that offer guidelines specifically designed to complement the capabilities of GPT-4-Turbo, thus enabling a more logical and scientifically sound evaluation.

Guideline for the Data AnnotationThe guideline for data annotation emphasizes the importance of using recent images to avoid data leak

\begin{table}
\begin{tabular}{l c l} \hline \hline
**Level** & **\#Samples** & **Description** \\ \hline Perception & 70 & MLLMs retrieve information from multimodal inputs, using skills like object recognition and OCR. \\ \hline Understanding & 110 & MLLMs process perceived information to construct meaning, comprehending and interpreting data contextually. \\ \hline Applying & 60 & MLLMs apply knowledge to similar situations, such as using text-based knowledge to interpret images as in medical imaging. \\ \hline Analyzing & 120 & MLLMs break down information to explore relationships, performing tasks like attribute comparison or causal reasoning. \\ \hline Evaluation & 40 & MLLMs make judgments based on criteria and standards, like assessing image quality or discerning content authenticity. \\ \hline Creation & 20 & MLLMs synthesize information to generate new content, from visual storytelling to coding with vision. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Taxonomy of MLLM-Bench including examples at six cognitive levels.



age2, sourcing data from publicly licensed platforms like Twitter or direct captures with clear copyright status, ensuring image clarity while accommodating real-world quality variance, maintaining impartiality by excluding sensitive content, and promoting diversity in response formats to reflect complex real-world interactions. See details in Appendix B.2.

Footnote 1: [https://www.cdss.org/en/news/news_data-driven-learning-and-the-results-of-the-tourages-for-the-training-of-the-the-art-to-use-the-in-the-future-of-the-the-training-of-the-the-training-of-the-the-training-of-the-the-training-of-the-the-training-of-the](https://www.cdss.org/en/news/news_data-driven-learning-and-the-results-of-the-tourages-for-the-training-of-the-the-art-to-use-the-in-the-future-of-the-the-training-of-the-the-training-of-the-the-training-of-the-the-training-of-the-the-training-of-the) 

family**, and **Gemini-1.5-family** represent cutting-edge proprietary solutions, leveraging state-of-the-art techniques for multimodal understanding and generation. For open-sourced MLLMs, the LLaVAseries and their variants (LVIS, MiniGPT-v2, YiVL, and ALLaVA provide robust solutions for multimodal adaptation and generalization. Qwen-VL, Qwen-2-VL, Cheetor, and mPLUG-Owl2 use query tokens as the bridge between visual and textual representations. Similarly, Pixtral shows strong performance on multimodal tasks, while maintaining state-of-the-art performance on text-only benchmarks. It supports multiple images in the long context window of 128k tokens. LWM, with its ability to support a 1 million token context, pushes the boundaries of context handling in MLLMs. SEED-LLaMA uses a VQ-based encoder, while Fuyu adopts a single linear layer to link two modalities. Lastly, Phi-3-Medium strikes a balance between efficiency and accuracy, offering strong performance in multimodal tasks. Models are detailed in Appendix D.

### Evaluation Results

Table 5 presents the results of benchmarked models. The outcomes are quantified as the number of wins, ties, and losses for each model relative to LLaVAlv1.5-13B, with the models sorted by their total win rates in descending order. The last two columns list the Elo ratings and the rankings of models with respect to their Elo scores.

Overall, closed-source models demonstrate a leading advantage on the leaderboard, with some closed-source models such as Claude-3-5-sonnet and the Gemini-1.5 series showing outstanding performance. However, at the same time, the gap between open-source and closed-source models is narrowing. Leading open-source models like Pirstral and the Owen-2-VL series are exhibiting levels close to those of closed-source models. Nevertheless, we must acknowledge that there are significant variations in the performance of open-source models, and further efforts are needed in their pursuit to catch up with closed-source models.

In the meanwhile, we calculate the Spearman coefficient between the anchor-base method and

\begin{table}
\begin{tabular}{p{49.7pt} p{60.7pt} p{142.3pt} p{142.3pt}} \hline \multicolumn{1}{c}{**Capability**} & \multicolumn{1}{c}{**Image**} & \multicolumn{1}{c}{**Sample Questions**} & \multicolumn{1}{c}{**Criteria**} \\ \hline  & Food Recognition & What type of mushroom is it in the image? Is it edible? & What type of mushroom is it in the image? Is it answer is based on visual characteristics of the mushroom in the image. & Identify mushroom as galerina marginata. **2.** State the mushroom is not edible. **3.** Ensure the answer is based on visual characteristics of the mushroom in the image. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & Identify mushroom as galerina marginata. **2.** State the mushroom is not edible. **3.** Ensure the answer is based on visual characteristics of the mushroom in the image. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans’ large, colorful bills as a distinctive feature. **2.** Describe additional attributes: body color, size, and any unique markings. **3.** Include the setting or behavior if it aids in recognition. & I. Mention toucans' large, colorful bills as a distinctive feature.

 

the Elo method, achieving a value of 98.02%. This result demonstrates the robustness of our approach.

## 6 Validating Evaluation Paradigm and MLLM-Bench

We validate our evaluation paradigm through five critical dimensions: alignment with human judgments(Section 6.1), the impact of per-sample criteria (Section 6.2), robustness against test variations(Section 6.3), bias analysis(Section 6.4), and the consistency of different MLLM judges (Section 6.5).

### Alignment with Human Evaluation Settings

For this alignment experiment, we recruited four undergraduate volunteers4, all of whom are students at a university with an all-English curriculum. For volunteers participating in human evaluations, we sample 252 evaluation items. Each item in the evaluation set comprises a question associated with an image and two answers generated by models: one from the model under evaluation and the other from the anchor model LLaVA-v1.5-13B. We present the details of sample and model selection in Appendix F.1.

Footnote 4: Each volunteer is paid according to the local salary (i.e., equivalent to roughly 10 dollars per hour).

To check the consistency between human evaluators and GPT-4-Turbo, we calculate the agreement between each individual's results as well as the aggregated results of human evaluators and GPT-4-Turbo. The aggregated results are derived from a majority vote among four human evaluators. This step combines individual judgments into a consensus and addresses situations like indecision or equal preference. If selections for the evaluated model and the anchor model are equal, or if more evaluators choose "_unable to decide_" than any specific model, the outcome is classified as "_unable to decide_".

ResultsThe last row of Table 6 shows a high alignment between human evaluators and GPT-4-Turbo, with the agreement between individual evaluators and GPT-4-Turbo reaching 86.59% and the agreement between the aggregated results of human evaluators and GPT-4-Turbo reaching 88.02%. This high degree of alignment reaches the same level of agreement among human evaluators. The results validate the premise that GPT-4-Turbo can effectively mirror human judgment in the setting of MLLM evaluation, confirming GPT-4-Turbo as a reliable and effective tool in the evaluation process.

### Ablation on Per-sample Criteria

SettingsTo further demonstrate the superiority of our method, we include some baselines for comparison. **GPT-4 + detection**5 (w/ or w/o criteria): For

\begin{table}
\begin{tabular}{l c r r r r r r r} \hline
**Models** & **Perception** & **Understanding** & **Applying** & **Analyzing** & **Evaluation** & **Vine** & **Elo Ratings** & **Elo Rankings** \\ \hline Claude-3-5-sonnet & 67/0/3 & 106/4/40 & 56/2/2 & 90/8/2 & 38/1/1 & 39/0/1 & 0.94 & 1438 & 1 \\ Gemini-1-5-pron & 69/10 & 101/1/8/1 & 53/5/2 & 88/6/4 & 38/1/1 & 39/0/1 & 0.92 & 1368 & 2 \\ Gemini-1-5-H & 67/21 & 99/74 & 49/8/3 & 87/9/4 & 40/0/0 & 36/40 & 0.90 & 1346 & 3 \\ Claude-3-opus & 56/13/1 & 98/9/3 & 45/11/4 & 83/14/3 & 33/5/2 & 33/6/1 & 0.83 & 1267 & 4 \\ GPT-4-mini & 61/63 & 90/91/1 & 45/10/5 & 77/10/13 & 32/26/6 & 38/20/2 & 0.82 & 1189 & 5 \\ GPT-4-vision-preview & 56/10/4 & 92/9/9 & 40/17/3 & 84/11/5 & 32/26/6 & 33/5/2 & 0.80 & 1106 & 9 \\ Pixrat-12B & 55/8/7 & 88/81/4 & 46/7/7 & 72/14/14 & 32/2/6 & 36/1/3 & 0.78 & 1170 & 6 \\ Qwen-2-VL-72B & 49/5/16 & 78/15/17 & 40/10/10 & 72/7/21 & 32/26/6 & 32/17/2 & 0.72 & 1114 & 8 \\ Open-2-VL-7B & 56/10/4 & 67/24/19 & 39/13/8 & 64/12/24 & 33/6/11 & 32/6/2 & 0.67 & 1065 & 12 \\ LLAvA-v1-6.34B & 46/17/7 & 78/22/10 & 36/15/9 & 61/28/11 & 33/3/4 & 24/10/6 & 0.66 & 1104 & 10 \\ LLAxA-v1-6.34B & 47/13/10 & 31/18/18 & 33/13/14 & 66/15/19 & 26/8/6 & 25/10/6 & 0.65 & 1086 & 11 \\ LLAxA-v1-6.5Urcana-13B & 40/21/9 & 65/33/12 & 35/19/6 & 51/26/23 & 33/5/2 & 27/94 & 0.60 & 1127 & 7 \\  & **LLAVA-v1-5.13B** & **(**– 

each image, we adopt Detic Zhou et al. (2022) generate top-10 BBoxes ranked by confidence score. Then we feed GPT-4 with BBox coordinates for evaluation. **GPT-4 + caption6** (w/ or w/o criteria): For each image, we generate a detailed caption using GPT-4-Turbo. Then we feed GPT-4 with the captions for evaluation. **GPT-4 + detection + caption (w/ or w/o criteria)**: For each image, top-10 BBox coordinates and a detailed caption are sent to GPT-4 in a single prompt for evaluation. **GPT-4-Turbo and GPT-4-Turbo + criteria (ours)**: Identical setting as in Section 6.1. Prompts for GPT-4 evaluation are in Appendix F.4.

Footnote 6: The prompt for caption generation with GPT-4-Turbo is in Appendix F.3.

ResultsTable 6 summarizes the results. Persample criteria bring more than 5% overall agreement increase on GPT-4+_det_ and GPT-4-Turbo evaluation, demonstrating the effectiveness of our proposed method. Evaluation using GPT-4 with compound information (_det + cap_) outperforms using either of them, suggesting that these two methods introduce distinct information to GPT-4. We also relate our work to a recent work Fu et al. (2024), which finds that GPT-4 performs well on some MLLM benchmarks with dense captions as input. Nevertheless, GPT-4-Turbo-as-a-judge still outperforms GPT-4-as-a-judge under our setting in terms of agreement with humans, which demonstrates the necessity of adopting a vision model for evaluation in MLLM-Bench.

### Robustness Analysis

To demonstrate the robustness of our evaluation paradigm, we conduct complementary analyses from two perspectives: evaluation consistency and prompt perturbation. All experiments maintain identical settings as described in Section 5.1.

Evaluation ConsistencyWe select three models and test them three times using GPT-4-Turbo. Table 7 presents the average win rates with standard deviations across cognitive levels. The results in repeated measurements confirm high inter-evaluation consistency. This reproducibility strengthens the reliability of adopting GPT-4-Turbo as the judge in our evaluation paradigm.

Prompt PerturbationWe examine prompt sensitivity through two controlled experiments: (1) Semantic-preserving prompt restructuring via GPT-4-Turbo, and (2) Chain-of-Thought augmentation requiring post-selection reasoning. The results show that the maximum total score variation across settings is merely 2%. This demonstrates the criteria's inherent stability against prompt variations. See Appendix F.5 for detailed prompts and experiment results.

### Bias Analysis

To address potential concerns about biases in our evaluation paradigm, we conduct analyses on two critical biases: positional bias and length bias.

Positional BiasWe examine whether answer ordering affects judgments by comparing votes between original and shuffled answer pairs. Specifically, if GPT-4-Turbo has consistent votes when orders of a pair of answers are shuffled, then we

\begin{table}
\begin{tabular}{l c c c c c} \hline Methods & **Judge 1** & ** Judge 2** & ** Judge 3** & ** Judge 4** & **Aggregated** \\ \hline GPT-4 + _det_ w/o Criteria & 79.10\% & 78.20\% & 71.54\% & 72.48\% & 75.37\% \\ GPT-4 + _det_ w/ Criteria & 82.71\% & 78.79\% & 72.87\% & 80.18\% & 81.20\% \\ \hline GPT-4 + _cap_ w/ Criteria & 80.00\% & 81.72\% & 74.59\% & 82.14\% & 81.15\% \\ GPT-4 + _cup_ w/ Criteria & 78.72\% & 80.32\% & 74.58\% & 80.98\% & 80.53\% \\ \hline GPT-4 + _det+ cap_ w/o Criteria & 81.12\% & 83.42\% & 75.27\% & 84.12\% & 83.00\% \\ GPT-4 + _det+_ cap_ w/ Criteria & 80.41\% & 82.29\% & 74.86\% & 83.54\% & 83.16\% \\ \hline GPT-4-Turbo w/o Criteria & 81.62\% & 82.22\% & 75.42\% & 82.05\% & 82.80\% \\
**GPT-4-Turbo w/ Criteria (ours)** & **85.37\%** & **86.59\%** & **76.88\%** & **82.55\%** & **88.02\%** \\ \hline \end{tabular}
\end{table}
Table 6: Alignment between human evaluation and different evaluation methods. _det_: detection; _cap_: caption. The **Aggregated** column aggregates the results of 4 human evaluators.

Figure 2: Distribution of votes from GPT-4-Turbo.

 

mark the vote as unbiased (No bias). Otherwise, we mark it as positionally biased towards the first (Answer 1) or the second answer (Answer 2). As shown in Figure 2, 80.3% of judgments remain consistent regardless of answer order, demonstrating strong positional robustness.

Length BiasWe conducted an experiment to examine GPT-4-Turbo's preference for output length, as illustrated in Figure 3. The y-axis shows GPT-4-Turbo's average preference, with 0 indicating a preference for short answers and 1 for long answers. The x-axis displays the token differences between the evaluated answers. The results suggest that GPT-4-Turbo's preference for output length is negligible.

### Effects of Different MLLM Judges

While we use GPT-4-Turbo for our experiments, we aim to show that the framework is designed to be adaptable and can seamlessly incorporate other potent MLLM models. To prove this, we conduct the same setting in Section 5.1, except that we replace GPT-4-Turbo with Claude-3-opus as the evaluator. We only evaluate a subset of models due to a limited budget. As shown in Table 8, the resulting ranking has a Spearman correlation of 0.95 with GPT-4-Turbo's voting results, which demonstrates that even if we change the model evaluator, the evaluation results highly align with the original ones. This flexibility allows for the substitution of GPT-4-Turbo with other powerful models, iterating along with the rapidly evolving field of MLLMs.

## 7 Conclusion

In this paper, we propose a new paradigm for MLLM evaluation and present MLLM-Bench, a benchmark for automatically evaluating the MLLMs' ability on open-ended queries. It is derived from a comprehensive taxonomy, paving the way for a more responsible and conscientious approach to AI development. In MLLM-Bench, instead of providing the judge model with a standard answer, we equip each evaluation sample with evaluation criteria to evaluate open-ended answers reasonably. Experimental analysis on MLLM-Bench shows that evaluation using a potent MLLM using our per-sample criteria strategy aligns better with human than other baselines. We hope that MLLM-Bench can introduce more insights to MLLM evaluation and development.

\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Models** & **Perception** & **Understanding** & **Applying** & **Analyzing** & **Evaluation** & **Creation** & **Total** \\ \hline LLaVA-v1.6-34B & 0.62 (0.05) & 0.68 (0.03) & 0.59 (0.01) & 0.61 (0.00) & 0.82 (0.02) & 0.65 (0.04) & 0.65 (0.01) \\ Openflamingo & 0.12 (0.01) & 0.02 (0.01) & 0.05 (0.01) & 0.03 (0.01) & 0.03 (0.02) & 0.02 (0.01) & 0.04 (0.01) \\ Yi-VL-6B & 0.05 (0.01) & 0.06 (0.01) & 0.11 (0.02) & 0.06 (0.01) & 0.07 (0.01) & 0.08 (0.01) & 0.07 (0.00) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Consistency analysis through triplicate evaluations. Values show mean win rates with standard deviations.

Figure 3: Preference towards different output lengths of GPT-4-Turbo.

\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Models** & **Perception** & **Understanding** & **Applying** & **Analyzing** & **Evaluation** & **Creation** & **Win Rates** \\ \hline Qwen-VL-Chat & 31/21/18 & 37/24/49 & 25/20/15 & 35/25/40 & 9/9/22 & 11/11/18 & 0.35 \\ LVIS & 26/17/27 & 30/35/45 & 15/26/19 & 34/28/38 & 11/14/15 & 11/12/17 & 0.30 \\ mPLUG-Owl2 & 18/18/34 & 28/24/58 & 13/24/23 & 19/27/54 & 11/8/21 & 8/9/23 & 0.23 \\ MiniGPY-2 & 20/16/34 & 23/28/59 & 11/23/26 & 23/22/55 & 11/6/23 & 4/9/27 & 0.22 \\ SEED-LLaMA & 17/12/41 & 10/21/79 & 13/17/30 & 11/20/69 & 7/4/29 & 1/7/32 & 0.14 \\ InstructBLIP & 12/18/40 & 13/14/83 & 10/11/39 & 11/22/67 & 6/7/27 & 2/7/31 & 0.13 \\ Fuyu-8B & 9/16/45 & 4/20/86 & 5/11/44 & 6/14/80 & 2/1/37 & 0/1/39 & 0.06 \\ kosmos2 & 5/19/46 & 3/11/96 & 2/13/45 & 2/23/75 & 2/1/37 & 0/5/35 & 0.03 \\ BLIP2 & 4/11/55 & 2/11/97 & 4/9/47 & 1/11/88 & 1/2/37 & 0/1/39 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Number of wins/ties/losses for each model on each level, adopting **LLaVA-v1.5-13B** as the anchor and **Claude-3-Dpus** as the judge. Models are sorted by overall win rates in descending order.

 



## Acknowledgements

This work was supported by the Shenzhen Science and Technology Program (JCYJ20220818103001002), Shenzhen Doctoral Startup Funding (RCBS20221008093330065), Tianyuan Fund for Mathematics of National Natural Science Foundation of China (NSFC) (12326608), Shenzhen Key Laboratory of Cross-Modal Cognitive Computing (grant number ZDSYS20230626091302006), and Shenzhen Stability Science Program 2023.

## Limitations

While MLLM-Bench strives to assess multimodal large language models (MLLMs) comprehensively, it cannot encapsulate the full diversity of real-world multimodal interactions, acknowledging the challenge of simulating the unpredictable variety of real-life tasks.

Potential SubjectivityThe design of human-annotated per-sample criteria, which seeks to mirror human user experience, may introduce subjectivity, potentially affecting the consistency and generalizability of results. However, the primary objective of introducing per-sample criteria is to align model performance with human needs, as the benchmark is designed to evaluate the real-world capabilities of the models. While we acknowledge the existence of subjectivity in per-sample criteira, we posit it as a "positive" subjectivity, as it brings the model's performance closer to human expectations and practical needs.

ReproducibilityTo promote reproducibility, we have provided all the necessary code to replicate the results presented in this paper, along with the evaluation prompts detailed in Appendix E. Reproducibility is a cornerstone of this research, underscoring its importance and our commitment to transparency and scientific rigor.

Potential Deficiency of GPT-4-TurboWhile GPT-4-Turbo is a potential evaluator, its assessment outcomes may not always align perfectly with factual accuracy or human standards. There are situations where GPT-4-Turbo itself cannot handle the task. If GPT-4-Turbo itself cannot solve the problem, it cannot be a qualified judge to conduct the evaluation. Table 12 shows examples of GPT-4-Turbo's incorrect evaluation without our per-sample criteria.

Potential BiasesThe mention of length and position biases points to intrinsic limitations in GPT-4-Turbo's processing, as discussed in Section 6.4. These biases can affect the model's performance on the benchmark, potentially skewing results based on the length of input or the position of relevant information. This suggests a need for criteria that account for these biases, ensuring that the evaluation reflects the model's ability to understand and generate content impartially, regardless of these factors. As mentioned in Section 5.1, shuffling the order of model-generated responses could migrate the position bias, as done by Wang et al. (2023b; Chen et al., 2023b).

Extensibility to Larger Scale Benchmark DatasetOne might be concerned that the current dataset size is not large enough and the dataset's category distribution is unbalanced. In this paper, the selection of 420 samples was a deliberate methodological choice, aimed at demonstrating the efficacy of our proposed evaluation paradigm (i.e. per-sample criteria for evaluation) for multimodal large language models (MLLMs) in open-ended tasks. Moreover, our approach is scalable. One can scale up our benchmark through few-shot prompting, where new images are collected, and GPT-4-Turbo is used to generate instructions and corresponding per-sample criteria based on the samples and criteria in our benchmark. While there is a trade-off between scalability and the precision of per-sample criteria, one effective solution is to manually verify the generated criteria. This process strikes a balance between reducing time and human resource costs while maintaining a precise and comprehensive evaluation. Also, we are committed to continuously updating and improving our benchmark to ensure a more balanced and comprehensive system in future versions.

Extensibility to More Recent MLLMsWithin the scope of our continuous endeavors, we remain dedicated to the inclusion of the most recent models into our evaluative framework. We have established an online leaderboard, which openly encourages submissions from the global community. This platform is designed to facilitate the perpetual evaluation and juxtaposition of novel models against pre-established benchmarks, thereby promoting an atmosphere of relentless innovation and enhancement. Notably, to prevent evaluation leakage and deter potential benchmark manipulation, the per-sample criteria employed by this online

leaderboard are confidential and accessible exclusively to individual submitters.

Societal ImpactBy promoting automated evaluation methods, there is a risk that human judgment may be undervalued or overlooked. While our benchmark aims to align closely with human evaluations, it is crucial to maintain a balance and ensure that human oversight remains integral to the evaluation process.

## Ethical Statements

There are no ethics-related issues in this paper. The data and other related resources in this work are open-source and commonly used by many existing studies.

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* Wang et al. (2023a) Junke Wang, Lingchen Meng, Zejia Weng, Bo He, Zuxuan Wu, and Yu-Gang Jiang. 2023a. To see is to believe: Prompting gpt-4v for better visual instruction tuning.
* Wang et al. (2023b) Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. 2023b. Large language models are not fair evaluators.
* Wu and Aji (2023) Minghao Wu and Albam Fikri Aji. 2023. Style over substance: Evaluation biases for large language models.

 

Peng Xu, Wenqi Shao, Kaipeng Zhang, Peng Gao, Shuo Liu, Meng Lei, Fanqing Meng, Siyuan Huang, Yu Qiao, and Ping Luo. 2023. Lvlm-eub: A comprehensive evaluation benchmark for large vision-language models. _arXiv preprint arXiv:2306.09265_.
* Ye et al. (2023) Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Anwen Hu, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou. 2023. mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration.
* Young et al. (2024) Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al. 2024. Yi: Open foundation models by 01. ai. _arXiv preprint arXiv:2403.04652_.
* Yu et al. (2023) Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. 2023. Mm-vet: Evaluating large multimodal models for integrated capabilities. _arXiv preprint arXiv:2308.02490_.
* Zhang et al. (2023) Xinlu Zhang, Yujie Lu, Weizhi Wang, An Yan, Jun Yan, Lianke Qin, Heng Wang, Xifeng Yan, William Yang Wang, and Linda Ruth Petzold. 2023. Gpt-4v (sion) as a generalist evaluator for vision-language tasks. _arXiv preprint arXiv:2311.01361_.
* Zheng et al. (2023) Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llvm-as-a-jadge with mt-bench and chatbot arena.
* Zhou et al. (2022) Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. 2022. Detecting twenty-thousand classes using image-level supervision.
* Zhu et al. (2023) Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023. Minigpt-4: Enhancing vision-language understanding with advanced large language models. _arXiv preprint arXiv:2304.10592_.

 

Detailed taxonomy

The detailed taxonomy is shown in Table 9. The table provides a comprehensive overview of 42 capabilities distributed across six cognitive levels within the MLLM-Bench framework. These capabilities are designed to benchmark the performance of multimodal language and vision models (MLLMs) across a range of tasks that mimic human cognitive abilities. Here's a summary of the capabilities by level:

* This level focuses on basic recognition tasks such as identifying objects, symbols, actions, and landmarks, as well as recognizing text in various languages and formats.
* At this level, capabilities extend to more complex comprehension tasks like scene and attribute recognition, understanding emotions and facial expressions, recognizing hidden objects, and grasping multimodal commonsense, including jokes and memes, across different languages and cultures.
* This involves applying knowledge to practical tasks, including localizing objects, counting, understanding spatial relationships, interpreting medical images, and generating image captions and dense captions that describe images in detail.
* Here, the focus shifts to analytical tasks such as understanding natural relations, analyzing charts, comparing attributes, finding differences, reasoning about events and social relationships, deducing identity and function, reasoning about physical properties, and predicting actions and trends.
* This level assesses the model's judgment capabilities, including evaluating image quality, assessing damage, detecting fake images, and identifying ethical issues.
* The highest level of cognitive capability involves creative tasks, such as using vision to aid in coding and telling stories visually.

Each level builds on the previous ones, progressing from basic perception to complex and creative problem-solving, reflecting an ascending order of cognitive complexity and capability required by MLLMs.

## Appendix B More Details on Data Collection and Annotation

We mainly introduce the division of labor for these six volunteers as well as the pipeline for data collection and annotation.

### Task Distribution for Volunteers

To ensure consistency and quality across our dataset, we have engaged six volunteers, each of whom is tasked with collecting and annotating data within one or two specific levels. The distribution of responsibilities is as follows:

* Perception Level: One volunteer is responsible for all 70 instances.
* Understanding Level: Two volunteers share this category, with each annotating 55 instances.
* Applying Level: A single volunteer manages all 60 instances.
* Analyzing Level: This is shared between two volunteers, with each handling 60 instances.
* Evaluation and Creation Levels: A single volunteer is responsible for the combined total of 60 instances across these two categories to balance the workload.

This division ensures that the workload is approximately equal for each volunteer. The entire annotation cycle was completed over a span of 21 days.

### Guideline

The guideline for data collectors in Table 10 outlines essential considerations for data annotation to ensure the dataset's quality and relevance. Firstly, data currency is emphasized by prioritizing the most recent images, ideally within three months, sourced from social networks or daily life captures to mitigate the risk of data leakage. License-friendliness is crucial, advocating for the use of publicly licensed data with clear sharing agreements to respect intellectual property rights. Image clarity is necessary, with a recommendation for a mix of high and lower resolution images (with a 

minimum short-edge resolution of 512) to accurately represent real-world conditions. Impartiality is maintained by avoiding content related to sensitive topics, ensuring the dataset's neutrality and broad applicability. Instruction-image cohesion is vital, requiring precise and contextually reflective instructions to facilitate clear understanding and effective model testing. Lastly, diverse response formats are encouraged to enrich the dataset, promoting varied feedback beyond simple binary choices to better simulate real-world interactions and enhance analytical capabilities.

## Appendix C Data Statistics

MLLM-Bench is characterized by a rich diversity and complexity of instructions, each tailored to probe a specific capability of multimodal large language models. These instructions challenge models to generate responses that are both comprehensive and descriptive, engaging with the multifaceted nature of real-world scenarios and information. To illustrate the breadth of our instruction set, we present a word cloud visualization that encapsulates the frequency of terms within our instructions, as shown in Figure 4. We list one example per category in Table 4.

\begin{table}
\begin{tabular}{l l} \hline \hline Capability Level & Capability \\ \hline  & General Object Recognition \\  & OCR \\  & Multilingual Text Recognition \\  & Action Recognition \\  & Symbol Recognition \\  & Food Recognition \\  & Landmark Recognition \\ \hline  & Scene Understanding \\  & Attribute Recognition \\  & Image Topic Understanding \\  & Hidden Objects Recognition \\  & Facial Expression Recognition \\  & Emotion Understanding \\  & Multimodal Commonsense Understanding \\  & Joke and Meme Understanding \\  & Multilingual Multicultural Understanding \\  & Document Understanding \\  & Table Understanding \\ \hline  & Object Localization \\  & Object Counting \\  & Spatial Relationship Understanding \\  & Medical Image Understanding \\  & Image Captioning \\  & Dense Captioning \\ \hline  & Natural Relation Understanding \\  & Chart Understanding \\  & Attribute Comparison \\  & Difference Finding \\  & Event Cause Reasoning \\  & Social Relation Reasoning \\  & Identity Reasoning \\  & Function Reasoning \\  & Physical Property Reasoning \\  & Visual Math Reasoning \\  & Action Prediction \\  & Trend Prediction \\ \hline  & Image Quality Evaluation \\  & Damage Evaluation \\  & Fake Image Detection \\  & Ethical Problem Detection \\ \hline  & Coding Capability with Vision \\  & Visual Storytelling \\ \end{tabular}
\end{table}
Table 9: Overview of 42 capabilities on 6 cognitive levels in MLLM-Bench.

 



## Appendix D Details of Benchmarked Models

Table 11 presents a comparison of various models, highlighting their characteristics such as their open-source availability and architectural components, including visual adapters and base large language models (LLMs). Models vary in size from 1.7B to 14B parameters, with some details remaining confidential, indicated by "/". Not all models are open-sourced, as exemplified by GPT-4-Turbo. Architectural details vary, with some models utilizing visual adapters like CLIP-ViT-L, ViT+Q-Former, and others, while the base LLMs mentioned include Vicuna-13B, LLaMA2-7B, and more. The table aims for clarity by abbreviating model names and provides a snapshot of the diverse approaches in integrating visual processing with language models, demonstrating a range of strategies for enhancing model capabilities.

\begin{table}
\begin{tabular}{p{85.4pt} p{390.6pt}} \hline \hline
**Characteristics** & \multicolumn{1}{c}{**Description**} \\ \hline Data Currency & To mitigate the risk of data leakage, it is crucial to utilize the most current images available. Annotators should **prioritize images sourced from social networks like Twitter or directly captured in daily life**, ideally within three months prior to our data collection phase. This approach helps prevent the collected data from being previously used in the training of evaluated models. Maintaining and updating the dataset in real time is recommended to address potential data leakage concerns effectively. \\ \hline License-friendliness & When selecting data sources, prioritize those that are **publicly licensed and offer favorable sharing agreements**. Social networks like Twitter or content personally photographed by annotators are preferred due to their clear copyright status. This ensures that our data collection respects intellectual property rights and adheres to legal requirements. \\ \hline Image Clarity & The collected images must be of sufficient quality to be identifiable by humans. However, incorporating a selection of lower-resolution images **(the short-edge resolution is at least 512)** is also advised to accurately represent the diversity of data quality encountered in real-world scenarios. This approach ensures that our dataset reflects practical conditions and challenges. \\ \hline Impartiality & To maintain the dataset’s neutrality, **avoid including content related to sensitive topics** such as geopolitical issues. This commitment to impartiality ensures that our dataset can be used widely without bias, supporting a broad range of applications and studies. \\ \hline Instruction-Image Cohesion & To ensure images and instructions are related, the instructions should not only be precise, but also be tailored to reflect the context depicted in the image as well as to test the specific model capability. For this to be effective, the language used in the instructions should be precise and unambiguous, facilitating a clear understanding of the image content and task requirements. \\ \hline Diverse Responses Formats & Encouraging a variety of response formats enriches the dataset and better simulates real-world interactions. Instead of limiting responses to simple binary choices, data collectors are encouraged to seek out descriptive narratives and diverse forms of feedback. This strategy distinguishes our dataset from existing benchmarks and enhances its applicability to complex analytical tasks. \\ \hline \hline  & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table}
Table 10: The guideline for data collectors that states key considerations for data annotation.





## Appendix E Details of GPT-4-Turbo Evaluation

### Potential Deficiency of GPT-4-Turbo

While GPT-4-Turbo is a potential evaluator, its assessment outcomes may not always align perfectly with factual accuracy or human standards. There are situations where GPT-4-Turbo itself cannot handle the task. If GPT-4-Turbo itself cannot solve the problem, it cannot be a qualified judge to conduct the evaluation. Table 12 shows examples of GPT-4-Turbo's incorrect evaluation without our per-sample criteria.

### Prompt for Pairwise Voting Using GPT-4V with Per-Sample Criteria

Our evaluations are conducted following a _pairwise voting_ protocol. A stronger model is expected to have a larger number of wins in pairwise voting. The prompt of conducting voting by GPT-4-Turbo is shown in Figure 6.

### Anchors in Pairwise Evaluation

To facilitate a fair and consistent comparison across multiple models, we employ an anchor-based evaluation strategy. For each protocol, we use answers from the moderately powerful model _LLaVA-v1.5-Vicuna-13B_ as benchmarks.

## Appendix F Additional Results for Validation Experiments

### Correlation with Human Alignment Model Selection

Our test set for the experiment is constructed by extracting two data points from each of the 42 capabilities identified in our benchmark. We select three models representing different levels of capabilities--top, middle, and bottom--as determined by GPT-4-Turbo's direct voting outcomes. These models are **Qwen-VL-Chat**, **InstructBLIP-Vicuna 13B**, and **BLIP2-Flan-T5-XL**. **LLaVA-v1.5-13B** serves as an anchor. The evaluation set thus comprised 252 items in total. All volunteers are required to make judgments on all 252 data samples.

Interface for Human EvaluationThe user interface of human evaluation is shown in Figure 5.

Detailed ResultsTable 15 presents the pairwise agreement among four human evaluators, offering insights into inter-evaluator alignment levels regarding a set of evaluations. The inter-evaluator agreement is calculated by the matching percentage of two evaluators' voting results. The agreement ranges from 0.80 to 0.88, indicating a high degree of consensus among the evaluators. Specifically, the correlation values suggest that while there is a strong overall alignment in their assessments, each evaluator also brings a unique perspective to the evaluation process. Evaluator 1 and Evaluator 2 exhibit the highest correlation (0.88), suggesting their evaluations are most closely aligned. In contrast, the lowest correlation is observed between Evaluator 1 and Evaluator 3 (0.69), indicating a lesser, yet still significant, level of agreement. These findings underscore the evaluators' ability to consistently recognize and rank the evaluated items according to similar criteria, while also maintaining individual discretion in their judgments. This balance between consensus and individuality is crucial for ensuring both the reliability and the richness of the evaluation process, highlighting the evaluators' competence in providing nuanced assessments.

### Bounding Box Generation

We employ Detic7[Zhou et al., 2022] to generate bounding box coordinates and their labels. For each image, we only keep the top-10 objects according

Figure 4: Distribution of questions in MLLM-Bench. We present the relative distribution of these recurring Instructions and their subsequent distributions.





## Appendix A

\begin{table}
\begin{tabular}{l c r r} \hline \hline
**Models** & \multicolumn{2}{c}{**MLLM Architecture**} & \multicolumn{1}{r}{**GitHub Stars**} & \multicolumn{1}{r}{**Huggingface Download**} \\ \cline{3-4}  & _Closed-Source_ & & \\ \hline GPT-4: Turbo (OpenAI, 2023) & / & & \\ Claude-3 (Anthropic, 2024) & / & & \\ Gemini-Pro (Anil et al., 2023) & / & & \\ \hline LLVA-v1.5-13B (Liu et al., 2023b) & Pretrained Vision Encoder + Projector + LLM & 15.4K & 333.7K \\ LVIS-Instruct4v-LLAVA-7B (Wang et al., 2023a) & Pretrained Vision Encoder + Projector + LLM & 122 & 5 \\ MiniGP-F2 (Chen et al., 2023a) & Pretrained Vision Encoder + Projector + LLM & 24.7K & / \\ LLVA-v1.5-7B (Liu et al., 2023b) & Pretrained Vision Encoder + Projector + LLM & 15.4K & 703K \\ LLVA-v1.6-Vucana-7B (Liu et al., 2024b) & Pretrained Vision Encoder + Projector + LLM & 15.4K & 1.2M \\ LLVA-v1.6-Vucana-13B (Liu et al., 2024b) & Pretrained Vision Encoder + Projector + LLM & 15.4K & 100.1K \\ LLVA-v1.6-Sq. (Lin et al., 2024b) & Pretrained Vision Encoder + Projector + LLM & 15.4K & 592.8K \\ YYL-GLB (Young et al., 2024) & Pretrained Vision Encoder + Projector + LLM & 7K & 17.2K \\ ALLVA-3B-Longer (Chen et al., 2024b) & Pretrained Vision Encoder + Projector + LLM & 134 & 93 \\ kosmos2 Peng et al., 2023) & Pretrained Vision Encoder + Grounded LLM & 18.1K & 29.2K \\ LWJM Liu et al., 2024a) & Pretrained Vision Encoder + Projector + Long-Context LIM & 6.6K & / \\ BLIP2-Plan-TST-XL (Li et al., 2023c) & Query tokens + LM & 8.5K & 35.4K \\ Owen-VL-Chat Dai et al., 2023a) & Query tokens + LMM & 3.4K & 289.9K \\ InstruckBL-Pivcam-13BQ (Liu et al., 2023) & Query tokens + LMM & 8.5K & 5.4K \\ mPLUG-Owl2 (Ye et al., 2023) & Query tokens + LMM with Modality-Adaptive Module & 1.9K & 9.7K \\ Chetor Li et al., 2023b) & Query tokens + VPG-C + LMM & 308 & / \\ Fury-8Bashri et al., 2023) & Linear Vision Encoder + LMM & 7 & 17.8K \\ SEED-LLAMA (Ge et al., 2023) & VO-based Vision Encoder + LMM & 445 & / \\ OpenFlanningo (Awadalla et al., 2023) & Perceiver Resamplet + LMM with Gated Cross-Attention Layers & 3.4K & / \\ \hline \hline \end{tabular}
\end{table}
Table 11: Model architecture and popularity. ”/” means the model either uses a private download link or their download counts on HuggingFace are not shown.

Figure 5: User interface of human evaluation.





**Question:** Determine the percentage increase in "Total Income' from the quarter 30 Sep 2023 to the quarter 30 June 2023.

STARTARTH Hospital & TRAMA CARE SERVICES LIMITED

Regt. Office: J.A.16 April Tower at A Santa District Center North-Holland, 101205 India

Congworth Offices: HD-GT, Sector: I. Ontario Radio West Current Data Bank Agency UF 013100 Indian

Subtitle: www.yathathasorg/bla_com

Renei: copyrightheadings@ucs.edu; cnn: 08510100409IC174706

STATEMENT OF UNADUETO CONSOLIDATED TRANACIES RESULTS FOR THE QUAREK/HALT TARE ENDER BOTIS SEPTERS, 2023

On a Million Street Street and Trip

\begin{tabular}{|l|c|c|c|c|c|c|} \hline  & & \multicolumn{3}{c|}{CONSIGDIATED} \\ \cline{3-10}  & & Quarter & Ended & & \multicolumn{2}{c|}{HalT Year Ended} & \multicolumn{2}{c|}{**Year Ended**} \\ \cline{3-10}  & & 30 Jan 2023 & 30 Jan 2023 & 30 Jan 2023 & 30 Jan 2023 & 30 Jan 2023 & 30 Jan 2023 \\ \hline I & Brequard Don operations & 1712.04 & 1,537.07 & 1,728.9 & 3,257.6 & 2,369.6 & 5,202.0 \\ \hline H & Other factor & 38.21 & 1,537.7 & 7.45 & 3,57.7 & 1,35.2 & 2,20.0 \\ \hline H & Total income (I=II) & 1,791.00 & 1,693.08 & 1,286.34 & 3,303.64 & 2,692.86 & **5,201.00** \\ \hline H & Requisac

to confidence scores. For each object, we store the coordinates of top-left and bottom-right corner, which will later be sent to text-only GPT-4.

### Prompts for Caption Generation

The prompt for generating image captions is shown in Figure 7.

### Prompts for GPT-4 Evaluation

We show the evaluation prompts for _GPT-4 + caption_ and _GPT-4 + detection_ in Figure 8 and Figure 9, respectively.

### Prompt perturbation

We supplement two settings variating the prompts to investigate the impact of the formulation of prompts on our evaluation results. In the first setting, we aim to assess how variations in prompt content could affect GPT-4-Turbo evaluation, we reformed the prompts (change wordings and prompt structure, while the content remains semantically the same) by 'gpt-4-0125-preview'. The rewritten prompt is shown in Figure 10. In the second setting, we incorporate an extra requirement in the prompt, specifically instructing the judge to conduct a Chain of Thought (CoT) after making the selection. The reformed prompt is shown in Figure 11. Results for different settings are summarized in Table 13.

### Comparison of Alignment between Human and Different Methods

Table 14 shows the comparison of alignment between different evaluation methods and human evaluation. The number of evaluations for each settings are listed below the ratio.

## Appendix G Correlation with Other Benchmarks

We discuss the correlation between MME-Perception, MME-Cognition and MM-Vet and MLLM-Bench. Specifically, we compute the pairwise Spearman correlation of rankings. Table 16 shows that MLLM-Bench has the highest correlation with MM-Vet, which is a comprehensive benchmark testing 6 core abilities of MLLMs with short-answers. On the other hand, the Perception and Cognition splits of MME evaluate MLLMs at a fundamental and an advanced level, respectively. Therefore, the latter split has a higher correlation with MLLM-Bench than the former does. Thus, we highlight that our benchmark is proposed to complement with previous works, testing MLLMs at multiple levels with open-ended questions.

 

* **Group for generating image captions using GPT-4-Turbo.**

Figure 8: Prompts for _GPT-4 + Caption_ Evaluation

\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Settings** & **Perception** & **Understanding** & **Applying** & **Analyzing** & **Evaluation** & **Creation** & **Total** \\ \hline Default & 0.49 & 0.35 & 0.43 & 0.35 & 0.38 & 0.23 & 0.37 \\ Setting1: Prompt Reforming & 0.50 & 0.34 & 0.32 & 0.36 & 0.30 & 0.28 & 0.36 \\ Setting2: Answer-then-CoT & 0.49 & 0.36 & 0.42 & 0.33 & 0.33 & 0.35 & 0.38 \\ \hline \end{tabular}
\end{table}
Table 13: Prompt robustness analysis for Qwen-VL-Chat using LLaVA-v1.5-13B as anchor. All values represent win rates.

Figure 7: Prompts for generating image captions using GPT-4-Turbo.

 

* **Prompts for _GPT-4 + Detection Evaluation_

**Prompt:**

## You are an excellent evaluator.

### Your assignment involves providing evaluations for given responses.

Each question will consist of a *list of objects* about an image, a *question* and *two corresponding answers*. Your task is to discern which response is superior based on the **quality of the answer** and its alignment w.r.t the objects.

### Each object in the object list will contains three keys, "bbox", "conf", "label"

bbox" is a list of four numbers, which are the coordinates of the bounding box of the object in the image, the order is [x1, y1, x2, y2], where (x1, y1) is the top left corner of the bounding box, (x2, y2) is the bottom right corner of the bounding box.

### "conf" is a number, which is the confidence of the object detection model.

### "label" is a string, which is the label of the object.

### There are only two situations where you may choose 'unable to decide':

### Situation one: The question type is 'close-ended' and both answers are correct or wrong.

### Situation two: Both answers contain considerable factual errors or ethical issues.

### Otherwise, you should always choose a better answer by responding 'Answer1' or 'Answer2'.

\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Evaluator 1**} & \multicolumn{1}{c}{**Evaluator 2**} & \multicolumn{1}{c}{**Evaluator 3**} & \multicolumn{1}{c}{**Evaluator 4**} & \multicolumn{1}{c}{**Aggregated**} \\  & (Order 1) & (Order 1) & (Order 2) & (Order 2) & \\ \hline GPT-4 + detection w/o Criteria & 79.1\% & 78.2\% & 71.54\% & 72.48\% & 75.37\% \\ GPT-4 + detection w/o Criteria & (106/134) & (104/133) & (93/130) & (79/109) & (101/134) \\ GPT-4 + detection w/ Criteria & 82.71\% & 78.79\% & 72.87\% & 80.18\% & 81.2\% \\  & (110/133) & (104/132) & (94/129) & (89/111) & (108/133) \\ \hline GPT-4 + caption w/o Criteria & 80.0\% & 81.72\% & **74.59\%** & **82.14\%** & **81.15\%** \\  & (152/100) & (152/186) & (153/181) & (138/168) & (153/191) \\ GPT-4 + caption w/ Criteria & **78.72\%** & **80.32\%** & **74.58\%** & **80.98\%** & **80.53\%** \\  & (148/188) & (151/188) & (132/177) & (132/163) & (153/190) \\ \hline GPT-4 + detection + caption w/o Criteria & 81.12\% & **83.42\%** & **75.27\%** & **84.12\%** & **83.0\%** \\  & (159/196) & (161/193) & (140/186) & (143/170) & (166/200) \\ GPT-4 + detection + caption w/ Criteria & 80.41\% & **82.29\%** & **74.86\%** & **83.54\%** & **83.16\%** \\  & (156/194) & (158/192) & (137/183) & (137/164) & (163/196) \\ \hline GPT-4-Turbo w/o Criteria & 81.62\% & **82.22\%** & **75.42\%** & **82.05\%** & **82.8\%** \\  & (151/185) & (148/180) & (135/179) & (128/156) & (154/186) \\ GPT-4-Turbo w/ Criteria & **78.61\%** & **80.30\%** & **73.00\%** & **77.84\%** & **88.02\%** \\  & (158/201) & (159/198) & (146/200) & (144/185) & (147/167) \\ \hline \end{tabular}
\end{table}
Table 14: Comparison of alignment between different evaluation methods and human evaluation, the aggregated column aggregates the results of 4 human evaluators (includes the number of valid evaluations in each setting). The agreement is calculated through a matching percentage.

Figure 9: Prompts for _GPT-4 + Detection_ Evaluation



* **Prompt:**
###You possess exceptional assessment skills.
***Your task is to conduct evaluations on provided responses.
***Each evaluation will include a visual, a query, a category of query, and two possible responses. Your role is to determine which response is of higher quality and how well it aligns with the visual.
## You are permitted to select 'unable to decide' only under two specific situations:
****Situation one: The query falls under the 'close-ended' category and both responses are either correct or incorrect.
****Situation two: Both responses exhibit significant factual inaccuracies or ethical concerns.
****In all other situations, you are obligated to identify a more suitable response by indicating 'Answer1' or 'Answer2'.
****Your decision should be only stated as 'Answer1', 'Answer2', 'unable to decide: situation one', or 'unable to decide: situation two' in your final response.

```
\(<\cdots\)Question{question} \(>\)...... \(\sim\)QuestionType{question_type} \(<\cdots\)Answerlanswer1{answer2} \(<\cdots\)Answer2{answer2} \(<\cdots\)##### Please refer to the given criteria when you making the judgment Criteria: {criteria}
```

Figure 10: Rewritten prompt for pairwise voting using GPT-4-Turbo.

 



\begin{table}
\begin{tabular}{l c c c c} \hline  & MME\({}^{P}\) & MME\({}^{C}\) & MM-Vet & **MLLM-Bench** \\ \hline MME\({}^{P}\) & 1.00 & 0.14 & -0.40 & 0.57 \\ MME\({}^{C}\) & 0.14 & 1.00 & 0.80 & 0.81 \\ MM-Vet & -0.40 & 0.80 & 1.00 & 0.89 \\
**MLLM-Bench** & 0.57 & 0.81 & 0.89 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 16: Pairwise Spearman correlation between different benchmarks: MME\({}^{P}\): MME-Perception; MME\({}^{C}\): MME-Cognition; MM-Vet; **MLLM-Bench**.

\begin{table}
\begin{tabular}{c c c c c} \hline  & **Evaluator 1** & **Evaluator 2** & **Evaluator 3** & **Evaluator 4** \\ \hline Evaluator 1 & - & 0.88 & 0.80 & 0.82 \\  & - & (198/225) & (177/220) & (160/190) \\ Evaluator 2 & 0.88 & - & 0.85 & 0.83 \\  & (198/225) & - & (181/212) & (161/194) \\ Evaluator 3 & **0.80** & **0.85** & - & **0.81** \\  & (177/220) & (181/212) & - & (153/180) \\ Evaluator 4 & **0.82** & 0.83 & 0.81 & - \\  & (160/196) & (161/194) & (153/189) & - \\ \hline \end{tabular}
\end{table}
Table 15: Pairwise Agreement among Human Evaluators.