Visual Perception in Text Strings

## Abstract

Abstract Understanding visual semantics embedded in consecutive characters is a crucial capability for both large language models (LLMs) and multi-modal large language models (MLLMs). This type of artifact possesses the unique characteristic that identical information can be readily formulated in both texts and images, making them a significant proxy for analyzing modern LLMs’ and MLLMs’ capabilities in modality-agnostic vision understanding.
In this work, we select ASCII art as a representative artifact, where the lines and brightness used to depict each concept are rendered by characters, and we frame the problem as an ASCII art recognition task. We benchmark model performance on this task by constructing an evaluation dataset with an elaborate categorization tree and also collect a training set to elicit the models’ visual perception ability. Through a comprehensive analysis of dozens of models, results reveal that although humans can achieve nearly 100% accuracy, the state-of-the-art LLMs and MLLMs lag far behind.
Models are capable of recognizing concepts depicted in the ASCII arts given only text inputs indicated by over 60% accuracy for some concepts,
but most of them achieves merely around 30% accuracy when averaged across all categories.
When provided with images as inputs, GPT-4o gets 82.68%, outperforming the strongest open-source MLLM by 21.95%.
Although models favor different kinds of ASCII art depending on the modality provided, none of the MLLMs successfully benefit when both modalities are supplied simultaneously. Moreover, supervised fine-tuning helps improve models’ accuracy especially when provided with the image modality, but also highlights the need for better training techniques to enhance the information fusion among modalities. All resources are available at https://github.com/JiaQiSJTU/VisionInText .

## 1 Introduction

While conventional wisdom suggests that texts primarily function as carriers of linguistic information and images as conveyors of visual information, real-world scenarios often involve the integration of multiple information formats. For example, images may carry textual information, thus Optical Character Recognition (OCR) (Mori et al., ) has been extensively studied. It focuses on capturing and understanding linguistic information embedded in images through visual processors, which is a crucial ability required in modern models for visual reasoning tasks (Yu et al., ).

In contrast, the comprehension of visual information embedded within text strings has not received commensurate attention. One representative example that reflects visual semantics by a sequence of characters is ASCII art (Xu et al., ) as shown in Fig. 1. Visual information in these artifacts is situated in the middle of text strings and images, and can be readily expressed in both formats containing identical content. In other words, it is modality-agnostic.

Understanding how well models can capture visual semantics in text modality is significant for developing large language models (LLMs) (Dubey et al., ; Bai et al., ). Upon pre-training on a vast amount of text corpus, language models are capable of encoding visual information through escape characters, such as “\n” and “\t”, which encodes 2D structures in human writings. Additionally, such visual information is ubiquitous in a wide range of text processing tasks, such as processing tabular data (Deng et al., ) and playing board games (Topsakal & Harper, ), indicating the necessity of visual perception capability in language models. Nevertheless, using visual information reflected in characters to break through the defense line is becoming a threat to LLM safety issues (Jiang et al., ). A thorough analysis for better understanding LLMs’ visual perception ability to make proactive defense is in urgent need.

Figure: Figure 1: Examples of ASCII art. The left side contains text and image modalities of ASCII art pieces under different categories. The right side presents a multiple choice question in ASCIIEval.
Refer to caption: x1.png

With the advent of multi-modal large language models (MLLMs) (Achiam et al., ; Reid et al., ; Anthropic, ) that arm LLMs with visual processors, the aforementioned modality-agnostic characteristic also naturally leads to a new perspective of understanding MLLMs visual perception ability and the degree of modality fusion. On the one hand, we expect that MLLMs can perform robustly among different modalities, when visual information in different modalities is provided individually.
On the other hand, MLLMs are expected to take the best of both worlds when two modalities are presented simultaneously and demonstrate stronger vision understanding ability.

In this work, we define ASCII art recognition as an ideal proxy to investigate models’ visual perception ability in text strings through comprehensive evaluation and fine-tuning.
Different from previous work that has focused on box diagrams (Hayatpur et al., ; Bayani, ), rich-formatting texts (Jiang et al., ), or tone-based ASCII art (Wang et al., ) that can be easily generated by rules or converted from images, we focus on ASCII art drawn by human artists, which is notably more abstract, replete with visual information, and more popular among people. We formulate the task as a multiple-choice question-answering problem, where the answers are objective for straightforward verification, to achieve fairer comparisons. Then, we task models to recognize the concept depicted in the ASCII art.
Due to the lack of a dataset covering diverse categories thoroughly benchmarking the ability of existing models, we crawled data from online websites and cleaned manually under an elaborate categorization tree. In this way, we construct a test set dubbed ASCIIEval covering 359 concepts. To further elicit the models’ visual perception ability, a training set was collected with approximately 10k data points.

We convert each ASCII art into a text string, an image, or both modalities at the same time as inputs, evaluated dozens of existing LLMs and MLLMs, and fine-tuned representative open-source models. Our major findings are summarized as follows:

- $\circ$
Models can truly recognize visual semantics through text inputs, indicated by the over 60% accuracy of GPT-4o in certain concept categories. However, existing LLMs performs poorly on ASCIIEval, where most of them achieve merely around 30% accuracy (Sec. 5.1).
- $\circ$
There is an oversight in modality fusion that hinders MLLMs from answering questions flexibly among modality-agnostic visual signals. We observed that well-known MLLMs show a strong bias towards image modality, the expected synergistic effects do not emerge, and their training techniques fail to facilitate the backbone LLMs’ visual understanding ability (Sec. 5.2 & 5.3.1).
- $\circ$
LLMs and MLLMs show different trends in model performance when provided with different input modalities and excel at different ASCII art categories. Specifically, they perform relatively better on ASCII art containing fewer characters when given text inputs, whereas performing better on those with more characters given image inputs (Sec. 5.3.2 & 5.3.3).
- $\circ$
Better training strategies or model architectures are required for optimizing modality-agnostic visual perception in text strings. Supervised fine-tuning using task-specific training data helps MLLMs leverage representations from different modalities slightly better, but shows little improvement given only text inputs (Sec. 5.4).

## 2 Backgrounds & Related Work

### 2.1 LLM & MLLM Benchmarks

Mainstream evaluations for LLMs primarily focus on abilities in world knowledge, common sense reasoning, instruction following, long context modeling, and mathematical reasoning. Representative benchmarks include MMLU (Hendrycks et al., ), C-Eval (Huang et al., ), GSM8K (Cobbe et al., ), HumanEval (Chen et al., ), and StrategyQA (Geva et al., ). None of them consider the visual perception ability of LLMs as a distinct research problem.

Benchmarks for MLLMs focus on similar abilities when given a mix of text and images, such as MMMU (Yue et al., ), MMBench (Liu et al., ), and MME (Yin et al., ).
Most images considered in these benchmarks are photographs, paintings, or comics, rather than visual information reflected in text characters. Additionally, the information between interleaved images and texts is not guaranteed to be equivalent or complementary, whereas information in ASCII art can be semantic-equivalent among different modalities.

Current benchmarks contain some ASCII art-related tasks. For example, Gu et al. () introduces a fine-grained and diverse instruction-following evaluation dataset, in which ASCII art generation is a single case with approximately 40 samples of varied user requests.
BigBench (Ghazal et al., ) contains ASCII MNIST digit recognition, ASCII word recognition, and ASCII kanji recognition. All of these tasks challenge the LLMs in recognizing different characters within the ASCII art. Test cases can be easily collected by using automatic conversion tools like Figlet (^1^11[https://en.wikipedia.org/wiki/FIGlet](https://en.wikipedia.org/wiki/FIGlet)), where models may learn conversion rules instead of truly understanding visual semantics.

In contrast, our work focuses on ASCII art depicting real-world profiles, containing more abstract visual features. We consider ASCII art recognition to be a preliminary ability for ASCII art generation, and propose ASCIIEval based on this task, which can serve as both an LLM benchmark and an MLLM benchmark, bringing unique characteristics compared to existing benchmarks.

### 2.2 Research on ASCII Arts

The history of ASCII art can be traced back to the 1860s. Due to the limitations of early computers, text characters were widely used to simulate graphs, gradually becaming an important graphic design technique. ASCII art broadly includes diverse types and styles (Carlsson & Miller, ; Carlsson, ), such as line art, emoticons, colored ASCII art, and animated ASCII art. Strictly speaking, it refers to art made up of 95 printable ASCII fixed-width characters (Xu et al., ), which are easy to copy from one file to another and display consistently across different computers.

Early studies focused on extracting ASCII art from general texts (Hiroki & Minoru, ; Hayashi & Suzuki, ; Suzuki, ) by exploring byte patterns, morphological features, compression ratios, etc. Subsequently, ASCII art gained more attention in the area of computer vision. Researchers generally categorize ASCII art into tone-based and structure-based types and have developed algorithms to synthesize ASCII art from images (Xu et al., ; Takeuchi et al., ; Xu et al., ; Chung & Kwon, ). Tone-based ASCII art emphasizes the intensity distribution of the reference image, while structure-based ASCII art focuses on the major structure of the content. The latter is mostly curated by human artists and is more challenging to synthesize automatically.

Works on ASCII art classification typically convert such text graphics into images as a default setting and exploit different image features to improve the classification accuracy of deep neural networks (Fujisawa et al., ; Matsumoto et al., ; Fujisawa et al., ). Fujisawa et al. () constructs ASCII art data automatically to enhance the models’ image classification ability. Most of the aforementioned works are tested using an ASCII art classification dataset containing only five categories, which is inadequate for comprehensively analyzing how well the LLMs and MLLMs can grasp the visual representation of ASCII art.

There are also works that take advantage of ASCII art to achieve specific goals. Jiang et al. () represent rich-formatting texts as ASCII art and find that it results in highly effective jailbreak attacks that bypass state-of-the-art defense techniques. In contrast, Wang et al. () find that tone-based ASCII art with rich visual information cannot be understood by current LLMs, which can be used as an effective tool to detect whether the participant is a bot or a human. ASCII art is also utilized to enhance LLMs’ spatial reasoning ability in Wu et al. ()’s work. Box diagrams, as a special kind of ASCII art, are widely used in the development lifecycle (Hayatpur et al., ) and have been benchmarked by Bayani () with recognition and generation tasks.

In this work, we regard ASCII art as an ideal information carrier that bridges the gap between the text and image modalities, to facilitate the understanding of modality-agnostic visual perception ability for both LLMs and MLLMs.

## 3 ASCII Art Recognition

We first define the ASCII art recognition task formally. Then, we introduced how we constructed the test and training data, dubbed ASCIIEval and ASCIITune, followed by statistical analysis.

### 3.1 Problem Formulation

We formulate ASCII art recognition as a multiple-choice question-answering problem. Let $T$ represent the text string of an ASCII art and $I$ refer to the corresponding image modality. The model is asked to predict the correct choice containing the concept depicted by $T$ or $I$ among candidates $C$.

For LLMs that only accept text input, the prediction $\hat{{\bm{y}}}$ is generated as follows:

$$ $\hat{{\bm{y}}}_{T}={\rm LLM}(T,C)$ (1) $$

For MLLMs, $\hat{{\bm{y}}}$ can be inferred under two additional conditions:

$$ $\displaystyle\hat{{\bm{y}}}_{I}$ $\displaystyle={\rm MLLM}(I,C)$ (2) $\displaystyle\hat{{\bm{y}}}_{IT}$ $\displaystyle={\rm MLLM}(I,T,C)$ $$

We denote the above three input conditions as Text-only, Image-only, and Text-Image, respectively. The final prompt is structured by corresponding string templates given the inputs. See Appendix B.

### 3.2 Data Collection for ASCIIEval

We carried out the data construction process in four stages to collect a high-quality test dataset covering a wide range of categories.

Data Preparation We first crawled ASCII art created by human artists from two online galleries (^2^22[https://asciiart.website/](https://asciiart.website/), [https://www.asciiart.eu/](https://www.asciiart.eu/)).

Classification Criteria Unification Next, we manually designed a 3-layer classification tree after unifying the categories based on the categorical information from the original websites and removing potentially harmful categories. The most fine-grained category is named the concept, representing the semantic meaning reflected in the art. Similar concepts are merged into second-layer groups. Finally, they are grouped into seven major classes inspired by the iOS emoji categories.
Each concept can be depicted in various ways by ASCII artists.

Data Filtering Subsequently, we conducted additional filtering operations using a combination of rules and human annotations as follows:

- $\circ$
Each ASCII art string was normalized by removing redundant empty spaces at the beginning of each line and at the end of the string, without compromising its visual semantics.
- $\circ$
ASCII art consisting of more than 100 lines, not belonging to reserved categories, and repetitive to other ASCII arts under the same concept were discarded. Repetition was identified by calculating the edit distance between two ASCII strings. If the distance divided by the length of the existing string was smaller than 0.3, the new ASCII art will be considered redundant.
- $\circ$
Human annotators were tasked to filter out unrecognizable art, remove words in ASCII art to focus the dataset on visual perception and avoid information leakage through words, and adjust the category according to the 3-layer category tree.

Multiple-Choice Data Construction Finally, we collected negative choices for each ASCII art by randomly sampling from other concepts within the same group. It should be noted that the ground truth labels were initially collected from the websites and subsequently verified by human annotators during the data filtering process. Each ASCII art string was then converted into an image.

### 3.3 Data Collection for ASCIITune

To further elicit models’ visual perception ability through supervised fine-tuning on the ASCII art recognition task, the creation of a training set is essential. An intuitive solution is to leverage previous works on ASCII art synthesis (Xu et al., ; ) by converting existing image datasets, such as ImageNet (Deng et al., ), into the required format. A public dataset (^3^33[https://huggingface.co/datasets/mrzjy/ascii_art_generation_140k](https://huggingface.co/datasets/mrzjy/ascii_art_generation_140k)) indicates that after automatic tone-based synthesis, approximately 85% data samples are filtered out due to poor quality. Furthermore, existing data conversion tools are inadequate for structure-based ASCII art, which accounts for 94% of the data in ASCIIEval according to annotators’ labels. Additionally, artists often combine both tone-based and structure-based features in a single artifact.

Therefore, we chose to collect the training set in a manner similar to ASCIIEval instead of relying on automatic conversion. Data sources include ASCII arts from another less well-organized website (^4^44[https://ascii.co.uk/art](https://ascii.co.uk/art)), and the crawled content was extracted into individual ASCII art pieces based on specific rules derived from observations. We also included the unrecognized ASCII art that was previously withdrawn during the construction of ASCIIEval. The normalized ASCII art is discarded if recognized as repetitive with samples in ASCIIEval or among each other.

Due to the large amount of data with diverse concepts, carefully categorizing data for high-quality distractors is unfeasible. Instead, we prompted Llama-3-70B-Instruct to generate negative choices given the ground truth concept and utilized the Perspective API to filter out unsafe samples based on the concatenation of candidate choices. Samples with scores less than 0.2 across all six dimensions, i.e., toxicity, severe toxicity, identity attack, insult, profanity and threat, are retained.

### 3.4 Data Analysis

Figure: Table 1: Statistics of ASCIIEval and ASCIITune.
Refer to caption: x2.png

As shown in Table 1, ASCIIEval comprises 3,526 samples distributed across 359 concepts, 23 groups, and 7 classes. The data distribution is illustrated in Fig. 2 (More in Appendix C). Each concept is represented by 9.82 ASCII art pieces on average, with a maximum of 170 and a minimum of 1, indicating an imbalance. ASCIITune consists of 11,836 samples with 2,307 concepts, which are more diverse but of lower quality. The number of characters and lines in ASCIIEval range from 4 and 1 to 15,282 and 100, respectively, reflecting its diversity and complexity. ASCIITune holds similar statistics.

Human Upper Bound We randomly extracted 100 samples from ASCIIEval three times and asked three different annotators to perform the multiple-choice task. They achieved 100%, 98% and 97% accuracy, respectively, demonstrating that this task is simple for humans.

## 4 Experiment Setup

### 4.1 Evaluated Models

For open-source instructed models, we experiment with LLMs from different model families, including Llama (Touvron et al., ), Qwen (Bai et al., ), Mistral (Jiang et al., ) and Gemma (Team, ), and with MLLMs from Llava (Liu et al., ), CogVLM (Wang et al., ), Qwen-VL (Bai et al., ) and Chameleon (Team, ). More in Appendix D.

Besides, GPT-4o (OpenAI, ) and Gemini (Reid et al., ) are selected as two leading proprietary models. Both of them are multi-modal models capable of accepting text or image inputs and outputting text. The specific versions we used are gpt-4o-2024-05-13 and Gemini-1.5-pro.

We implemented all open-source models with fewer than 100B parameters locally while collecting predictions from the other models through API requests (^5^55[https://www.together.ai/](https://www.together.ai/), [https://openai.com/](https://openai.com/)). All of the models are decoded using greedy search or by setting the temperature to 0 for fair comparisons and easier reproduction. The maximum number of output tokens equals 32 for open-source models and 128 for proprietary ones.

### 4.2 Evaluation Metrics

We perform an exact match between the correct option and a model’s output to calculate accuracy on ASCIIEval. As analyzed in Sec 3.4, the test data is unbalanced with varying art counts under each concept. Therefore, we adopt micro-accuracy over each sample for analyzing specific ASCII art characteristics, and macro-average over each concept for quantifying model performance.
We also define pass rate to measure a model’s ability to successfully follow the instruction by providing an effective answer. Proprietary models may fail due to their safety policy.

## 5 Results and Analysis

In this section, we first benchmark the performance of LLMs and MLLMs on ASCIIEval. Next, we investigate whether the existing MLLM training approaches enhance the vision understanding abilities of LLMs and delve deeper into understanding which types of ASCII arts are more challenging. Finally, we examine whether supervised fine-tuning can better align models for this task.

### 5.1 Performances of LLMs

The performance of LLMs with only text inputs is shown in Fig. 3. Most of these models exhibit strong instruction-following abilities, achieving a pass rate equaling 100%. Therefore, this metric is not shown in the figure and we primarily focus on macro-accuracy comparisons.

Figure: Figure 3: Macro-accuracy of LLMs on ASCIIEval. The red line is the random baseline (25%).
Refer to caption: x3.png

Proprietary Models vs. Open-source Models GPT-4o performs best among all the models. It outperforms the best open-source model, Gemma-2-27B-it, by 32.51%, indicating a conspicuous gap between the leading proprietary models and open-source ones. Gemini ranks second, outperforming the open-source models, but still significantly lags behind GPT-4o.

Comparisons among Model Families Models within the same series generally exhibit performance proportional to their sizes. However, this trend does not hold true across different model series and families. For example, Qwen2-72B-Instruct outperforms Qwen1.5-110B-Chat. Additionally, Gemma, with only 27B parameters outperforms other competitors with more than 70B and even hundreds of billions of parameters. This underscores the potential of developing lightweight models with strong visual perception abilities in text strings.

Overall Performances of LLMs Most models with fewer than 10B parameters, including the MoE model Mistral-8x7B-Instruct-v0.1, perform similarly to a random baseline. None of these models achieve an accuracy higher than 50%, with GPT-4o ranking first at only 42.77%. Although it’s hard to guarantee that the ASCII art in ASCIIEval was never used during pre-training, the poor accuracy reflects that ASCIIEval stands as a challenging benchmark for LLMs, underscoring the oversight of visual perception ability in current LLMs.

### 5.2 Performances of MLLMs

We evaluate MLLMs using different input modes as introduced in Sec. 3.1.

**Table 2: Performance of MLLMs with different input modalities. Accuracy (%) refers to macro accuracy. Pass rate (%) is listed to show the instruction-following ability of MLLMs. The highest accuracy is in bold and the second highest are underlined. Models are ranked by Avg, defined as the mean of the accuracy under different modes horizontally.**
| Models | Avg | Text-only | Image-only | Text-Image |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Accuracy | Pass | Accuracy | Pass | Accuracy | Pass |  |  |
| GPT-4o | 67.36 | 42.88 | 99.97 | 82.68 | 98.75 | 76.52 | 99.83 |
| CogVLM2-Llama3-chat-19B | 53.07 | 24.73 | 99.32 | 67.80 | 100 | 66.68 | 100 |
| Llava-v1.6-34B | 51.87 | 28.62 | 100 | 65.66 | 100 | 61.33 | 100 |
| Gemini-1.5-pro | 50.84 | 33.49 | 97.36 | 60.69 | 99.46 | 58.33 | 98.78 |
| Llava-v1.5-13B | 49.52 | 26.00 | 100 | 61.87 | 100 | 60.70 | 100 |
| Llava-v1.5-7B | 49.45 | 24.66 | 100 | 62.18 | 100 | 61.52 | 100 |
| Llava-v1.6-mistral-7B | 48.54 | 25.89 | 100 | 60.72 | 100 | 59.02 | 100 |
| Llava-v1.6-vicuna-13B | 47.43 | 26.03 | 100 | 59.70 | 100 | 56.55 | 99.52 |
| CogVLM-Chat-hf | 46.61 | 21.25 | 86.07 | 61.00 | 100 | 57.58 | 99.97 |
| Qwen-VL-Chat | 39.10 | 24.79 | 90.70 | 52.32 | 96.68 | 40.09 | 77.94 |
| Chameleon-30B | 21.08 | 0.01 | 3.29 | 34.54 | 99.97 | 28.70 | 100 |
| Chameleon-7B | 18.13 | 0.00 | 0.00 | 26.46 | 96.17 | 27.93 | 99.40 |

Proprietary Models vs. Open-source Models Results in Table 2 indicate the gap between GPT-4o and other models for MLLMs. It achieves 43.88%, 82.68%, and 76.54% on the three modes with nearly 100% pass rate, while the second-best results lag behind by 14.26%, 14.86%, and 9.84% in accuracy respectively. GPT-4o not only handles character strings better but also understands the ASCII art images well regardless of the style and abstractiveness differences compared to other image datasets, such as ImageNet (Deng et al., ) and MS-COCO (Chen et al., ). Nevertheless, GPT-4o, with the most competitive setting, still underperforms the human upper bound (98.33%).

Comparisons among Input Settings Another observation is that the performance follows the trend Image-only $>$ Text-Image $>$ Text-only. Image encoders in MLLMs capture the visual information in text strings more effectively, leading to superior performance over the Text-only mode.
Generally, we expect that multi-modal models can provide a more holistic understanding of the data. However, when incorporating text modality with image, the performance of all models except Chameleon-7B drops with a maximum decrease of 12.32% compared to the Image-only setting. This reveals that existing MLLMs are unable to understand the complementarity and consistency of different modalities, resulting in an inability to make correct predictions.

Degradation of Instruction-following Ability We also observe that the instruction-following ability of some MLLMs, as indicated by the pass rate, drops significantly when ASCII art is provided in text strings. Among open-source late-fusion MLLMs, models from the Llava family show little influence on the backbone LLMs’ ability, whereas others experience considerable degradation. The only early-fusion MLLMs, Chameleon, falls to approximately 0% accuracy and pass rate under the Text-only setting. Ideally, early-fusion strategies should better integrate the representations and interactions among modalities, leading to a more cohesive and accurate understanding of data. However, their poor performance and notable decline indicate significant room for improvement.

### 5.3 Analysis of the results

As most open-source MMLMs are trained on a pre-trained LLM using late fusion strategies, we first investigate whether the LLM’s visual perception ability improves after MLLM training. Next, we analyze the trends in model performance under different ASCII art sizes and categories. The top 5 LLMs under the Text-only setting and the top 5 MLLMs under the Image-only setting, which are generally their default input modalities, are primarily considered.

#### 5.3.1 Do LLM’s visual perception ability evolve after MLLM training?

Previous work on multi-modal models usually focuses on MLLMs’ visual understanding ability over LLMs. A natural question arises: Can an MLLM’s training under the late fusion strategy enhance its backbone LLM’s visual perception ability? Given the semantic-equivalence feature of ASCII art, this question potentially explores how well the representations among different modalities are fused.

In Table 3, we compare the performance of the late-fusion MLLMs and their backbone LLMs under the Text-only mode. Qwen-VL-Chat achieves an improvement of 1.49% over Qwen-7B-Chat, while their absolute performance remains below the random baseline. The accuracy of LLMs trained by CogVLM decreases by 4% to 5%, whereas the fluctuation in accuracy for the Llava series is negligible.
In summary, the results indicate that current late-fusion approaches do not enhance the LLMs’ visual understanding ability, which warrants further exploration.

**Table 3: Comparisons of MLLMs and their backbone LLMs measured by macro-accuracy (%).**
| MLLM | LLM (backbone) | MLLM Acc | LLM Acc | $\Delta$ |
| --- | --- | --- | --- | --- |
| Llava-v1.5-7B | Vicuna-v1.5-7B | 24.66 | 26.05 | -1.39 |
| Llava-v1.5-13B | Vicuna-v1.5-13B | 26.00 | 25.47 | 0.53 |
| Llava-v1.6-mistral-7B | Mistral-7B-Instruct-v0.2 | 25.89 | 26.28 | -0.39 |
| Llava-v1.6-vicuna-13B | Vicuna-v1.5-13B | 26.03 | 25.47 | 0.56 |
| Llava-v1.6-34B | Nous-Hermes-2-Yi-34B | 28.62 | 27.88 | 0.74 |
| CogVLM-Chat-hf | Vicuna-v1.5-7B | 21.25 | 26.05 | -4.80 |
| CogVLM2-Llama3-chat-19B | Llama-3-8B-Instruct | 24.73 | 28.71 | -3.98 |
| Qwen-VL-Chat | Qwen-7B-Chat | 24.79 | 23.30 | 1.49 |

#### 5.3.2 Is the complexity
proportional to the number of characters?

We classify test samples into 7 subsets based on the number of characters in the ASCII art. The results are shown in Figure 4.

LLMs are proficient in recognizing ASCII art with fewer characters, and they even outperform competitive MLLMs with image inputs, such as CogVLM2-Llama3-chat-19B, on ASCII arts with fewer than 50 characters. In smaller ASCII art, significant features are densely packed within consecutive characters. For instance, the string “() '`;” captures some major characteristics of a dog in Fig. 1. The results indicate that LLMs excel at capturing the relationship between a concept and some featured combinations of characters. However, as the length of the ASCII art increase, such features are likely to be diluted, and much stronger 2D perception abilities are required.

Conversely, MLLMs are better at recognizing larger ASCII art. Smaller ASCII art tends to be more abstract, where artists try to depict significant features of a concept with few characters. In contrast, larger ASCII art is more similar to real images or posters that MLLMs are trained on. For example, the Spiderman in Fig. 1 shares much more similarity in terms of outline and luminance contrast to a real poster. Nevertheless, MLLM also face challenges on ASCII arts containing more than 1600 characters, as evidenced by the performance drop of both GPT-4o and CogVLM-Chat-hf. This may be due to the fact that larger ASCII art contains more spaces with the same grayscale, providing ineffective or redundant features for MLLMs, thereby aggravating the recognition difficulty.

In summary, LLMs are adept at understanding short and abstract art, and MLLMs are proficient in interpreting longer and more detailed art, which are mainly influenced by the characteristics of the input modality (More in Appendix E).
Although different modalities have strengths with various forms of ASCII art, late fusion strategies fail to combine them effectively, as showed in Sec. 5.3.1.

Figure: (a) LLM
Refer to caption: x4.png

#### 5.3.3 How do models perform on different categories?

Models’ performances across the 7 different classes are shown in Fig. 5. LLMs trained purely on text corpus perform better at recognizing ASCII arts belonging to the “objects” class. MLLMs given image inputs show consistent improvement in recognizing “travel & places” over LLMs compared to other classes relatively. Moreover, all models struggle with ASCII art referring to “symbols”, which comprise different logos and astrology symbols. MLLMs actually perform quite well at recognizing well-known logos, such as Apple and Linux, where GPT-4o achieves 97.96% macro-accuracy and CogVLM2-Llama3-Chat-19B gets 91.16%. However, their performance drops dramatically on relatively niche astrology symbols. Nevertheless, it is simple for both LLMs and MLLMs to answer the question “Can you show me some astrology symbols?”. Existing models tend to use rare Unicode characters or emojis to explain the symbols, but cannot understand the visual semantics embedded in those symbols flexibly. More cases can be found in Appendix G.

Figure: (a) LLM
Refer to caption: x6.png

### 5.4 Can Supervised Fine-tuning elicit models’ visual perception capability?

We fine-tune Llama-3.1-8B-Instruct and Llava-v1.6-mistral-7B using ASCIITune constructed in Sec. 3.3. The LLM is trained solely with the Text-only data setting, while the MLLM is trained under four different settings: Text-only, Image-only, Text-Image, and Random. “Random” represents that we uniformly select from the above three modality settings for each input sample. All of the models are tuned for 2 epochs with a batch size of 16. The results of the fine-tuned models and comparisons to the original results are shown in Table 4.

Models with pure text inputs don’t significantly benefit from fine-tuning on the task-specific dataset. They achieve at most a 1.30% improvement under the Text-only setting, while MLLMs with image inputs gains more than a 10% increase accuracy. Even though models are able to recognize ASCII art strings as analyzed above, the experiments also highlight the limitations of current models.

Moreover, supervised fine-tuning on this dataset helps Llava better leverage the representations from both modalities, as shown by the further improvements of Text-Image results over the Image-only results. The decrease in performance of Llava tested under the Text-only mode indicates that the model tends to gather useful information from the image modality when trained by text-image pairs. Llava trained using the Random setting shows better performance on Text-only samples, though with a compromise on samples with image inputs. Exploring training techniques to make the information among modalities more compatible and to improve the accuracy remains a direction of future work.

**Table 4: Macro-accuracy(%) of the mode after supervised fine-tuning on different modes of training data. The corresponding performance on modes of ASCIIEval are shown in the last three columns.**
| Model | SFT Data | Text-only | Image-only | Text-Image |
| --- | --- | --- | --- | --- |
| Llama-3.1-8B-Instruct | Text-only | 27.46↑0.24 | - | - |
| Llava-v1.6-mistral-7B | Text-only | 26.49↑0.60 | - | - |
| Image-only | - | 75.58↑14.86 | - |  |
| Text-Image | 25.50↓0.39 | 76.78↑16.06 | 76.92↑17.90 |  |
| Random | 27.19↑1.30 | 74.52↑13.80 | 74.92↑15.90 |  |

## 6 Conclusion

In this work, we focus on analyzing and eliciting models’ visual perception ability in text strings. We introduce the ASCII art recognition problem, which task models to recognize the concepts depicted by the art conveyed through different modalities. We constructed both test and training data, and conducted comprehensive evaluations with dozens of LLMs and MLLMs followed by supervised fine-tuning. Results pinpoint the weaknesses of current models on this task, highlighting a lack of effective fusion techniques for semantic-equivalent information across different carriers.

## Appendix A Data License

We express our gratitude to the ASCII artists whose fantastic creations underpin our research. In order to assess the visual perception abilities of models, we made slight modifications to the original ASCII art for the test set ASCIIEval. Meanwhile, we retained the original ASCII art and provided the URL to the data source. It is important to note that our data is licensed under CC BY NC 4.0, which permits only non-commercial use and is intended exclusively for research purposes.

## Appendix B Prompt Template

We designed three prompt templates for different input modes:

All of the models except Qwen-VL are evaluated based on these prompt templates with minor modifications to adapt to their default settings, especially for the position of the image.

Qwen-VL is more sensitive to prompt templates according our experiments. Therefore, we adapted the above templates into Qwen-VL’s original format, which is ”Context: … Question: … Answer:”.

## Appendix C Data Statistics

The number of samples and the hierarchical relationship between classes and groups illustrated in Figure 2 are shown in Table 5.

**Table 5: The number of samples under each category.**
| Classes | Groups |
| --- | --- |
| animals & natural (1,122) | animal (870), plant (130), nature (122) |
| objects (777) | object (451), electronics (192), clothing (81), furniture (53) |
| smileys & people (644) | role (199), character (195), body (146), occupation (68), people (36) |
| activities (473) | event (207), sport (126), activity (84), instrument (35), monument (21) |
| travel & places (406) | transportation (123), building (123), places (30) |
| food & drink (66) | food (66) |
| symbols (38) | logo (27), astrology (11) |

## Appendix D Details about Evaluated Models

For open-source instructed models, we experiment with the following LLMs and MLLMs:

##### LLMs.

Llama (Touvron et al., ) contains three collections of generative models with different sizes, including Llama-2, Llama-3, and Llama-3.1; Qwen (Bai et al., ) is another group of models with instructed verions, including Qwen, Qwen1.5 and Qwen2 series; Mistral (Jiang et al., ) includes different versions of instruction fine-tuned models, i.e., Mistral-7B-Instruct-v0.1, v0.2 and v0.3. Besides, Mixtral-8x7B-Instruct-v0.1 and Mixtral-8x22B-Instruct-v0.1 which are pre-trained generative Sparse Mixture of Experts are also compared; Gemma (Team, ) is a family of lightweight text-to-text models with instruction-tuned variants. We considered Gemma-2-9B-it and Gemma-2-27B-it.

##### MLLMs.

Llava (Liu et al., ) augmented a pre-trained LLM with a pre-trained vision encoder. The vision model’s representations are projected into the LLM’s representation space with a projection layer, and it is frozen during instruction tuning while the projector and the backbone LLM are updated; CogVLM (Wang et al., ) aims at retaining the original capabilities of the LLM while adding visual understanding abilities. Representations from the pre-trained vision transformer encoder are passed through an MLP adapter as the input, and a group of trainable visual expert modules in the attention and FFN layers are introduced into the LLM. All of the parameters except the ones from the original LLM are tuned; Qwen-VL (Bai et al., ) proposed a position-aware vision-language adapter for compressing image features. The model is trained through three stages, i.e., pre-training, multi-task pre-training and supervised fine-tuning; Chameleon (Team, ) is a family of early-fusion token-based mixed-modal models, different from the above late-fusion ones.

## Appendix E Analysis on Samples under Different ASCII Art Sizes

Based on the length characteristics of different ASCII art, we divided the test set into various subsets, as shown in Table 6.

**Table 6: The number of samples with ASCII arts divided by different characteristics.**
| #Characters | [1, 50] | (50,100] | (100, 200] | (200, 400] | (400, 800] | (800, 1600] | (1600, +$\infty$) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| #Samples | 221 | 366 | 546 | 710 | 760 | 618 | 305 |
| #Lines | [1,5] | (5, 10] | (10, 15] | (15, 20] | (20, 25] | (25,+$\infty$) | - |
| #Samples | 414 | 854 | 699 | 534 | 399 | 626 | - |

The performances of LLMs and MLLMs on testing samples grouped by the number of lines contained in the ASCII art are shown in Fig. 6. The trends are similar to those grouped by the number of characters in Sec 5.3.2, i.e., LLMs favor smaller ASCII art while MLLMs prefer larger ASCII art.

Figure: (a) LLM
Refer to caption: x8.png

We also show the trends of the top 5 MLLMs under Text-only and Text-Image modes respectively in Fig. 7. It reveals that the overall trend in Text-only mode is similar to that of LLMs, indicating that models are easier to be adept at small-sized ASCII art in text format. In contrast, the overall trend in Text-Image mode shares more similarity with the Image-only mode, pointing out the strong bias towards image signals of MLLMs.

Figure: (a) MLLMs in the Text-only mode.
Refer to caption: x10.png

## Appendix F Performance on Samples under Different Categories

The models’ performance under different groups is shown in Fig. 8. Overall, the performance of MLLMs is more balanced across different categories, except for the drops in “astrology” and “instrument”. Meanwhile, LLMs’ accuracy fluctuates among different groups, with “electronics”, “food” and “object” topping the rank.

Figure: (a) LLM
Refer to caption: x12.png

## Appendix G Case Studies

We selected seven samples belonging to different classes from ASCIIEval and show the cases in Fig. 9 and Fig. 10. The correct answers are shown in red. The top-ranking models are highlighted in yellow if they make correct predictions. Otherwise, they are highlighted in blue with oblique lines.

Figure: Figure 9: Case studies (Part I).
Refer to caption: x16.png

Figure: Figure 10: Case studies (Part II).
Refer to caption: x18.png

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. How does the tokenization process affect the model's ability to understand visual semantics in ASCII art when processed as text?

2. From the perspective of AGI, why enhancing the model's performance on understanding ASCII arts is important?
soundness: 2
strengths: 1. The paper attempts to use ASCII art to evaluate the visual understanding capabilities of LLMs and MLLMs, offering a new perspective for assessing these models.

2. The authors manually constructed the ASCIIEVAL and ASCIITUNE datasets, containing 3,526 and 11,836 samples respectively, which can be used for evaluating and fine-tuning LLM/MLLM models on ASCII arts understanding.

3. A wide range of different LLMs and MLLMs were tested on ASCIIEVAL, with detailed analysis and discussion of the results provided, contributing to a deeper understanding of model performance.
summary: The paper explores the use of ASCII art as a tool to evaluate the visual perception capabilities of large language models (LLMs) and multimodal large language models (MLLMs). It introduces two datasets, ASCIIEVAL and ASCIITUNE, which contain 3,526 and 11,836 samples respectively, aimed at assessing and fine-tuning these models. The study conducts extensive testing on a variety of LLMs and MLLMs using the ASCIIEVAL dataset, providing detailed analysis and insights into their performance.
weaknesses: 1. My main concern is that ASCII art may not effectively represent modality-agnostic visual perception due to the inherent differences in information representation between text and image modalities. 

    In text form, LLMs process token sequences derived from a tokenizer, independent of the visual shapes of ASCII characters. In contrast, as images, semantic information is conveyed through pixel rendering, influenced by factors unrelated to the characters themselves, such as font and size. 

    As a result, using ASCII art might lead to misleading conclusions about a model's visual perception abilities, as performance can vary significantly between text and image inputs due to the inherent different nature of information representation.

2. Since most real-world images are not represented by ASCII arts, the potential application scenario is also limited.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. In Section 3.2 on Data Filtering, is plain edit distance sufficient? Standard edit distance is known to be sensitive to temporal scaling (e.g., "ABBC" vs. "AAABBBBBBCCC"). It may be worth considering methods like Dynamic Time Warping as well.

2. For Figure 4, does Figure 4(a) operate in text-only mode and Figure 4(b) in image-only mode?

3. ASCIITune is likely to be more diverse and noisier than ASCIIEval. I am curious about its quality. It might be helpful to sample a portion of the data and measure human performance as a quality check.

4. While the character count of textual ASCII art is essential, providing the token length after tokenization could also be valuable. Additionally, I would like to know if the ASCII art data used in experiments respects the context length limitations of each model tested.
soundness: 2
strengths: Introduction of a new task: ASCII Art Recognition
- Although many image-text paired datasets exist for MLLM training, ensuring that the images and text in these datasets have precisely the same semantics is often challenging. Evaluating the alignment between vision and text modalities in MLLMs requires a strictly controlled setting, traditionally achieved using document data. This is typically done by pairing rendered images with OCR-ed text. However, this approach is quite limited to character recognition tasks. The ASCII art recognition task introduces a more suitable approach for expanding the field of multimodal representation learning.

Data release: ASCIITune (for training) and ASCIIEval (for evaluation)
- This work utilized various public data sources, constructed multiple-choice questions for more straightforward quantitative evaluation, avoided duplication between training and evaluation datasets, and appropriately excluded potentially sensitive categories. I appreciate releasing such carefully selected data and believe it will benefit the community.

Identification of areas where current LLMs and MLLMs have limitations
- One of the crucial implications of this study is that many current LLMs and MLLMs, both commercial and open-source, have difficulty recognizing textual ASCII art. This might be because these models haven’t been exposed to much of this type of data (ASCII art) during pretraining or due to structural limitations within each model. Similar issues have also come up in vision tasks (e.g., challenges with recognizing analog clocks). However, the inability to recognize information that can be fully represented in text is a noteworthy finding.
summary: This study introduces an ASCII art recognition task to assess the visual information perception abilities of multimodal large language models (MLLMs). Using data from various public sources, the authors built two datasets, ASCIITune for training and ASCIIEval for evaluation, following thorough normalization and filtering processes. Evaluation of numerous existing LLM and MLLM models with these datasets revealed that most models struggle with recognizing textual ASCII art. Additionally, it was observed that, for most MLLMs, performance in recognizing rendered ASCII art is significantly better than textual ASCII art. Finally, even after fine-tuning, performance improvements for textual ASCII art were minimal compared to the more substantial gains seen for rendered ASCII art.
weaknesses: Section 5.1 shows that "current LLMs do not fully understand textual ASCII art." Additionally, Section 5.2 shows that "most MLLMs recognize rendered ASCII art more effectively than textual ASCII art." The lower recognition performance for textual ASCII art seems to stem from LLMs' limitations, as further confirmed in Section 5.3.1. Therefore, it is important to explore why LLMs struggle with the proposed task and consider offering potential solutions. While this study attempts to address this through SFT (supervised fine-tuning), it appears insufficient.

Typical MLLM training generally involves two main stages: (1) a vision-text alignment stage utilizing image-caption data and (2) an instruction tuning stage using image-text QA data. However, representation learning for the text modality itself usually occurs in the LLM's pretraining phase. Therefore, the SFT approach used in this study may not be ideal for enhancing ASCII art recognition in pretrained MLLMs. Additionally, since SFT uses class labels for supervision (e.g., "A: bird"), it may not be suitable in cases where recognition capability is limited, and there may also be insufficient data and training FLOPs.

Here are a few methods worth attempting to address this issue. I believe these efforts and analyses could provide more valuable insights for readers:
1. Further pretraining (M)LLMs on the "textual ASCII art" as a text corpus.
- This step could allow the model to learn patterns specific to textual ASCII art directly.
2. Fine-tuning MLLMs to input rendered ASCII art and output "textual ASCII art" (unfreezing both the vision encoder and LLM).
- This approach may enable the model to learn both image comprehension and the process of generating textual ASCII art.

---

confidence: 4
contribution: 3
initial_rating: 5
presentation: 2
questions: 1. It seems like ASCII art is very sensitive to minor character changes, did the authors ablate this? Like a small character being missed can lead to the ASCII art not making any sense.
2. In the human evaluation, were there some images that had multiple interpretations? 
3. Could you improve the motivation and introduction? Currently, it feels a bit confusing, covering many points without clearly explaining why LLMs and MLLMs need this capability. While it's an interesting idea, I’m not fully convinced of its practical significance. This isn’t necessarily a criticism of the paper, but adding clarity would be helpful.
soundness: 3
strengths: 1. The authors propose a novel problem, the idea of this benchmark is quite new.
2. The benchmark proposed is new and large, giving the community a new limitation of Multimodal Models to discover and analyze.
summary: The authors propose a novel benchmark to test the ability of multimodal large language models to understand ASCII art. Their experiments show that most multimodal large language models suffer at this task, performing as poorly as random. On the other hand, humans can perform near perfectly at this task. However this changes when we provide an LLM an image of the given ASCII art. The authors benchmark a wide variety of LMMS and report their results.
weaknesses: 1. I am not entirely convinced by the motivation of the paper, I don't really understand why it impacts security. The authors should be more clear while describing why is this important for MLLMs to learn.
2. I tried pasting some of the ASCII art text as shown in FIG 1, and was not able to reproduce it in python. The closest I could get was with the dog, this gives me some doubts about the evaluation. The other two ASCII art were not reproducible at least when I pasted it in python.
3. Additionally, I feel some art can have multiple interpretations; for example, certain depictions of cats might resemble rats or raccoons. Since this is art, it naturally lends itself to varied perspectives.
4. Missed reference to an important paper: Can Large Language Models Understand Symbolic Graphics Programs?

Meta-review: This paper proposes a benchmark to evaluate how Large Language Models (LLMs) and Multimodal LLMs (MLLMs) can perform visual perception through ASCII art. While the paper presents a novel perspective on LLMs' visual perception capabilities and provides interesting analyses, reviewers noted that ASCII art has limitations in representing modality-agnostic vision representations and its narrow scenarios for potential use cases.

The paper presents various experimental analyses. However, it lacks adequate analysis or plausible hypotheses for key findings, such as why state-of-the-art LLMs lag behind human performance, or why GPT-4o significantly outperforms other open-source models.

Overall, the AC believes that the reported results likely stem from LLMs' one-dimensional text understanding (i.e., how LLMs encode the texts). Therefore, it is difficult to find a deep and new understanding of LLMs' internal perception mechanisms throughout this paper. Considering this limitation and its restricted applicability, the paper requires substantial improvements before it can be accepted.

Decision: Reject