MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering

## Abstract

Abstract Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de facto gold proxy to evaluate AI models in the domain of text-centric scene understanding. Nonetheless, most existing TEC-VQA benchmarks have focused on high-resource languages like English and Chinese. Despite pioneering works to expand multilingual QA pairs in non-text-centric VQA datasets through translation engines, the translation-based protocol encounters a substantial “visual-textual misalignment” problem when applied to TEC-VQA. Specifically, it prioritizes the text in question-answer pairs while disregarding the visual text present in images. Moreover, it fails to address complexities related to nuanced meaning, contextual distortion, language bias, and question-type diversity. In this work, we tackle multilingual TEC-VQA by introducing MTVQA, the first benchmark featuring high-quality human expert annotations across 9 diverse languages, consisting of 6,778 question-answer pairs across 2,116 images.
Further, by comprehensively evaluating numerous state-of-the-art Multimodal Large Language Models (MLLMs), including GPT-4o, GPT-4V, Claude3, and Gemini, on the MTVQA dataset, it is evident that there is still a large room for performance improvement, underscoring the value of MTVQA. Additionally, we supply multilingual training data within the MTVQA dataset, demonstrating that straightforward fine-tuning with this data can substantially enhance multilingual TEC-VQA performance. We aspire that MTVQA will offer the research community fresh insights and stimulate further exploration in multilingual visual text comprehension. The project homepage is at https://bytedance.github.io/MTVQA/ .

## 1 Introduction

In the era of burgeoning AI, especially in MLLMs [, , , , , , , , , , , , , , , , , ],
Text-Centric Visual Question Answering (TEC-VQA) [, , , , , , ] has served as a de facto gold proxy to evaluate AI models in the domain of text-centric scene understanding. Compared with general VQA [, , , , , , , , , , , , , , , ], TEC-VQA places greater emphasis on answering questions that require understanding textual information within images. It provides a streamlined avenue for individuals without specialized expertise to articulate their requirements and access applications in text-centric visual environments. However, the majority of advancements in TEC-VQA have predominantly concentrated on high-resource languages, e.g., English [, , , ], Chinese [, ], Japanese [, ] and etc., thus restricting the applicability of AI models to the global community, particularly populations speaking low-resource languages.

To tackle the problem of language diversity, several seminal studies [, , ] in the general VQA field, leverage off-the-shelf translation engines to expand existing question-answer pairs from high-resource languages to their multilingual counterparts including low-resource ones. However, when applied to TEC-VQA, this translation-based approach may fall prey to the “visual-textual misalignment” problem as only the text in question-answer pairs can be processed, while the visual text present in the images is overlooked. Although the visual text can be obtained by an OCR (Optical Character Recognition) engine and then translated into English, this indirect process could introduce issues such as nuanced meaning, contextual distortion, language bias, and question type diversity. These factors further render the transferability of the translation protocol infeasible for TEC-VQA. Taking the second case in Fig. 1 as an example, if either the visual text or the question in Russian is recognized or translated problematically, then the question would never be answered. The status quo begs for a question: “Can we address the visual-textual misalignment problem directly from the source language per se for multilingual TEC-VQA and what we stand in the MLLM era?”

Figure: Figure 1: Multilingual text-centric VQA visualization selected from four languages. From left to right: Arabic (AR), Russian (RU), Thai (TH), Vietnamese (VI). The corresponding translations in English are in brackets. More examples see Figure 7.
Refer to caption: x1.png

Figure: Figure 2: Left: overview of various categories of text-rich images. Right: image and QA pairs distribution over the 9 languages in MTVQA benchmark.
Refer to caption: x2.png

In this work, to answer the question above, we establish MTVQA, a novel and high-quality multilingual TEC-VQA benchmark, where all images are collected from real-world and meticulously annotated by human experts in nine languages: Arabic (AR), Korean (KO), Japanese (JA), Thai (TH), Vietnamese (VI), Russian (RU), French (FR), German (DE), and Italian (IT). More concretely, to ensure the visual-textual alignment at most, the annotation process follows the raise-then-correct paradigm, where a group of human annotators raises several distinct questions, ranging from simple content extraction to text-related reasoning, and subsequently provides answers. Another group then double-checks these QA pairs to ensure accuracy and consistency. Consequently, as illustrated in Fig. 2, 6,678 training images and 21,829 question-answer pairs, as well as 2,116 test images and 6,778 question-answer pairs are obtained, covering more than 20 fine-grained scenarios from both documents and natural scenes, such as menus, logos, maps, bills, PPTs, research papers, and etc. To our knowledge, MTVQA is the first TEC-VQA dataset to provide native human annotations for multilingual text-rich scenarios, especially for low-source languages.

Figure: Figure 3: Left: comparison of the overall performance of various MLLMs in the MTVQA benchmark. Right: comparison of the performance exhibited by MLLMs in the 9 languages of the MTVQA.
Refer to caption: x4.png

We further investigate recent representative MLLMs in Fig. 3, including GPT-4o, GPT-4V, Gemini, QwenVL, etc., and their performance on our proposed MTVQA. As vividly revealed in Fig. 3 (a), GPT-4o is the top performer, followed by Claude3 Opus and Gemini Ultra, with closed-source MLLMs generally leading. General MLLMs like QwenVL Max and QwenVL Plus show mid-tier performance, while text-centric MLLMs lag behind. Fig. 3(b) shows closed-source MLLMs consistently outperforming others, especially in French (FR) and German (DE), while languages like Arabic (AR) and Thai (TH) witness lower scores across all categories. The results unequivocally demonstrate that opportunities for improvement persist within existing MLLMs when applied in multilingual text-rich scenarios. In summary, the main contributions of this paper can be categorized into three points:

- •
We coin the MTVQA dataset, which to the best of our knowledge is the first multilingual TEC-VQA benchmark providing human expert annotations to solve “visual-textual misalignment” problem in multilingual text-centric scenarios.
- •
We benchmark the state-of-the-art MLLMs on our new dataset and show that there are still opportunities for improvement on even the most advanced MLLMs when it comes to multilingual text-rich scenarios.
- •
We establish a set of new multilingual TEC-VQA baselines for both closed-source and general-purpose, text-centric MLLMs.

## 2 Related Work

### 2.1 MLLMs for Text-centric VQA

Recent advancements in MLLMs [, , , , , , , , , , , , , , , , ] have revolutionized VQA tasks, as demonstrated by the remarkable zero-shot performance of these models. Notably, the high generalizability of MLLMs, when explicitly trained on visual text understanding datasets and fine-tuned with instructions, has significantly enhanced their application in text-centric VQA scenarios [, , , , ]. For example, LLaVAR [], UniDoc [], which extend LLaVA [] into the realm of document understanding, pioneering the text-centric VQA of MLLMs by training them to predict texts and coordinates from document images. Furthermore, DocPedia [] operates visual input in the frequency domain rather than in space, which enables higher input resolution without increasing the input sequence. Lately, mPLUG-DocOwl [], Qwen-VL [], and TextMonkey [] leverage publicly available document-related VQA datasets to further enhance the text-centric VQA capabilities. Despite the promising results achieved by existing MLLMs in text-centric VQA tasks, their focus on high-resource languages such as English and Chinese has posed challenges in achieving reasonable performance for low-resource languages. This is primarily due to the lack of data or benchmarks for these low-resource languages.

### 2.2 Multilingual Text-centric VQA Benchmarks

VQA has garnered significant attention in recent years, with numerous studies, datasets, and benchmarks being proposed to advance the field [, , , , , , , , , , , , , , , ]. Many datasets have been created that encompass scene text of various domains, including natural images [, ], scanned documents [, ], book and movie covers []. One notable limitation of these datasets is their predominant focus on English [, , , ] or other high-resource languages such as Chinese [, ] and Japanese [, ], which restricts the applicability of VQA systems for low-resource languages such as Thai and Vietnamese.

There are some recent efforts toward extending VQA tasks to a wider range of languages [, , , , , ] by providing a multilingual VQA datasets. For example,  Gao et al. [] created a free-form bilingual VQA dataset (FM-IQA) containing over 150,000 images and 310,000 freestyle Chinese question-answer pairs and English translations. Raj Khan et al. [] developed a large-scale multilingual and code-mixed VQA dataset (MuCo-VQA) supporting five languages. Of more relevance are the works xGQA (8 languages) [] and MaXM (7 languages) [], which apply translation-based protocols to expand VQA data beyond English. However, the translation-based multilingual VQA datasets inherently face issues, such as the “visual-textual misalignment” problem, where only the textual information in question-answer pairs is considered, while the visual text in images is overlooked. Additionally, the nuanced meaning and context are often distorted; language bias is introduced by machine translation models, and the coverage of certain question types is limited, as highlighted by []. Moreover, none of the previous multilingual datasets focus on text-centric scenarios where multilingual text frequently occurs.

Our benchmark, MTVQA, distinguishes itself by focusing on multilingual text-centric VQA scenarios using human expert annotations. It covers 9 languages, thereby facilitating the training and evaluation of multilingual models in diverse linguistic contexts. Additionally, our dataset can gauge the VQA system’s ability for not only high-resource languages but also those that are typically underrepresented in current datasets [, , , , ].

## 3 MTVQA Benchmark

The MTVQA benchmark is meticulously established to evaluate the multilingual text comprehension performance of Multimodal Large Language Models (MLLMs). MTVQA covers nine languages: Arabic (AR), Korean (KO), Japanese (JA), Thai (TH), Vietnamese (VI), Russian (RU), French (FR), German (DE), and Italian (IT). The data construction process involves a comprehensive collection of text-rich images and a two-round human expert annotation process.

Figure: Figure 4: A brief diagram of the annotation process.
Refer to caption: x5.png

### 3.1 Data Collection

The raw data collection is oriented towards gathering text-rich images from various natural and document scenarios, ensuring diversity and quality. This includes images from publicly available datasets(e.g., ICDAR MLT19 []) and those sourced from the internet, such as menus, logos, maps, bills, PowerPoint slides (PPTs), research papers, etc, as shown in Fig. 2. A total of 1,220 images from document scenarios and 876 images from natural scenarios are collected for the test set of the MTVQA benchmark. To ensure the visual-textual alignment, for text-rich images lacking text and language annotations, we subject them to a standardized data cleaning process, which includes multilingual text recognition and language classification. Afterwards, we organize all the text-rich images we have obtained into language-specific groups, preparing them for the subsequent stage of data annotation.

### 3.2 Human Expert Annotation

To obtain informative and accurate text-related QA pairs for images grouped by specific languages, a specialized group of annotators with expertise in the local regions of each language is recruited. These annotators are native speakers, guaranteeing a profound understanding and skillfulness in the linguistic subtleties and cultural contexts required for precise annotations. Given the subjective nature of understanding text within images, the annotation team is divided into two independent groups. One group is tasked with generating questions and providing answers based on the images, while the other group is responsible for evaluating and correcting the QA pairs. This division, known as the raise-then-correct paradigm, ensures a thorough and trustworthy evaluation of the text-rich image comprehension process. Moreover, the annotation results for each language underwent a 10% sampling inspection by a quality inspector to ensure adherence to standards. QA pairs that are not meeting the criteria are returned for re-annotation. Before beginning the formal annotation task, all annotators have received training and examples of annotations. The two-round annotation process is briefly illustrated in Fig. 4, further details of which are elaborated in the subsequent sections.

Figure: Figure 5: Word clouds showcasing top answers in various languages, tokenized via NLTK with removing stop words, punctuation, and digits.
Refer to caption: x6.png

First Round Questioning and Answering. In the first round, we allocate three annotators per language to generate initial QA results. The annotators are tasked with thoroughly examining the textual and visual elements within a text-rich image in our collection. They are to examine the textual and visual elements within the image to formulate five meaningful and distinct questions and provide corresponding answers. All annotators should adhere to the following criteria: (1) the first three questions should satisfy that answering these questions requires direct reading of the textual information in the image, (2) the fourth and fifth questions require reasoning about the text in the image to answer, (3) the questions and answers must be reasonably correct and consistent with the content of the image, and (4) the answer should be as concise as possible and free of nonsense (e.g., when the question is “When is the volunteer recruitment period”, the answer should be “9:00-16:00” rather than “The volunteer recruitment period is 9:00-16:00”). This requirement for brevity aims to make the evaluation process user-friendly and reliable, avoiding the influence of extraneous content on the evaluation metrics.

Second Round Evaluation and Correction. To reduce the effect of human subjective cognitive bias on our MTVQA benchmark and get high-quality question-answer pairs, we assign two annotators for each language to carry out the annotation evaluation and correction process. These annotators follow a specific evaluation and correction protocol to ensure consistency and accuracy based on the first round results: (1) Assess the relevance of the question to the text in the image. Irrelevant QA pairs are discarded; (2) Verify the correctness of the answer and make necessary modifications; (3) Check for redundancy in the answer. If the answer repeats the question’s information, the repeated content is removed for conciseness. (4) Ethical Assessment. Review the image content or the question-answer pair for any unethical content, including but not limited to politics, personal privacy issues, etc. Such content is removed to uphold ethical standards in the dataset.

Figure: Figure 6: Statistics of question and answer lengths of different languages aggregating training and test set, using GPT-4o tokenizer.
Refer to caption: x15.png

### 3.3 Data Statistics

The MTVQA benchmark consists of 8,794 images and 28,607 question-answer pairs across the nine languages, divided into a training set with 6,678 images and 21,829 question-answer pairs and a test set with 2,116 images and 6,778 question-answer pairs. Detailed data distribution is illustrated in Fig. 2. Additionally, the benchmark showcases the vocabulary richness for each language through word clouds, as depicted in Fig. 5, and the lengths of questions and answers are statistically analyzed using the GPT-4o tokenizer, as shown in Fig. 6.

## 4 Experiments

### 4.1 Baseline Models

To comprehensively assess MLLMs’ multilingual perception and comprehension capabilities, We select state-of-the-art MLLMs from three categories: open-source general MLLMs, open-source text-centric MLLMs, and closed-source MLLMs. Each category contains the following models: (1) General MLLMs: InternVL-V1.5 [], InternLM-Xcomposer2-4KHD [], Mini-Gemini-HD-34B [], Llava-Next-34B [], DeepSeek-VL [], YI-VL-34B [];
(2) Text-centric MLLMs: TextSquare [], TextMonkey [], mPLUG-DocOwl 1.5 [], MiniCPM-V 2.0 [];
(3) Closed-source MLLMs: GPT-4o [], GPT-4V [], Gemini Ultra [], QwenVL Max [], QwenVL Plus [], Claude3 Opus [], Claude3 Sonnet [], and GLM-4V [].
For the closed-source MLLMs, we use the chat version through official APIs, while for the open-source MLLMs, we utilize the instruct versions available on the HuggingFace Model Hub. The open-source MLLMs’ model size varies from 7b to 34b.

### 4.2 Implementation Details

We conduct the evaluation experiments over the baseline MLLMs with their default settings, ignoring the effect of generation configuration on the results. To make the output of MLLMs more evaluation-friendly, we design the following prompt format to limit the output length: “Answer the question using a word or phrase in the language of the question. + <Question>”, where “<Question>” represents the actual question from the MTVQA test set. This approach aims to make the answers as concise as possible. Besides, InternLM-Xcomposer2-4KHD  [] is chosen as the base model for an instruction tuning experiment on the MTVQA training set. The instruction tuning process adheres to the default training settings specified by the source, with “HD-16” and completes one epoch of training on 8 NVIDIA-A100 GPUs within 2 hours.

**Table 1: Performance of the leading closed- and open-source MLLMs on the MTVQA benchmark. The best results of each language are bolded. The best results for open-source models are underlined. “Xcomposer-SFT” denotes instruction tuning to Xcomposer2-4KHD with MTVQA’s training set.**
|  | AR | DE | FR | IT | JA | KO | RU | TH | VI | Avg. |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Closed-Source MLLMs |  |  |  |  |  |  |  |  |  |  |
| GPT-4V [] | 11.5 | 31.5 | 40.4 | 32.3 | 11.5 | 16.7 | 10.3 | 15.0 | 28.9 | 22.0 |
| GPT-4o [] | 20.2 | 34.2 | 41.2 | 32.7 | 20.0 | 33.9 | 11.5 | 22.5 | 34.2 | 27.8 |
| Gemini Ultra [] | 14.7 | 32.3 | 40.0 | 31.8 | 12.3 | 17.2 | 11.8 | 20.3 | 28.6 | 23.2 |
| QwenVL Max [] | 7.7 | 31.4 | 37.6 | 30.2 | 18.6 | 25.4 | 10.4 | 4.8 | 23.5 | 21.1 |
| QwenVL Plus [] | 4.8 | 28.8 | 33.7 | 27.1 | 12.8 | 19.9 | 9.4 | 5.6 | 18.1 | 17.8 |
| Claude3 Opus [] | 15.1 | 33.4 | 40.6 | 34.4 | 19.4 | 27.2 | 13.0 | 19.5 | 29.1 | 25.7 |
| Claude3 Sonnet [] | 10.5 | 28.9 | 35.6 | 31.8 | 13.9 | 22.2 | 11.0 | 15.2 | 20.8 | 21.1 |
| GLM-4V [] | 0.3 | 30.0 | 34.1 | 30.1 | 3.4 | 5.7 | 3.0 | 3.5 | 12.3 | 13.6 |
| Open-Source MLLMs |  |  |  |  |  |  |  |  |  |  |
| InternVL-V1.5 [] | 3.4 | 27.1 | 31.4 | 27.1 | 9.9 | 9.0 | 4.9 | 8.7 | 12.4 | 14.9 |
| Mini-Gemini-HD-34B [] | 2.2 | 25.0 | 29.2 | 25.5 | 6.1 | 8.6 | 4.1 | 4.3 | 11.8 | 13.0 |
| Llava-Next-34B [] | 3.3 | 24.0 | 28.0 | 22.3 | 3.6 | 6.1 | 2.6 | 0.4 | 9.8 | 11.1 |
| DeepSeek-VL [] | 0.6 | 14.2 | 15.3 | 15.2 | 2.9 | 3.8 | 1.6 | 0.9 | 5.2 | 6.6 |
| YI-VL-34B [] | 1.7 | 13.5 | 15.7 | 12.1 | 4.8 | 5.2 | 0.8 | 3.5 | 4.1 | 6.8 |
| MiniCPM-V 2.0 [] | 1.3 | 12.7 | 14.9 | 17.0 | 3.7 | 5.6 | 2.2 | 2.2 | 6.8 | 7.4 |
| MiniCPM-V 2.5 [] | 6.1 | 29.6 | 35.7 | 26.0 | 12.1 | 13.1 | 5.3 | 12.6 | 15.3 | 17.3 |
| TextSquare [] | 3.7 | 27.0 | 30.8 | 26.7 | 3.2 | 7.2 | 6.7 | 5.2 | 12.4 | 13.6 |
| TextMonkey [] | 2.0 | 18.1 | 19.9 | 22.1 | 4.6 | 7.2 | 3.2 | 0.9 | 11.1 | 9.9 |
| mPLUG-DocOwl 1.5 [] | 1.0 | 13.9 | 14.9 | 18.2 | 2.9 | 5.0 | 2.0 | 0.9 | 6.4 | 7.2 |
| Xcomposer2-4KHD [] | 2.0 | 20.6 | 23.2 | 21.6 | 5.6 | 7.7 | 4.1 | 6.1 | 10.1 | 11.2 |
| Xcomposer-SFT | 11.8 | 31.7 | 37.4 | 29.3 | 14.5 | 12.9 | 5.8 | 13.9 | 20.2 | 19.7 |

### 4.3 Evaluation Results

Evaluation metric. To accurately assess whether the visual text that occurs in the answer is correct, we adopt Accuracy as the metric. The Accuracy metric measures the percentage of questions for which the predicted answer matches exactly with any of the target answers for the question. The accuracy metric awards a zero score even when the prediction differs slightly from the target answer.

Zero-shot evaluation. We perform a zero-shot evaluation of various types of MLLMs on the MTVQA benchmark with a consistent prompt. The evaluation results are shown in Table 1, where GPT-4o [] achieves the highest average accuracy of 27.8$\%$ across the 9 languages. This result suggests that while MLLMs have some capability in comprehending multilingual text, the performance is still not robust, and multilingual text-centric visual question answering (VQA) tasks remain a significant challenge, even for state-of-the-art closed-source MLLMs. The evaluation also reveals that both open-source and closed-source models performed better on Indo-European languages that use the Latin alphabet, such as German (DE), French (FR), and Italian (IT). This is likely due to the more extensive training data available for English and their visual and linguistic similarities. In addition, all closed-source models, except for GLM-4V, generally outperform the open-source model across the nine languages, potentially benefiting from pre-training on diverse multilingual data. MiniCPM-V 2.5 performs best among the open-source models, even comparable to closed-source models such as QwenVL Plus. Interestingly, the text-centric MLLMs, like TextSquare [], TextMonkey [] and mPLUG-DocOwl 1.5 [], do not show a significant performance advantage over other open-source models for the languages tested, suggesting a focus on high-resource languages (mainly English and Chinese) and a lack of attention to other languages.

Instruction tuning. As shown in Table 1, the instruction tuning experiment on MTVQA benchmark brings a 8.5$\%$ improvement. Concerning specific languages, French sees the largest improvement of 14.2$\%$ in accuracy, while Russian has the smallest improvement of 1.7$\%$. The results demonstrate that MLLMs vary in their ability to understand and learn from text-centric data in different languages, leaving great potential for future research of multilingual text-centric MLLMs pre-training.

**Table 2: Few-shot performance of GPT-4V on the MTVQA benchmark. In-context examples are randomly selected from the training set of MTVQA in the respective languages. “n-shot” represents the number of the selected in-context examples.**
|  | AR | DE | FR | IT | JA | KO | RU | TH | VI | Avg. |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| zero-shot | 11.5 | 31.5 | 40.4 | 32.3 | 11.5 | 16.7 | 10.3 | 15.0 | 28.9 | 22.0 |
| two-shot | 11.6 | 35.3 | 42.2 | 33.1 | 13.2 | 17.0 | 11.4 | 19.0 | 31.0 | 23.7 |
| five-shot | 13.5 | 34.8 | 43.0 | 35.7 | 13.4 | 18.8 | 11.6 | 19.2 | 33.0 | 24.8 |
| eight-shot | 15.5 | 34.8 | 42.1 | 35.6 | 13.4 | 17.5 | 12.1 | 18.2 | 32.8 | 24.7 |

Few-shot evaluation of GPT-4V. Here, we compare the performance of GPT-4V on MTVQA under the few-shot settings. Specifically, we perform zero-shot, two-shot, five-shot, and eight-shot evaluations. We randomly select in-context examples from the train set in the respective languages and evaluate GPT-4V on the remaining instances. As shown in Tab. 2, compared to the zero-shot setting, GPT-4V’s performance has improved considerably under few-shot, highlighting its exceptional in-context learning ability in multilingual text comprehension contexts. Moreover, comparisons based on varying numbers of in-context samples reveal that augmenting the in-context samples can aid in further enhancement; however, after reaching a certain volume, the improvement becomes saturated.

## 5 Limitation

The current version of the MTVQA dataset, while dialectically diverse, exhibits limitations in its language coverage. Despite encompassing a range of languages, it falls short of inclusivity, omitting numerous lesser-spoken languages. This prompts our future continual endeavor to ensure comprehensive representation across the linguistic spectrum. Additionally, the dataset currently provides a single canonical response per question, which may not fully capture the range of answers corresponding to different expressions of the same underlying semantics. Recognizing the multifaceted nature of the inquiries, future versions will aim to include a spectrum of plausible answers to reflect the varied perspectives inherent to each question.

## 6 Conclusion

To attack the visual-textual misalignment issue in multilingual TEC-VQA, we introduce MTVQA, a new benchmark featuring high-quality human expert annotations in 9 diverse languages. We believe that MTVQA is the first benchmark to provide fully manual annotations tailored to text-centric scenarios. The results obtained from closed-source, general-purpose and text-centric MLLMs on our MTVQA dataset indicate that there is still room for improving their performance in multilingual text-centric scenarios. Although the MTVQA dataset has limitations, such as the underrepresentation of several lesser-spoken languages and single canonical answers, future updates will address these issues by expanding the multilingual scope and including a range of plausible answers. We are confident that this dataset can inspire researchers within the TEC-VQA community with new perspectives and ideas.

## Appendix A Appendix

Figure: (a) German (DE) example
Refer to caption: x24.png

**Table 3: Mean lengths of question-answer pairs in different languages of the training set and test set, using GPT-4o tokenizer.**
|  | AR | DE | FR | IT | JA | KO | RU | TH | VI |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Training Set |  |  |  |  |  |  |  |  |  |
| Question | 8.29 | 8.72 | 9.73 | 12.05 | 12.43 | 11.74 | 11.56 | 11.35 | 11.21 |
| Answer | 9.66 | 6.96 | 7.34 | 11.24 | 12.70 | 13.56 | 12.00 | 11.26 | 13.31 |
| Test Set |  |  |  |  |  |  |  |  |  |
| Question | 8.08 | 8.29 | 9.76 | 11.93 | 12.48 | 12.2 | 11.65 | 10.98 | 10.99 |
| Answer | 7.95 | 6.67 | 6.61 | 11.04 | 12.55 | 13.61 | 14.42 | 12.08 | 13.00 |

Figure: Figure 8: Word clouds showcasing top questions in various languages, tokenized via NLTK with the removal of stop words, punctuation, and digits.
Refer to caption: x29.png

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: Please address the weaknesses above.
soundness: 3
strengths: S1. Non-trivial data collection effort, which human annotators and quality control measures.

S2. Benchmarking of frontier models, both open and closed source, demonstrating gaps in current models. Extensive benchmarking in terms of number of models.

S3. Table 4 and experiments with OCR+text processing are both interesting and surprising. It also verifies that vision is critical to the benchmark, not just more simple OCR processing.

S4. Evidence that (multi-modal) few-shot prompting improves performance.
summary: The paper proposes a VQA dataset where models must contextualize multilingual text in the image to answer questions. The paper provides extensive benchmarking and ablations on frontier open, closed models.
weaknesses: W1. It is not clear what Text-Centric Visual Question Answering is in the abstract. Given that this is a fairly specific task, it might be good to clearly define it or you risk your reader getting lost.

W2. Figure 4 could use some more explanation in the caption. As the figure stands now, it is not super clear how I should parse it or what the takeaways should be.

W3. L266, what OCR tool? Please cite.

W4. The benchmark frames languages as low resource; however, most languages are fairly wide spread.

W5. Can more recent open and closed source models be added (e.g., claude 3.5, llama 3.1, etc.)

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 2
questions: The reviewer would like to know the answer to the weaknesses above.
soundness: 3
strengths: - Release of datasets that allow benchmarking as a combination of multimodal and multilingual tasks
- Evaluation experiments with many MLLMs
summary: This paper provides a Text-Centric Visual Question Answering (TEC-VQA) benchmark consisting of nine languages: Arabic (AR), Korean (KO), Japanese (JA), Thai (TH), Vietnamese (VI ), Russian (RU), French (FR), German (DE), Italian (IT) texts in images, and questions and answers about those images in a raise-then-correct paradigm, resulting in over 6000 QA pairs for over 2000 images. The results of the exact match evaluation show that the accuracy is at most 30% for closed-source MLLMs and open-source MLLMs, indicating that this benchmark is challenging.
weaknesses: - Benchmark data quality
  - For example, looking at the third example in Figure 1, the question “Where do hedgehogs live?” can be answered with a certain degree of probability even without looking at the image itself. In the second example in Figure 7 of the appendix, the number 299 is written in large Arabic numerals, so even if MLLMs don't have the ability to read Italian from the image, they could probably answer the price as 299 euros. There are many examples like this that don't seem to be appropriate as VQA or multilingual language tasks, so the overall quality of the benchmark data is a concern.
  - Also, looking at Figure 5, it appears that words indicating native languages in each country's language (e.g. Deutsch in German, 日本語 in Japanese, Français in French, etc.) account for a certain percentage. In other words, there is a concern that many of the questions of this type, which ask about languages, will be answered correctly if the text is in French, for example, if the answer is Français.
- Importance as a benchmark
  - For example, we can evaluate the performance of multilingual QA using benchmarks such as Okapi [Lai+, EMNLP 2023], and we can evaluate the performance of QA that also includes text and asks about image understanding using benchmarks such as MMMU [Yue+, CVPR 2024]. The proposed benchmark dataset aims to satisfy these properties at the same time, but it is unclear what the performance and limitations of MLLM will be when it is used in this way.
  - Table 4 reports on the combination with OCR, but it is unclear what the accuracy of OCR is in the first place. It would also be necessary to evaluate the accuracy when the true value of the text contained in the image is given, rather than OCR.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Please see weakness.
soundness: 3
strengths: 1. The human expert annotations presented in this paper is valuable.

2. This paper empirically analyze the multilingual VQA abilities of current leading MLLMs on their benchmark.
summary: This paper proposes MTVQA dataset ,which is claimed as the first Text-Centric Visual Question Answering (TEC-VQA) benchmark providing human expert annotations to solve the “visual-textual misalignment” problem in multilingual text-centric scenarios. Experimental results show that existing Multimodal Large Language Models remains a large room for improvement.
weaknesses: 1. The results presented in Appendix B.1 show that GPT-4o struggles to understand certain languages. It would be helpful to provide results for some MLLMs when given ground truth English questions, rather than questions translated by the model itself.

2. Since the SoTA performance on this benchmark is only around 30% accuracy, it is important to include human performance to demonstrate that the questions in this benchmark are not ill-defined.

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confidence: 5
contribution: 2
initial_rating: 5
presentation: 3
questions: see weaknesses.
soundness: 2
strengths: 1. All images are collected from real-world and annotated by human experts in nine languages. The benchmark consists of 6,678 training images and 21,829 question-answer pairs, as well as 2,116 test images and 6,778 question-answer pairs.
2. By evaluating numerous state-of-the-art MLLMs, including GPT-4o, GPT-4V, Claude3, and Gemini, on the MTVQA dataset, it is evident that there is still a large room for performance improvement, underscoring the value of MTVQA.
summary: The paper constructs the multilingual TEC-VQA benchmark, where all images are collected from real-world and  annotated by human experts in nine languages: Arabic (AR), Korean (KO), Japanese (JA), Thai (TH), Vietnamese (VI), Russian (RU), French (FR), German (DE), and Italian (IT). They evaluate numerous MLLMs on the MTVQA dataset. Additionally, we supply multilingual training data within the MTVQA dataset.
weaknesses: 1. The amount of data is not substantial，can it sufficiently evaluate the differences among various MLLMs across different languages and domains? There is a lack of more detailed error analysis.
2. Lacking some more insightful findings, such as the factors influencing multilingual capabilities, the difficulty and generalizability of different languages, etc.
3. The improvement of  incontext in Table 3 is not very significant, what could be the reason for the considerable differences across different languages?

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confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: See weaknesses
soundness: 2
strengths: - The dataset is reasonably large, though the number of images is still limited and there is only one answer per question. 
- The zero-shot evaluation used a large number of models. 
- The tested models perform poorly on this dataset, suggesting that the dataset could have value.
summary: This paper presents a dataset for text-based VQA, containing 8,794 images and 28,607 QA pairs. The production cost is about 90K USD. The dataset contains seven languages, including Arabic (AR), Korean (KO), Japanese (JA), Thai (TH), Vietnamese (VI), Russian (RU), French (FR), German (DE), and Italian (IT). The paper tested a number of models under zero-shot settings, GPT-4V under in-context-learning few-shot, and GPT-4 with OCR.
weaknesses: - The exact match error metric is overly restrictive, which is partially why the tested models do not achieve high scores. Even two humans would not answer the same question exactly the same. The authors should introduce a multiple-choice metric. In addition, they should establish a human baseline, where human annotators are asked to answer the same questions. This would serve two purposes: (1) it validates the provided answers and (2) it shows where the ceiling of performance is. Given the metric, it is possible that even humans cannot guess the exact answer. 

- In Line 74, the authors claim that translation does not work for TEC-VQA, since it would ignore the text in the image. However, this was never tested. The author should have a baseline where only the question is translated, and a baseline that performs OCR and translation. 

- The dataset is small relative to the development cost. In particular, the authors should explain why image acquisition cost about 30,000 dollars and what kind of manual collection was used. 

Minor comments: 

Line 79 reads: "The status quo begs for a question: 'Can we directly leverage visual text in source language per se for multilingual
TEC-VQA and what we stand in the MLLM era?'" But it's two questions. 

Can you explain the content of "regulatory requirements" (Line 263)? In particular, what is considered "politically sensitive"?