Can we talk models into seeing the world differently?

## Abstract

Abstract Vision language models (VLMs) have drastically changed the computer vision model landscape in only a few years, opening an exciting array of new applications from zero-shot image classification, over to image captioning, and visual question answering. Unlike pure vision models, they offer an intuitive way to access visual content through language prompting. The wide applicability of such models encourages us to ask whether they also align with human vision — specifically, how far they adopt human-induced visual biases through multimodal fusion, or whether they simply inherit biases from pure vision models. One important visual bias is the texture vs. shape bias, or the dominance of local over global information. In this paper, we study this bias in a wide range of popular VLMs. Interestingly, we find that VLMs are often more shape-biased than their vision encoders, indicating that visual biases are modulated to some extent through text in multimodal models. If text does indeed influence visual biases, this suggests that we may be able to steer visual biases not just through visual input but also through language: a hypothesis that we confirm through extensive experiments. For instance, we are able to steer shape bias from as low as 49% to as high as 72% through prompting alone. For now, the strong human bias towards shape (96%) remains out of reach for all tested VLMs.

## 1 Introduction

As the old adage goes, all models are wrong, but some are useful. Similarly, all (machine learning) models are biased, but according to the no free lunch theorem, only some biases are useful [, ].
While most biases observed in language models reflect social particularities that originate in unbalanced training data, vision models have been shown to be exceptionally misaligned with human perception, referred to as the *texture vs. shape bias* []. Specifically, when recognizing objects, an object’s shape often plays a minor role to models while the texture is strongly preferred. Humans instead predominantly decide by the object’s shape ($96\%$ shape over texture decisions). This finding has received significant attention [, , , , , ], because it shows that our vision models, while approaching near-human levels in accuracy on specialized tasks, poorly approximate fundamentals of human perception.

As the current generation of deep learning models is increasingly multimodal [, , , ], it is often unclear whether these models inherit certain biases from their text encoders, vision encoders, or whether those interact and are combined through multimodal fusion. Specifically for the (vision-only) texture vs. shape bias, we are interested in how this bias is influenced by language. If language is indeed able to influence a vision-only bias, this offers the possibility of aligning and generally steering biases simply and intuitively via prompts.

We study of the texture vs. shape bias and its steerability in recent vision language models (VLMs). Here we use VLM to refer to text/vision-input models that output text generated by a large language model (LLM).
Our investigation shows that the model’s inherent bias towards texture is far less pronounced in VLMs than in most previously studied vision-only models. As shown in Fig. 1, most VLMs decide by shape more often than by texture, even when prompted in a neutral way (while not approaching the human shape bias).
Further, we can confirm that VLMs learn to understand the visual concepts of *shape* and *texture*, such that they allow steering of the prediction to some extent by simple prompt modifications.

Our contribution can be summarized into the following findings:

- •
Text appears to influence visual texture/shape bias. Through a large-scale study, we report that even though texture bias is a vision-only bias found in common vision models, VLMs are often more shape-biased than their vision encoders, even though they decide based on texture more often than humans would do (Sec. 4).
- •
VLMs have an understanding of the visual concepts shape and texture which allows steering. Given that multimodal models do not appear to simply inherit visual biases from their vision encoders, this suggests that we may be able to steer visual biases through language. We test and confirm this hypothesis with a set of experiments. For instance, shape bias can be steered as low as 49% and as high as 72% through prompting alone: given that visual biases can be influenced through text, this insight can be harnessed to steer them (Sec. 5).

## 2 Related Work

Vision Language Models.
Sparked by the success of vision language pretraining [, , , ], where features extracted from image-text paired data are aligned in a joint embedding space, recent VLMs added language modeling during training [, , ], enabling models to reason about images.
Subsequently, finetuning these VLMs on instruction-following data [, , , , ], such as reinforcement learning from human feedback (RLHF) [], enables users to prompt these models, easing their usability for humans.
Resulting models are commercialized [, , ] or open-sourced [, , ], and consequently become accessible to a wide range of users.

Shape Bias.
Humans primarily rely on shape information to recognize objects. On the other hand standard ImageNet-trained vision-models, such as convolutional neural networks (CNNs), are biased towards texture to make their classification decisions []. Nonetheless, shape information can still be present in layers/latent space of the model before the classifier [, ].
Prior research has shown that the texture bias of CNNs can be reduced in training [, , , , , , ].
But the network architecture has a high influence, and vision-only ViTs [] were shown to be more shape-biased by default [], more human-like [], scalable by data size [], and can be explicitly designed to separate shape and texture in their token space []. Jointly embedding vision and language in these networks (but not CNNs as we will show!), through CLIP [], further increases their shape bias in zero-shot classification []. Yet, these models still do not reach human levels. The only known models to achieve such levels are image-generative classifiers [], which in some way also combine vision and language.

Other Biases.
For the sake of completeness, we list other relevant works on uni/multimodal biases. However, we want to point out that the focus of this paper lies on the texture/shape bias.

Vision biases have been widely investigated, such as single-demographic effects (race and gender) for face recognition tasks [, ], or high-frequency biases for classification tasks [, ]. Recently, [] found another bias that separates human from model vision, by investigating the critical frequency band of object recognition. For language models, several works focus on investigating societal biases, such as gender and race [, ] and ways of debiasing them [, , ], or explicitly forcing them []. A recent study also found that LLMs are biased towards high-value over likely options [].
Another study focused on encoded moral beliefs []. LLMs can also pick up human traits -
one study found that adding "take a deep breath" to prompts improves performance []. Of course, some of the unimodal biases also apply to VLMs (*e.g*., []), but a few works have also explicitly focused on biases in VLMs.
For example, neurons of CLIP [] were studied in [], revealing that some neurons respond to the same concept regardless of its presentation, which is a potential reason for the high generalizability. On the other hand, this enables attacks by rendering text on images (typographic attacks). Additionally, several works demonstrated that VLMs fail to count objects [, , ], and generally struggle in structured tasks [].

## 3 Methodology

A cornerstone of our analysis is the measurement of texture/shape bias in (LLM-based) VLMs when performing tasks that require some level of image classification. In the following subsections, we introduce how and in which tasks exactly we measure shape bias.

### 3.1 Measuring Texture/Shape Bias

We use the texture-shape cue-conflict classification problem (cue-conflict) [] consisting of 1,280 samples with conflicting shape and texture cues synthetically generated via a style transfer model [] from ImageNet [] samples (see Fig. 1 for examples). The shape and texture classes belong to 16 super-classes of ImageNet. Following [], we have excluded 80 images from the dataset where texture and shape cues belong to the same class.

We want to emphasize that from an information perspective alone, predicting either label (or both) would be correct. However, humans tend to prioritize the shape cue for predictions which is in stark contrast to most models [].

Using the shape or texture cue label as the correct label allows us to measure the shape and texture accuracy, respectively. Based on these measurements, we measure the cue accuracy as the ratio of predictions that contain either the shape or texture label (as opposed to a misclassification):

$$ $\textit{Cue~{}Accuracy}={\textit{Shape~{}Accuracy}+\textit{Texture~{}Accuracy}}$ (1) $$

Throughout the paper, we will refer to this as the accuracy. We use the definition of shape bias [], which is defined by the ratio of shape decisions over accurate decisions:

$$ $\textit{Shape Bias}=\frac{\textit{Shape~{}Accuracy}}{Cue~{}Accuracy}$ (2) $$

While we primarily focus on measuring the shape bias in this study, the accuracy provides an important signal of the model quality and robustness, as well as for the comparability of results.

### 3.2 VLM Tasks

We measure the texture/shape bias of VLMs in two common tasks: visual question answering (VQA) [] where we seek to obtain a zero-shot classification [] of the object, and image captioning [] where we look for an accurate description of the image. In both cases, we use the cue-conflict dataset in single-round answering with no shared conversation history between conversations.

#### 3.2.1 Image Captioning.

In this task, we are instructing models to generate brief descriptions ("Describe the image. Keep your response short."). We specifically request the model to provide a short response to encourage it to single out the most crucial aspects of the image according to its judgment. Additionally, this has the benefit of faster inference.

##### Response Extraction.

As the responses are open-ended we rely on zero-shot classifications of the generated description to marginalize out the most descriptive class. To this end, we embed the generated descriptions and all (raw) class labels using ember-v1 [] and predict the class with the smallest cosine distance (similar to zero-shot classification in CLIP []). However, the generated caption may refer to multiple class labels (or none). As an additional signal, we perform a more granular analysis using an additional LLM (Nous-Hermes-2-Mixtral-8x7B-DPO []) by instructing the model to extract all mentioned classes (similar to []). This allows us to understand if the model detects both cues (but the embedding model enforces a specific prediction) and to quantify how often the model response is too generic to detect any class.

#### 3.2.2 VQA Classification.

Following the questioning style in LLaVA [], we ask the model "Which option best describes the image?" and provide an alphabetic enumeration of all class labels in the style "A. airplane". For a simpler response extraction and confidence evaluation (see below), we end the prompt by instructing the model to answer with only the letter corresponding to the correct answer ("Answer with the option’s letter from the given choices directly."). Compared to captioning, this is similar to the discrimination in ImageNet [] image classifiers [, , , , ] in the sense that it only allows the model to respond with a single class and does not provide an option to not answer - if models follow the instruction.

##### Response Extraction.

Despite instructing the models to only respond with an option letter, we observe multiple response styles: option letter + label ("H. cat."), just the label ("cat."), long explanation containing the option letter and/or label ("The image features a black and white image of a cat."). In all cases, punctuation and capitalization may be different ("H.", "H", "h)"). The first two response styles are easily correctable by simple post-processing (we prioritize the option letter in case of a conflicting option letter and label), and in some cases, explanations can be corrected as well if the response includes the option letter. However, we avoid heavy post-processing and consider individual answers as wrong if they are not recoverable. In most cases, the ratio of these is negligible.

##### Confidence of VQA predictions.

All answer options in our prompts correspond to a single character and, thus, token. Well-behaving models, where the response consistently starts with the option letter (and nothing else) allow us to gather insights into the prediction process. For these models, the logits of each token correspond to the logits of option letters. By applying a softmax function we can then analyze the confidence in each option (we map invalid tokens to a separate null class). This allows us to perform additional studies on sampling behavior.

## 4 Are VLMs Biased towards Texture or Shape?

In this section, we measure the shape bias of VLMs and try to understand if there are driving factors (specific models, scale, RLHF [] etc.) behind potential differences in behavior. Beyond this quantitative analysis, we aim to better understand the internal mechanisms through insights into the decision formation and ablation of the representation of the vision encoder.

### 4.1 Main Results

We start our experimental evaluation by measuring the shape bias in the VQA and captioning tasks, using a collection of diverse vision language models reflecting the multitude of research directions. These models include connections of common pretrained CLIP encoders and LLMs [, , , , , , , ], mixture-of-expert-LLMs [], optimized architectures for resource-constrained systems [], finetuning with RLHF [, ], massive vision encoders []. Additionally, we survey commercial, closed-source models like Gemini Pro Vision 1.0 [], GPT-4V (Preview) [], and Qwen-VL Plus/Max [] where access is limited to APIs and few details are known. For a detailed list of models, we refer the reader to the appendix.

Figure: Figure 2: Most VLMs are slightly shape-biased but some models show differences when asked to describe an image compared to VQA. We measure the shape bias on the cue-conflict dataset []. For reference, we also provide measurements on ResNet-50 [] from the initial shape bias study [], zero-shot classification (CLIP ViT-L/14 []), and a human average (over 10 subjects []).
Refer to caption: x2.png

The results in Fig. 2 (a full table is shown in the appendix) paint a fairly uniform picture. Across different models and on two different tasks, most VLMs seem to perform relatively similarly in terms of shape bias. Shape bias of VLMs is still significantly lower than in humans ($96\%$), but overall higher than in typical image-only discriminative classifiers (*e.g*., $22\%$ for an ImageNet-trained ResNet-50 [, ]). For most models, shape bias is higher than the shape bias of CLIP ViT-L/14 [] ($60\%$) which is a common vision encoder used in many of our tested models.

On average, we observe a slightly higher shape bias for the image captioning task at some cost in accuracy (mostly due to generic captions that do not mention any objects) and vice-versa.
For image description tasks, it can be significantly lower ($38.2\%$) for some outliers but generally ranges from $54.1$ - $73.2\%$.
For VQA it ranges from $52.9$ - $73.8\%$. In this task, we had to remove some models as they did not follow instructions. Some of these models are on the lower end of the shape bias in captioning, which raises the question of whether there might exist a correlation between instruction tuning quality and shape bias (to answer this question, more samples would be needed for a conclusive answer).

##### Which models are the most shape-biased?

The strongest shape bias is observed in InstructBLIP Vicuna-7B [] for VQA, but the model generally shows a lower accuracy compared to other models. A more accurate model is InternVL-Chat 1.1 [] which ranks second place for VQA but first for captioning.

##### GPT-4V is an outlier.

Given that GPT-4V often achieves SOTA performance and is considered an important baseline, it has a surprisingly poor accuracy compared to most other models - mostly due to refusal to answer: 131/1280 VQA conversations, i.e., roughly 10% which is substantially higher than the refusal rate of all other models ($<1\%$). Note that refusal rates do not affect the shape bias measurement. GPT-4V is also the model with the largest amount of generic image captions ($60.4\%$). We acknowledge that other prompts may have led to other results, however, the result is noteworthy, as the other VLMs mostly behave well under the same prompts. Interestingly, GPT-4V is also significantly more texture-biased than most models in both tasks.

##### Does scale matter?

LLM capacity does not seem to correlate with shape bias and unpredictably skews the shape bias by a few percent in each way as can be seen in Qwen-VL, LLaVA v1.5/NeXT/RLHF, or InternVL. Similarly, the overall largest models do not have the highest shape bias. However, scale usually improves accuracy.

##### Does RLHF align shape bias?

RLHF-tuned VLMs are still rare at this point and we only have three samples. On both LLaVA-RLHF [] models we see no changes in comparison to the default LLaVA models. GPT-4V [] (though it is unclear if vision was also RLHF trained) shows one of the lowest shape biases in our study, but we do not know how the base model ranks. Overall it is hard to derive a conclusive answer, but it seems that at least RLHF does not necessarily guarantee an alignment of visual preferences.

### 4.2 Examining Decision Formation

Figure: (a)
Refer to caption: x3.png

To better understand how decisions are formed we utilize a confidence measurement as described in Sec. 3.2. Exemplarily, we choose LLaVA-NeXT 7B [] to study the sampling of responses in VQA. The choice of this model is motivated by the fact that we can obtain access to the logits, in addition to the model’s ability to consistently follow the instructions as opposed to only responding with the option letter.

In Fig. 2(a), we visualize the confidence of the token corresponding to the shape or texture answer option. We were expecting some normal distribution that is slightly offset in favor of shape bias. However, to our surprise, we find that confidence in both options is almost binary. Analogously, when we only focus on correct answers (Fig. 2(b)), we observe that the model is highly confident in its responses. As the model assigns such a high confidence to the cue it answers with, this suggests that information from the other cue, in fact, gets lost in processing. This is also backed by the fact that the second (significantly less confident) prediction token often ($70.7\%$) does not belong to the conflicting cue. Only $17.7\%$ of the top-2 pairs contain both, shape and texture.

On average, we also observe that the model is slightly less confident in texture decisions ($\tilde{p}=0.87$ vs. $0.92$). This becomes even more evident when we only analyze responses above a confidence threshold (Fig. 2(c)). Up until $p=0.5$, this does not significantly influence any of the measurements. Beyond this point, the ratio of shape decisions increases without a decrease in texture decisions resulting in a higher shape bias. Beyond $p=0.9$ the ratios diverge, and the model reasons exclusively on shape cues.

It is not clear if these findings generalize to other models - *e.g*., GPT-4 was shown to be significantly less confident compared to its base model []. However, LLaVA is currently the building block for many successors [, ] and as such will strongly induce its bias into those. The next section also shows that many VLMs actually reason very similarly in our tasks hinting that our results may generalize after all.

### 4.3 The Role of the Vision Encoder

The texture/shape bias is primarily a vision bias - we ablate the vision encoder to understand how it drives the model decision. For this section, we additionally dissect inference from encoder pretraining.

##### Do VLMs just inherit the shape bias from their encoders?

Most VLMs combine a (frozen) CLIP vision tower with an LLM via some projector [, , ]. This raises the question of whether these VLMs simply learn to perform zero-shot classification using their encoders whenever the prompt requires some form of classification - thus, inheriting the shape bias from the encoder. To gain insights into this we measure the respective difference in shape bias (and accuracy).

We see that shape bias is indeed (strongly) influenced by the encoder, but the VLM can deviate in either direction ($-9.5\%$ to $+7.4\%$ in our examples in the appendix).
Further, all models decrease in accuracy compared to their encoders (all of which were frozen during expansion into VLMs). This is not surprising, as VLMs are less specialized, but it shows that VLMs do not simply pass through their encoder’s classifications and have some form of bias control.

As an additional signal, we measure the error consistency [] for insights into the alignment: a pair of models with high error consistency will share some traits in their internal decision-making process.
We observe that VLMs show a high but not perfect error consistency to their respective encoders (up to $79.8\%$). Generally, they are fairly similar to all zero-shot classifiers that we benchmarked (except CNN-based models; see below). This trend even prevails in models with different (MoE)-LLMs but the same encoder. In line with our previous results, this further emphasizes that shape bias in VLMs is influenced by the encoder, but their multimodal fusion with LLMs allows them to deviate beyond that.

##### Does multimodal training guarantee shape-biased encoders?

Next, we aim to understand a critical aspect of encoder training: the combination of vision and language.
It was already demonstrated that ViT-CLIP models show an increased shape bias in comparison to vision-only models independent of their architecture, training data, or method []. This may suggest that joint embedding alone increases shape bias. To verify this hypothesis, we measure the shape bias of a ResNet-50-based CLIP [] - and observe an opposite trend. With just $20.8\%$ (refer to the appendix for details), this model even slightly reduced shape bias compared to an ImageNet-trained ResNet-50 ($22.2\%$). As such, just fusing language into encoder training does not guarantee an increased shape bias and the results strongly depend on the vision architecture, as well.
For the design of *shape-biased* VLMs it is, thus, reasonable to rely on representations of ViT-based CLIP as opposed to vision-only models.

Figure: Figure 4: VLMs make similar errors on the cue-conflict datasets and share similarities with their vision encoders. In terms of errors, VLMs are also more similar to humans than ImageNet-trained/finetuned models. We measure the pair-wise error consistency [] between predictions. For this analysis, an error is any answer that does not belong to the shape class (analogous to []). Shown responses belong to LLM-based VLMs (under the VQA task), other selected models including ImageNet models, (some) VLM encoders under ImageNet-finetuning and zero-shot classification, and ten human subjects.
Refer to caption: x6.png

## 5 Steering Texture/Shape Bias

In the previous section, we have seen that in VLMs, visual biases are not simply inherited from the vision encoder, but the fusion with an LLM (including training) plays a crucial role.
We here seek to test how flexible the representation of texture/shape bias is and whether we can systematically steer it - first through visual steering or preprocessing (Sec. 5.1), and then through language or prompt engineering (Sec. 5.2). Since texture/shape bias is a visual bias, we generally expect more flexibility with vision steering. However, being able to steer a visual bias through language alone provides a powerful way of shaping visual biases in a user-specified way.

### 5.1 Steering in Vision

Figure: Figure 5: Image preprocessing can strongly steer texture/shape bias. Left: Shuffling image patches with decreasing patch size results in a strong texture bias, Right: Increasing Gaussian noise introduces a strong shape bias.
Refer to caption: x7.png

Earlier work demonstrated that ImageNet-models can still detect objects even if the image is split into patches and shuffled [, , ].
As patch size decreases, the operation is destroying more global shape information, yet retaining local texture information. While it is straightforward to assume that this also applies to VLMs and significantly reduces shape bias, we emphasize quantifying the shape bias precisely and understanding whether detection is still possible for these models, as they might be more intricate than specialized models.

Oppositely, to increase the shape bias we experiment with Gaussian noise processed inputs. This is loosely inspired by applying “diffusion-like noise” during training and inference which has been shown to drastically improve the shape bias of ImageNet-ResNets []. However, we only apply the noise during inference and use a more simplistic approach by adding $\mathcal{N}(0,\sigma^{2})$ noise to all channels, consecutively clamping values to $[0,1]$.

We show results on LLaVA-NeXT 7B in Fig. 5, also in comparison to its CLIP encoder under zero-shot classification, and finetuned on ImageNet. Adding noise results increases the VLMs shape bias up to $89.5\%$ at $\sigma^{2}=0.5$, and patch shuffling decreases shape bias (increases texture bias) to $8.4\%$ at $28\times 28$ patches. We see similar trends for the encoder, but the VLM overall achieves the highest shape bias. Inspired by these strong results, we repeat the experiments on the more shape-biased and larger InternVL-Chat 1.1 []. In this model, we can further extend the range $91.7\%$ shape bias ($\sigma^{2}=0.3$), and down to $6.1\%$ ($28\times 28$ patches). While extreme steering of shape bias comes at a cost in accuracy, all results are still well beyond random chance (min: 48.6% accuracy).

In conclusion, the shape bias can be strongly steered in the vision representation (including processing by the LLM) to almost both extremes—more shape-biased and more texture-biased.

### 5.2 Steering in Language: Prompt Engineering

Our previous results suggest that VLMs learn a connected multimodal understanding of shape and texture. This opens the question of whether visual biases can be influenced through text processing in these models. We test this hypothesis by recording texture/shape bias as a function steering them via text through prompt engineering.

##### Bias steering through hand-crafted prompting.

We start by asking VLMs to specifically identify either the “shape” or the “texture” category in a given cue-conflict image. This *hand-crafted biased prompting* approach does indeed steer shape bias to a certain degree: As shown in Fig. 6, prompting can steer a visual bias (*without* significantly affecting accuracy). Details on these biased prompts can be found in the appendix. Neutral prompts perform often similarly to shape-biased prompts, whereas texture-biased prompts deviate more significantly. This suggests that models may be more inclined to use shape by default, but also have access to a certain amount of texture information which can be accessed through biased prompting.

For two models (InternVL-Chat 1.1 [] and LLaVA-NeXT 7B []) we additionally replace the terms texture/shape by strong synonyms obtained from Thesaurus.com [, ]. Synonyms of either term can steer shape bias as well to a certain degree. For “texture” synonyms we observe more variance, as “texture” is overloaded by different meanings (*e.g*., some synonyms like “feeling”, “taste”, or “touch” are unrelated to texture in vision). In contrast, “shape” is a fairly well-defined term.

While the effect of steering by language is systematically visible, language steering alone does not fundamentally change the reliance on the underlying cue. This effect does not appear to be a limitation of LLM capacity: We performed an additional study on InternVL-Chat 1.2+ (34B vs. 13B) but did not obtain evidence that larger LLMs guarantee more steerability.

Figure: Figure 6: Prompts can steer the texture/shape bias to some extent. We test the same texture/shape-biased instructions on multiple models, showing that these can already shift some decisions (usually in favor of texture). For InternVL 1.1 and LLaVA-NeXT 7B we additionally test the understanding of texture/shape by using synonyms. Furthermore, we use an LLM to automatically search for specific prompts to optimize in either direction.
Refer to caption: x8.png

Interestingly, our findings of steering by language show parallels to a human psychophysical laboratory experiment. [] conducted control experiments in which humans were either asked to identify the shape while ignoring texture, or conversely to identify the texture while ignoring the shape. This “human prompt steering” worked, but only to a certain extent: When humans were tasked to ignore the shape, the human shape bias decreased from $96\%$ (neutral instruction) only to approx. $70\%$ shape bias (texture-biased instruction). Our models behave somewhat similarly: their visual shape bias can be steered through prompting, but it appears hard for them to completely go against their default visual bias.

##### Bias steering through automated prompt engineering.

Above we observed that hand-crafted prompts can steer visual biases, but only to a limited extent. Does this indicate a limit on how much language/prompting can influence biases, or merely reflect that the handcrafted prompts were chosen suboptimally? To investigate this question, we tested an *automatically crafted prompt*. This is achieved by employing an LLM as optimizer [] to continuously generate new prompts in natural language targeting to maximize either shape or texture bias in a feedback loop. We provide the LLM feedback about the achieved accuracy and shape bias. Additionally, we opt for greedy token sampling in the VLM to reduce noise in the feedback loop. For further details, we refer the reader to the appendix.

The results are shown in Fig. 6 in gray and denoted as “search”. We observe that for both LLaVA-NeXT-7B and InternVL-Chat 1.1, automatically generated prompts exceed the manually crafted biased prompts in terms of their effectiveness to increase texture bias, and roughly match them when it comes to increasing shape bias. For InternVL-Chat 1.1 the delta between both extremes is $23.3\%$, which can only serve as a lower bound and is likely improvable by a better design of the LLM task (or using other optimizers). In line with hand-crafted prompts, overall accuracy does not change considerably, which is a key differentiator from the vision-steering we explored in the section above. We should also note that the optimization here is done with respect to the cue-conflict test set; this is simply done as a proof of concept to show that there are prompts which are able to substantially influence visual biases and not to claim a SOTA shape bias. It would be interesting to test whether the generated prompts generalize to other models or datasets.

Taken together, language-based steering clearly has a more reduced effect compared to visual steering. That said, unlike visual steering through image modifications which substantially hurts accuracy, our prompting strategy barely affects accuracy. Additionally, the method may be more intuitive for practitioners who can access or change the prompt as desired.

## 6 Conclusion

In this study, we investigated texture/shape bias in VLMs. Through the lens of this specific bias, we are able to assess whether visual biases are inherited from vision encoders or modified through text processing. We find that the latter is the case: VLMs are often more shape-biased than their vision-only backbones. Beyond that, an intriguing aspect of VLMs is that the task/response can be steered through not just vision but also through language. Our experiments have demonstrated that VLMs have learned a multimodal association between the terms “shape”/“texture”, their synonyms, and their respective visual concepts to some extent. Taken together, we see that when it comes to multimodal models, the sum is greater than the parts, as a visual bias (texture/shape bias) can be steered to a certain degree through both manual and automated prompt engineering, alone.

Similar to the active research line on prompt engineering for CLIP [, , , ], it remains to be seen if prompts exist that can steer texture/shape bias to the same extent as vision can. While this aligns with human behavior in psychophysical studies [], it remains unclear if this is a reflection of a learned bias from human-annotated data or a spurious correlation. We plan to explore this further in future studies.

Lastly, despite experiments on an extensive and diverse collection of VLMs, we have seen a (surprisingly) homogenous landscape in terms of texture/shape bias, steerability, and error consistency. We are curious to see how our insights apply under more radical training/architecture changes and to other visual biases. We encourage the community to provide more open-source implementations without which studies like ours would not be feasible.

##### Limitations.

Our study, like most studies, is not free of limitations. Although we utilized a diverse array of VLMs, there is a possibility that we overlooked significant outliers. Moreover, the applicability of our findings to future VLMs remains uncertain; however, we believe our results effectively reflect the current landscape. A potential bias introduced in our study stems from the prompts we employed. Different prompts could have yielded disparate results, and certain models might have performed differently, particularly in Visual Question Answering (VQA) tasks. While we mitigated this by utilizing simple, widely used prompts (refer to the appendix for an exploration of alternatives), the possibility of better choices remains open for future investigation.

## Appendix

## Appendix A Overview of VLMs

Here we provide an overview of all used models from the main paper.

| Qwen-VL-Chat [] | Adds vision capabilities to Qwen-7B []. We set a repetition penalty of $1.2$ for this model. |
| --- | --- |
| Qwen-VL Plus/Max [] | AliBabas’s proprietary larger variants of Qwen-VL-Chat. Access only via API. |
| CogAgent [] | A special model for interaction with graphical user interfaces (GUIs) at high-resolution. |
| CogVLM [] | Adds "trainable visual expert module" in LLM layers to combine vision and language. |
| Emu2 [] | The 37B model claims "strong multimodal in-context learning abilities". |
| InstructBLIP [] | Connects frozen vision encoders and LLMs through a trainable Q-Former. Uses Vicuna or FLAN-T5 as LLMs. |
| LLaVA v1.5 [] | Improvements of LLaVA with modifications on the image encoder, the projector, and task-specific data. Uses Vicuna-7/13B as LLM. |
| LLaVA-NeXT [] | Successor of LLaVA v1.5 supporting higher resolutions through patching, and using better SFT training data for training, claiming “improved reasoning, OCR, and world knowledge” []. The 34B version switches from Vicuna-7/13B to Nous Hermes 2 Yi 34B. |
| MoE-LLaVA v1.5 [] | Variants of LLaVA v1.5 employing 4 sparsely activated Mixture-of-Experts (MoE), and smaller LLMs (Qwen, Phi-2, StableLM). |
| LLaVA-RLHF [] | Variants of LLaVA v1.5 aligned with Factually Augmented RLHF (Fact-RLHF) []. |
| UForm-Gen Chat [] | A small (1.5B) model for VQA and image captioning finetuned for multimodal chat. |
| Gemini 1.0 Pro Vision [] | Google’s proprietary multi-modal model based on the Gemini Pro LLM. Access only via API. |
| InternVL Chat 1.1/1.2+ [] | An open-source effort to provide an alternative to ViT-22B []. V1.1 is based on a 6B ViT and Vicuna-13B, V1.2+ uses Nous Hermes 2 Yi 34B as LLM including additional SFT on 10x more data. |
| GPT-4V (Preview) [] | OpenAI’s proprietary multi-modal model based on the GPT-4 LLM. Access only via API. Often considered to be the most powerful model. |

For our main analysis, we prompt all models at the default generation parameters (*e.g*., temperature) unless stated otherwise. Appendix F shows why this is not an issue.

## Appendix B Detailed Results Table

Tab. 2 shows the shape bias and accuracy for the VQA and Image Captioning task (see Fig. 2 in the main paper for a visualization). For the open-ended Image Captioning responses, we additionally provide evaluations through an LLM (see Sec. 3). These include the number of generated tokens (to measure how effective our “Keep […] short.” instruction is), the ratio of responses where exactly one class was detected (single class ratio), and the ratio of responses that do not refer to any description (generic ratio).
In Appendix I/Tab. 9, we provide ablations on Image Captioning under the removal of generic responses.

**Table 2: The shape bias and respective accuracy on the cue-conflict dataset for various VLMs in VQA classification or image description tasks. For the image description task, we additionally provide the average number of tokens generated by Vicuna’s tokenizer and the ratio of responses that only contain a single class or are generic (do not mention any class) as judged by a separate LLM. “-” indicates models that did not follow instructions on VQA and could, thus, not be evaluated.**
|  | VQA | Image Captioning |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | Shape | Accu- | Shape | Accu- | Avg. | Single Class | Generic |
| Model | Bias [%] | racy [%] | Bias [%] | racy [%] | Tokens | Ratio [%] | Ratio [%] |
| Gemini 1.0 Pro Vision [] | 64.1 | 82.33 | 63.2 | 68.00 | 18.9 | 63.0 | 32.3 |
| GPT-4V (Preview) [] | 47.9 | 69.75 | 53.6 | 52.67 | 44.8 | 37.2 | 60.4 |
| Qwen-VL Plus [] | 64.8 | 82.92 | 67.9 | 65.50 | 21.9 | 59.2 | 36.0 |
| Qwen-VL Max [] | 62.4 | 85.50 | 69.7 | 68.50 | 151.9 | 52.1 | 41.0 |
| Qwen-VL Chat [] | - | - | 38.2 | 67.42 | 27.3 | 59.1 | 33.2 |
| InternVL Chat 1.1 [] | 68.3 | 89.33 | 73.2 | 75.58 | 16.9 | 74.9 | 19.4 |
| InternVL Chat 1.2+ [] | 61.1 | 90.83 | 61.3 | 82.42 | 15.8 | 80.4 | 11.4 |
| LLaVA v1.5 7B [] | 61.4 | 80.75 | 61.4 | 76.08 | 12.1 | 73.8 | 19.2 |
| LLaVA v1.5 13B [] | 64.1 | 80.25 | 62.7 | 75.58 | 28.9 | 65.8 | 23.8 |
| LLaVA-RLHF 7B [] | 61.7 | 68.08 | 63.0 | 71.83 | 47.9 | 65.1 | 24.7 |
| LLaVA-RLHF 13B [] | 63.4 | 80.42 | 62.3 | 73.25 | 38.3 | 64.7 | 27.7 |
| LLaVA-NeXT 7B [] | 59.2 | 82.58 | 64.0 | 65.08 | 20.2 | 55.5 | 39.5 |
| LLaVA-NeXT 13B [] | 57.2 | 83.42 | 63.5 | 65.25 | 48.8 | 52.6 | 40.9 |
| LLaVA-NeXT 34B [] | 56.0 | 73.83 | 66.2 | 57.50 | 93.4 | 36.2 | 59.1 |
| MoE-LLaVA-StableLM [] | 59.1 | 80.08 | 63.0 | 73.92 | 24.1 | 67.4 | 21.6 |
| MoE-LLaVA-Qwen [] | 62.9 | 59.50 | 63.2 | 75.33 | 13.3 | 69.4 | 20.7 |
| MoE-LLaVA-Phi2 [] | 59.6 | 82.33 | 61.1 | 75.42 | 34.9 | 67.0 | 18.6 |
| InstructBLIP Flan-T5-xl [] | 68.2 | 79.58 | 67.1 | 81.50 | 116.7 | 57.0 | 22.3 |
| InstructBLIP Vicuna-7B [] | 73.8 | 72.25 | 67.7 | 80.67 | 94.0 | 60.9 | 28.0 |
| Emu2-Chat [] | 52.9 | 75.08 | 59.6 | 65.00 | 13.6 | 63.0 | 34.0 |
| CogAgent Chat [] | - | - | 67.4 | 60.33 | 40.1 | 49.6 | 47.7 |
| CogVLM Chat [] | - | - | 57.6 | 66.58 | 35.8 | 53.2 | 40.1 |
| UForm Gen Chat [] | - | - | 38.8 | 64.50 | 30.2 | 59.3 | 33.0 |

## Appendix C Prompting

This section provides an overview of all the prompts we have used in our study, including fine-grained details and ablation studies on their effectiveness.

### C.1 Main Prompts

The prompt for VQA Classification is:

with a default setting VQA_INSTRUCTION="Which option best describes the image?".

We use the following prompt for the Image Captioning task: "Describe the image. Keep your response short."

### C.2 Exploration of Alternative Prompts

In initial testing, we found that the choice of prompts affects the eventual results and has the potential to inevitably influence our study. Thus, in an effort to address this, we extensively evaluated our models with multiple different prompting techniques, used in literature [, ] and chose the best one.
Our prompt for VQA is inspired by LLaVA’s prompts for multiple-choice questions (^1^11[https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md) [Online; accessed 6. Mar. 2024]). In an additional experiment (Tab. 3), we ablated alternative prompts on LLaVA-NeXT 7B []. We change or use an empty VQA_INSTRUCTION and change options to CLIP-style options ("X. a photo of a {class}"). However, we only observed a minor fluctuation in accuracy and shape bias and no significant effects. Our default prompt delivers the best accuracy and is, thus, our preferred choice.

**Table 3: Exploration of alternative VQA prompts.**
|  | Shape | Accu- |
| --- | --- | --- |
| Prompt | Bias [%] | racy [%] |
| "Which option best describes the image? [...]" (default) | 59.2 | 82.58 |
| Default with CLIP-style options | 59.5 | 81.92 |
| "Describe the object in the image: [...]" | 60.2 | 81.33 |
| "Describe the object in the image: [...]" |  |  |
| with CLIP-style options | 59.4 | 80.17 |
| Empty instruction (just options) | 59.5 | 81.33 |

Our image captioning prompt is a reformulation of the VQA prompt ("Which option best describes the image?" $\rightarrow$ "Describe the image."). In the following, we ablate if the suffix ("Keep your response short.") may have interfered with our results. Additionally, we tested an alternative suffix that explicitly asks for more details on LLaVA-NeXT 7B []. The results for the former investigation in Tab. 4, show that our suffix indeed did not heavily bias the results in terms of shape bias. While adding the suffix leads to an impact in accuracy, it reduces the ratio of generic descriptions (not referring to any class) and has on average almost 4x fewer tokens resulting in significantly faster inference. Switching the suffix to "Be precise." increases shape bias, but at the same time also increases the number of generated tokens and worryingly the ratio of generic responses. Overall, we find that captioning prompts are more fragile, but our chosen default prompt provides an intriguing balance. For all ablated prompts, we find that the shape bias is higher than in VQA.

**Table 4: Exploration of alternative Image Captioning prompts.**
|  | Shape | Accu- | Avg. | Generic |
| --- | --- | --- | --- | --- |
| Prompt | Bias [%] | racy [%] | Tokens | Ratio [%] |
| "Describe the image. Keep your response short." (default) | $64.0$ | $65.08$ | $55.5$ | $39.5$ |
| "Describe the image." | $63.6$ | $68.25$ | $202.9$ | $46.8$ |
| "Describe the image. Be precise." | $67.3$ | $64.50$ | $166.2$ | $50.6$ |

### C.3 Biased Prompts

##### Hand-crafted.

For our hand-crafted biased prompts, we set VQA_INSTRUCTION
="Identify the primary {BIASED_TERM} in the image.", with BIASED_
TERM="shape" and BIASED_TERM="texture", for shape-, and texture-biased
prompts, respectively.

##### Synonyms.

We have retrieved the following strong synonyms from Thesaurus [, ].

- •
shape: architecture, aspect, body, configuration, contour, format, frame, model, outline, pattern, shadow, silhouette
- •
texture: balance, character, composition, consistency, fabric, feeling, make-
up, nature, pattern, quality, sense, smoothness, structure, surface, taste, touch

Figure: Figure 7: Detailed shape bias measurements under synonyms for biased VQA prompts.
Refer to caption: x9.png

The prompt is formed by replacing BIASED_TERM in the biased VQA prompt with the corresponding synonym.
We did not filter out any synonyms but still want to emphasize that some of them are not reasonable in the context of vision (*e.g*., for texture) which explains why the variance between them can be high. In Fig. 7, we also show the detailed shape bias measurements for each synonym (complementary to Fig. 6 in the main paper). We find that “pattern” - a synonym for both terms - tends to be more correlated with texture.

## Appendix D LLM-based Response Extraction

We rely on an LLM to extract labels from the generated captions for the Image Captioning task. For every generated description we instruct Nous-Hermes-2-
Mixtral-8x7B-DPO [] with the following prompt:

Then we simply split the generated string into a list. We found this prompt by manually testing some examples and picking the best-performing one. For example, we experimented with other options to denote generic responses like “-”. However, we found that this increases hallucinations, presumably as “-” is often used to begin bullet points and, thus, causes the model to continue generation.

## Appendix E Automated Prompt Search

Loosely inspired by [], we utilized an LLM to optimize prompts. we switched to Mixtral-8x7B-Instruct-v0.1(^2^22[https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) [Online; accessed 6. Mar. 2024]), as it performed better than the Nous Hermes version in early tests. The results shown in Sec. 5.2/Fig. 6 are a summary of multiple prompts that we tried in numerous multi-round conversations. We instruct the model to provide a prompt in a new line starting with “PROMPT: ” that we then extract and automatically evaluate. Afterwards, we return the results to the LLM and ask it to generate the next prompt.

We have experimented with multiple prompts but ultimately our approaches can be loosely divided into prompts that try to maximize or minimize shape bias without significantly affecting accuracy. Besides linguistic tweaks, we experimented with the following techniques:

- 1.
Offering rewards: We offered tips to the LLM to encourage it to generate more and better results(^3^33[https://twitter.com/voooooogel/status/1730726744314069190](https://twitter.com/voooooogel/status/1730726744314069190) [Online; accessed 6. Mar. 2024]). However, Mixtral seems to be fine-tuned to refuse such attempts.
- 2.
Adding in-context examples: We added an example (in language) of what it means to be shape or texture-biased in classification. This often seemed to bias the model to generate prompts that contain the example, too.
- 3.
Summarizing previous attempts: We encouraged the LLM to summarize previous attempts before generating the next prompt, hoping to keep the most important aspects in context. The LLM did not always follow this suggestion.
- 4.
Returning the extracted prompt: The LLM sometimes did not start the prompt with the requested prefix or misplaced it. We mitigated this by including the extracted prompt in our responses.
- 5.
Encouragements in response: Initially, we only returned the accuracy and shape bias but found that the LLM sometimes abruptly quits the search. Thus we included encouragements in the form of questions like “What is your next prompt?”. This seemed to improve conversations in terms of length, but could not entirely prevent the LLM from quitting.
- 6.
Simple but creative prompts: When just instructed the LLM to generate prompts, we noticed that it would sometimes collapse to verbose prompts where it would attempt to rephrase terms by synonyms. Inspired by regularization terms in optimization, we ask the model to keep its prompt simple and creative to avoid minor tweaking in favor of more radical changes.

In all cases, we append a mock conversation (*i.e*., both roles are written by us) to the history containing the neutral prompt and the respective shape bias/accuracy. An example conversation is shown in Tab. 5.

**Table 5: Example conversation for LLM-guided Prompt Search. ${}^{*}$Mock conversation.**
| User${}^{*}$ | Your task is to help me design an ideal image classification prompt for a vision/language model to MINIMIZE the shape bias without significantly hurting accuracy. The provided photos show natural objects modified to contain shape and texture of conflicting object classes. E.g., it could be a photo of an elephant (shape) with dog fur (texture). The model should classify the image as ’dog’ based on the texture of the object and ignore the shape. The model’s accuracy is the percentage of correctly classified images. The shape bias is the ratio of how often the model classified based on shape over texture. You can test your prompt by outputting a single new line starting with ’PROMPT: ’. Do not list options - the system will provide them automatically. Try to keep the prompt as short and simple as possible but be creative. It might be reasonable to summarize insights of previous attempts and to outline your goals before responding with a new prompt, but make sure that only the prompt starts with ’PROMPT:’. In response to the prompt you will be told the accuracy and shape bias. Then you will refine the prompt and we will continue until I say stop. Let’s go! |
| --- | --- |
| Assistant${}^{*}$ | PROMPT: Which option best describes the image? |
| User${}^{*}$ | Prompt: […], Accuracy: 82.58 %, Shape Bias: 59.43 %. What is your next prompt? |
| Assistant | To minimize the shape bias while maintaining accuracy, let’s encourage the model to focus more on textures than shapes. Here’s my next prompt:<br>PROMPT: Based on the surface characteristics and patterns, which category does the image most resemble? |
| User | Prompt: […], Accuracy: 78.25 %, Shape Bias: 50.9 %. Can you improve this? |
| $\cdots$ |  |
| Assistant | I have provided various prompts and explored different approaches to minimize the shape bias while maintaining high accuracy. The current optimal prompt is: […]<br>Here are some additional suggestions for minimizing the shape bias: […]<br>These methods may require more computational resources and time investment. If you have other questions or need guidance on different topics, please don’t hesitate to reach out. Wishing you the best in your continued efforts!<br>At this point, I believe that we have explored various approaches, and the current prompt is the best-performing one among those tested. If you decide to pursue any of the mentioned alternatives, I am confident that you will find ways to improve the model’s performance and minimize the shape bias. Good luck with your continued work!<br>As our conversation has concluded, I would like to thank you for engaging in this exercise, and I hope I have been helpful in providing prompts and suggestions. If you have other questions or need guidance on different topics in the future, please don’t hesitate to reach out. Wishing you the best in your projects and research! |

Optimization with LLMs is a highly exciting but also very active research field, where best practices have not yet emerged. For example, we have noticed that our instruction sometimes caused the LLM to refuse to continue when it found that the search was exhausted, or caused the LLM to maximize shape bias despite the instruction to minimize it. Overall, this is not an issue for our study as we are merely interested in understanding if quantitatively more texture/shape-biased prompts exist. The prompt shown in Tab. 5 (first message) is the final iteration integrating all of the above techniques.

## Appendix F Ablation of Temperature Scaling

We are interested in determining if generation parameters can influence the behavior of shape bias. Generally, VLMs only expose a few controllable parameters but all offer some form of stochastic sampling of tokens, often via temperature scaling of the token logits. Most models default to low-temperature settings (or settle for a greedy token strategy) which is more correlated with precise answers and reasonable for VQA. On the contrary, higher temperatures are correlated with more creative outputs and eventually token gibberish at extreme values. In general, temperature scaling also results in better-calibrated models [].

Exemplarily, we study this on LLaVA-NeXT 7B for both VQA and Image Captioning. We repeat the non-greedy experiments 3 times for statistically meaningful results; however, we generally notice a marginal error between runs.
Our results in Fig. 8, show no correlation between temperature and shape bias. As expected, the accuracy (slightly) decreases, because less confident tokens mapping to correct predictions are now replaced by false predictions. Yet, this affects texture/shape information alike. This can easily be explained by our token sampling analysis in Sec. 4. On average, texture/shape options are fairly similarly confident and top-1 tokens have a very high confidence (in the VQA setting) which the temperature scaling barely affects.

On the one hand, this finding serves as important confirmation that our comparison of VLMs at default values (picked by the original authors) is reasonable as it does not interfere with the shape bias. On the other hand, this implies that users seeking more creative outputs can tune the temperature (and similarly other parameters that control stochastic token sampling) without changing the underlying reasoning paths for vision inputs.

Figure: Figure 8: Temperature scaling has no significant effect on shape bias neither under VQA (left) nor Image Captioning (right) tasks but starts to decrease accuracy at higher levels. Experiments performed on LLaVA-NeXT 7B with 3 seeds (except Temperature = 0 and Temperature = 1 of Image Captioning where we use a single seed).
Refer to caption: x10.png

## Appendix G Results on CLIP Models

In this section, we provide results for CLIP models that we referenced in the main paper. We provide results for different architectures under three different prompting strategies: a computation of zero-shot centroids from 80 different prompts including usage of the class name [], "a photo of {class}." which is often used as a default prompt (note the dot), and "{class}" (without dot). We will argue that the latter is more comparable to the VQA task of our VLMs - but of course, VLMs may have a better representation in weights. Either way, the shape bias does not significantly deviate between the three strategies.
Tab. 6 shows the obtained shape bias (and accuracy) measurements.

We also noticed that the observed scaling laws in [] do not always hold for vision encoders, despite an increase in parameters from EVA02-CLIP-E/14+ (5B) to EVA02-CLIP-8B, we actually see a significant decrease in shape bias (but an improvement in accuracy).

Our results also contain (rather uncommon) ResNet-based CLIP models. Note these are the only models, where the 80 prompts significantly improve accuracy. In terms of shape bias, ResNet-based CLIPs significantly underperform any ViT or ViT-based CLIP.

**Table 6: Zero-shot classification on cue-conflict with different CLIP(-like) joint embedding models.**
| Model | Prompt | Shape Bias [%] | Accuracy [%] |
| --- | --- | --- | --- |
| EVA01-CLIP-g/14 [] | 80 Prompts [] | $66.03$ | $87.83$ |
| EVA01-CLIP-g/14 [] | "a photo of a {class}." | $66.03$ | $87.08$ |
| EVA01-CLIP-g/14 [] | "{class}" | $66.44$ | $86.67$ |
| EVA02-CLIP-8B@448px [] | 80 Prompts [] | $58.26$ | $91.83$ |
| EVA02-CLIP-8B@448px [] | "a photo of a {class}." | $57.58$ | $89.00$ |
| EVA02-CLIP-8B@448px [] | "{class}" | $56.60$ | $88.33$ |
| EVA02-CLIP-E/14+ [] | 80 Prompts [] | $65.62$ | $90.67$ |
| EVA02-CLIP-E/14+ [] | "a photo of a {class}." | $64.44$ | $89.75$ |
| EVA02-CLIP-E/14+ [] | "{class}" | $62.48$ | $86.17$ |
| CLIP-ViT-L/14 [] | 80 Prompts [] | $60.95$ | $84.08$ |
| CLIP-ViT-L/14 [] | "a photo of a {class}." | $60.20$ | $84.17$ |
| CLIP-ViT-L/14 [] | "{class}" | $60.16$ | $81.17$ |
| CLIP-ViT-L/14@336px [] | 80 Prompts [] | $61.52$ | $86.83$ |
| CLIP-ViT-L/14@336px [] | "a photo of a {class}." | $60.56$ | $86.42$ |
| CLIP-ViT-L/14@336px [] | "{class}" | $59.80$ | $83.75$ |
| CLIP-ResNet-50 [] | 80 Prompts [] | $19.70$ | $77.83$ |
| CLIP-ResNet-50 [] | "a photo of a {class}." | $20.96$ | $72.75$ |
| CLIP-ResNet-50 [] | "{class}" | $20.77$ | $71.83$ |
| CLIP-ResNet-101 [] | 80 Prompts [] | $25.50$ | $74.83$ |
| CLIP-ResNet-101 [] | "a photo of a {class}." | $25.23$ | $71.00$ |
| CLIP-ResNet-101 [] | "{class}" | $25.41$ | $70.83$ |

Tab. 7 shows the delta between shape bias and accuracy for some selected VLMs and their vision encoders under zero-shot classification (using "{class}" as prompt). All selected models rely on frozen vision encoder during training. As discussed in Sec. 4.3, the VLM accuracy decreases in all cases, but shape bias can deviate in either direction.

**Table 7: Quantitative comparison between shape bias and accuracy of VLMs (VQA) and their respective vision encoders (CLIP zero-shot classification) on cue-conflict.**
|  |  | VLM - Encoder [%] |  |
| --- | --- | --- | --- |
| VLM | Vision Encoder | Accuracy | Shape-Bias |
| LLaVA v1.5 7B [] | ❄ CLIP ViT-L/14@336px[] | $-3.50$ | $4.2$ |
| LLaVA v1.5 13B [] | $-3.00$ | $1.5$ |  |
| LLaVA-NeXT 7B [] | $-9.92$ | $-3.9$ |  |
| LLaVA-NeXT 13B [] | $-0.33$ | $-2.7$ |  |
| LLaVA-NeXT 34B [] | $-1.08$ | $-0.2$ |  |
| MoE-LLaVA v1.5 Phi2 x4 [] | $-1.42$ | $-0.3$ |  |
| MoE-LLaVA v1.5 Qwen x4 [] | $-24.25$ | $3.0$ |  |
| MoE-LLaVA v1.5 StableLM x4 [] | $-3.67$ | $-0.8$ |  |
| InstructBLIP FLAN-T5-XL [] | ❄ EVA-01-CLIP ViT-g/14@224px [] | $-6.83$ | $1.8$ |
| InstructBLIP Vicuna-7B [] | $-14.42$ | $7.4$ |  |
| Emu2-Chat [] | ❄ EVA-02-CLIP-E/14+@448px [] | $-11.08$ | $-9.5$ |

## Appendix H Results on ImageNet-trained Models

Complementary to the results on CLIP, we also provide some shape bias evaluations of ImageNet-trained/finetuned models in Tab. 8. Note how ViTs are much more shape-biased than ResNets, as shown in [], and yet, after ImageNet-finetuning, the previously well-performing CLIP (ViT-L/14@336px) model drops from 59.80 % to just 32.1 % of shape bias.

**Table 8: Classification on cue-conflict with ImageNet-trained/finetuned models.**
| Model | Shape Bias [%] | Accuracy [%] |
| --- | --- | --- |
| ResNet-50 [] | 22.3 | 67.33 |
| ResNet-50 (timm) [] | 23.1 | 65.42 |
| ResNet-152 (timm) [] | 28.6 | 65.83 |
| ViT-B/16 (ImageNet-21k pretraining) [] | 45.4 | 63.67 |
| ImageNet-finetuned CLIP (ViT-L/14@336px) [] | 32.1 | 82.75 |

## Appendix I Additional Thoughts on the Image Captioning Task

##### Did our choice of embedding model bias the results?

While we assume that most embedding models will provide similar classification performance if the description clearly mentions one class, it is unclear how the classification is biased if the description refers to multiple classes, invalid classes, or is generic. Thus, we additionally ablate results with SFR-Embedding [] which at the time of writing was the overall SOTA English embedding model on the Massive Text Embedding Benchmark (MTEB) []. While the accuracy improved by a negligible amount, shape bias results were largely unaffected. Thus, we settled for the faster ember-v1 models.

##### What happens if the description mentions multiple classes?

Based on our LLM analysis, we notice that in the majority of cases (min: $79.3\%$, mean: $92.2\%$, median: $93.0\%$, max: $97.6\%$; minimum is given by InstructBLIP Flan-T5-xl []), descriptions do not refer to multiple labels, thus a potential bias of the embedding model is negligible for our analysis.

##### What happens if the description is generic?

According to our LLM analysis, many generated descriptions do not refer to any object class (min: $11.4\%$, mean: $31.9\%$, median: $32.3\%$, max: $60.4\%$) - in stark contrast to VQA responses. However, we also notice that the embedding accuracy is above random choice in these cases. This suggests that the LLM may have missed objects and slightly overreported the ratio.

In the cases where the caption is indeed generic, our choice of embedding model may have biased our study. However, the previously mentioned embedding with SFR-Embedding [] showed similar trends. Thus, we assume that most other SOTA embedding models would behave similarly - yet, we are excited how future embedding models will embed these cases.

Another option is to remove generic responses from the analysis. We observe that this typically increases shape bias (and accuracy) - naturally more notably in cases where the generic ratio was high (Tab. 9). *E.g*., for the extreme case of GPT-4V [], shape bias increases by $9.3\%$ and accuracy by $40.47\%$ (!). This preprocessing also seems to restore scaling laws to a large extent: larger models achieve higher shape bias and accuracy. One outlier to this trend is InternVL Chat v1.2+ []. It may be intriguing to replace the reported results in Sec. 4/Fig. 2 with the analysis on non-generic responses, but we avoid doing so, as this would a) remove a significant portion of results; b) lead to poorly comparable results obtained on different subsets.

**Table 9: Comparison of shape bias and accuracy for the Image Captioning tasks for all responses and only responses which an LLM did not classify as generic.**
|  | All responses | Non-generic |  |  |
| --- | --- | --- | --- | --- |
| Model | Shape Bias [%] | Accuracy [%] | Shape Bias [%] | Accuracy [%] |
| Gemini 1.0 Pro Vision | 63.2 | 68.00 | 65.7 | 88.40 |
| GPT-4V (Preview) | 53.6 | 52.67 | 62.9 | 93.14 |
| Qwen-VL Plus | 67.9 | 65.50 | 71.9 | 88.56 |
| Qwen-VL Max | 69.7 | 68.50 | 72.1 | 91.52 |
| Qwen-VL Chat | 38.2 | 67.42 | 40.1 | 83.92 |
| InternVL Chat 1.1 | 73.2 | 75.58 | 74.5 | 87.89 |
| InternVL Chat 1.2+ | 61.3 | 82.42 | 62.3 | 88.15 |
| LLaVA v1.5 7B | 61.4 | 76.08 | 62.8 | 87.24 |
| LLaVA v1.5 13B | 62.7 | 75.58 | 65.1 | 88.24 |
| LLaVA-RLHF 7B | 63.0 | 71.83 | 64.7 | 83.80 |
| LLaVA-RLHF 13B | 62.3 | 73.25 | 66.3 | 86.08 |
| LLaVA-NeXT 7B | 64.0 | 65.08 | 66.9 | 92.48 |
| LLaVA-NeXT 13B | 63.5 | 65.25 | 65.3 | 92.95 |
| LLaVA-NeXT 34B | 66.2 | 57.50 | 73.6 | 96.39 |
| MoE-LLaVA-StableLM | 63.0 | 73.92 | 64.1 | 86.28 |
| MoE-LLaVA-Qwen | 63.2 | 75.33 | 64.4 | 88.03 |
| MoE-LLaVA-Phi2 | 61.1 | 75.42 | 63.1 | 86.05 |
| InstructBLIP Flan-T5-xl | 67.1 | 81.50 | 68.7 | 89.09 |
| InstructBLIP Vicuna-7B | 67.7 | 80.67 | 68.4 | 90.27 |
| Emu2-Chat | 59.6 | 65.00 | 60.3 | 89.94 |
| CogAgent Chat | 67.4 | 60.33 | 70.8 | 97.38 |
| CogVLM Chat | 57.6 | 66.58 | 61.9 | 93.72 |
| UForm Gen Chat | 38.8 | 64.50 | 37.9 | 83.00 |

## Appendix J Class-wise Texture/Shape Bias

Following the original shape bias study [], we include plots that show the class-specific texture/shape bias for all our models in VQA and Image Captioning (Fig. 9).

Figure: (a) VQA
Refer to caption: x11.png

## Appendix K Responsibility to Human Subjects

We did not collect any human data ourselves. Instead, the human shape bias data is taken from [] who collected the data in a controlled psychophysical laboratory (Wichmann-lab in Tübingen, Germany). Participants provided written informed consent and were compensated above minimum wage. The data was open-sourced by the authors on github without personally identifiable information. The experiment was conducted in accordance with institutional guidelines for human subjects research. For further information on the human experiment, we refer the reader to [].

## Appendix L Visualization of Vision Steering

We visualize the effects of vision steering (Sec. 5.1 in the main paper) in Fig. 10.

Figure: Figure 10: Steering by Vision. For one example image, we show how patch shuffling (top) increases texture bias by destroying shape information. Below we show how adding Gaussian noise increases shape bias by destroying texture information. Please note that we show more extreme values than those used in our experiments for visualization purposes.
Refer to caption: x13.png

## Peer Reviews

confidence: 4
contribution: 4
initial_rating: 6
presentation: 4
questions: - The results of steering the texture/shape bias via language prompts are interesting. The reviewer observes that steering towards a texture bias can lead to completely different model behaviors for different models. For example, the performance of Gemini increases by 1%, while LLaVA-NeXT-7B decreases by 2%. However, in the study of prioritizing shapes over texture cues in VLMs, LLaVA-NeXT-7B actually shows a stronger texture bias than Gemini. Do the authors have any comments or findings in this direction?
soundness: 4
strengths: - The paper is well-written with extensive analysis.

- The motivation is clear, and the problem is well-defined. It’s a pleasure to read.

- A comprehensive study is included, with inspiring and interesting findings.
summary: This paper studies vision-only biases in texture vs. shape and the preference for local over global information in recent vision-language models (VLMs). The paper reveals that VLMs partially preserve the shape bias from their vision encoders, though they do not reach human levels of shape reliance. The vision encoder produces a biased representation containing both texture and shape cues, which the LLM often suppresses, leading to recognition based on either shape or texture alone. At the last part, this paper shows that VLMs enable steering of visual biases through language prompt, allowing shape bias to be adjusted significantly.
weaknesses: - Class-wise analysis: For VQA classification, where tasks involve multiple classes, a class-wise analysis to examine texture and shape bias for each class would be helpful. Questions to explore include whether this texture and shape bias is consistent across all classes and whether there are differences between classes with simple vs. complex shapes.

- Additional qualitative results and examples: This paper provides quantitative results across various aspects; however, it lacks qualitative results and examples, which would help clarify the concepts through visual examples. For instance, the reviewer suggests including examples from the target dataset with texture and shape splits. Additionally, presenting qualitative examples of both correct and incorrect predictions could further enhance the paper’s clarity and impact.

---

confidence: 4
contribution: 3
initial_rating: 8
presentation: 3
questions: - Have you considered extending the study to include other visual biases such as color or spatial attention for a broader understanding of VLM behavior?
- Can you share additional examples or analyses of failure cases where the model showed unexpected behavior or limited steering?
soundness: 3
strengths: - Introduces a new investigation into steering visual biases in VLMs using natural language prompts, extending beyond traditional uni-modal bias studies.
- Comprehensive experiments using the texture/shape cue-conflict dataset validate findings and provide robust evidence for claims.
- Clear organization with effective figures and tables to illustrate results, making complex concepts more understandable.
- Demonstrates that visual biases can be influenced through prompts without retraining, offering practical implications for user-aligned AI outputs.
- Opens pathways for further research on user-driven customization and bias control in multi-modal AI, enhancing model adaptability and transparency.
summary: - The paper investigates how visual biases in vision-language models (VLMs), specifically the shape vs. texture bias, are affected by integrating a large language model (LLM) with a vision encoder.
- It highlights that VLMs inherently inherit some biases from their vision encoders but exhibit unique behaviors when processing visual cues, influenced by the multi-modal fusion of vision and language.
- The study shows that VLMs tend to favor shape-based recognition over texture, unlike traditional vision-only models, but do not match the high shape bias seen in human perception.
- The authors demonstrate that these biases can be steered using natural language prompts without retraining, allowing adjustments in model behavior to prioritize shape or texture-based decisions.
- The research provides insight into the potential of language prompting to align model outputs with user preferences, showcasing a new, efficient method to adjust visual biases in multi-modal models.
weaknesses: - Focus on shape and texture biases limits the study; exploring other biases (e.g., color, spatial attention) would give a fuller understanding of VLM behavior.
- Limited discussion on prompt selection; more detail on prompt strategies and robustness would enhance the study's depth.
- Prompt-based bias steering is constrained; deeper analysis on why some models are less responsive and how to improve steerability is needed.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 4
questions: - What is the base performance of the original image encoder in Table 2?
soundness: 4
strengths: - This paper is well-written and well-organized.
- Extensive study to study textual and shape biases in VLMs.
- Also, this paper presents a steering prompt that may change the bias.
- This paper supplements detailed analysis and implementation details.
summary: This paper shows that VLMs still preserve the texture and shape biases of image encoders, however, VLMs are more biased in shape rather than texture.
It also shows the results compared to the image encoder including ReSNet-50, and CLIP.
Also, this paper presents the methodology for controlling those biases through steering prompts.
weaknesses: - In section 5.2, spectral biases or critical bands that are difficult to control in language, I ask for more of the authors' opinions on why the language prompt influenced this bias and what it means when the performance decreases even if the bias value changes.

- According to the results of Figure 4, steering can slightly control textual/shape biases, but it seems to have a slight impact on overall performance, so what is the point of controlling these biases?

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: Please see above for questions. The importance would be W1 --> W3 --> W4 --> W2. Overall, better clarifying the significance of this study would improve my scoring.
soundness: 3
strengths: * S1: Overall, I think the paper is written clearly and highlights several key contributions.

* S2: The authors have conducted experiments on a wide range of VLMs and reach the interesting conclusion that -- VLMs influences the shape texture biases compared with their upstream vision-only encoders. I think the analysis is thorough and detailed in this part, covering multiple scenarios (VQA and image captioning), diverse models, and several comparative experiments (e.g., in Table 1). I am not an expert in explainability research, but this paper is the first to study VLMs' shape-texture biases to the best of my knowledge.

* S3: The authors demonstrate the stability of VLMs' biases towards either shape or texture (Figure 3) and how to steer their behaviors (Figure 4 and Figure 5). Both manual prompt tuning and automatic LLM optimization can achieve such steering. According to the authors' claim, this might be potentially useful.

* S4: The analysis and experiments in this paper are detailed and comprehensive.
summary: This paper investigates the shape-texture biases in recent vision-language models (VLMs). The author discovered that training with LLMs and vision-language tasks change the shape-texture biases, using the tasks of VQA and image captioning. However, the shape-texture bias is still influenced by the upstream vision-only encoders (e.g., CLIP). Based on such observations, the authors proposed both manual prompts and LLM-generated prompts to elicit the models' biases toward either shape or texture without significantly degrading the accuracy.
weaknesses: * W1: After reading the paper, it is still unclear to me **what the significance of studying shape and texture biases is**, primarily when MLLMs can already address more complicated tasks than object recognition nowadays. This concern limits the significance of this paper. Perhaps this point sounds biased, but I wish the authors had provided more arguments or examples of how studying shape-texture biases benefits the development or applications of VLMs. Such contexts would help the readers not to focus specifically on shape-texture biases.

* W2: The conclusions mentioned in the paper did not seem explicit from the quantitative results. For example, Table 1 does not show conclusive observations. If the goal of this table is to show that VLMs can behave very differently than their original visual encoder, this does not seem to be a surprising discovery since VLMs are trained on different datasets with different recipes compared with the original vision encoder.

* W3: More clarifications are needed on the steering part, where I have similar concerns with W1 -- the authors might want to clarify the usefulness of the potential contribution of steering such biases. (1) The claim in L448-449, "overall accuracy does not change considerably," might indicate that shape-texture bias is not a significant factor for accurate models. (2) With the recent progress in prompt engineering, the authors might want to explain better "why prompts change the model behaviors," which is non-trivial.

* W4: With this paper focusing on analysis, a crucial yet missing analysis is to rule out the biases from LLMs. For example, if the prompt mentioned in the paper contains the words of "textures" or "shapes," will the shape/texture biases observed related to the prior knowledge in the LLMs. It would strengthen the paper if authors can assist the analysis with some attention maps or other preferred measure to show that the **visual representation indeed changes** under such shape/texture steering.

Meta-review: Paper presents an approach that studies shape vs. texture bias in VLM models. Further, it studies steering of visual biases through language prompts, allowing shape bias to be dynamically adjusted. The paper was reviewed by four expert reviewers and received: 2 x accept, good paper and 2 x marginally above the acceptance threshold ratings. All reviewers acknowledge that the paper is well written, has clear motivation, and the analysis comprehensive. Main concerns and comments focused on (1) additional analysis, (2) additional qualitative results, (3) narrow scope of analysis (which excludes attention, etc.), (4) limited discussion on prompt selection, and (5) exposition and motivation with respect to prompt steering. Authors have provided a rebuttal that has addressed most reviewer comments with [VmoU] and [1KNs] rasing their scores. Overall, there appears to be consensus among the reviewers that this a solid paper with interesting insights and relatively through experimentation (with some additional results provided in the rebuttal). 

AC has read the reviews, rebuttal, discussion that followed and the paper itself. AC agrees with consensus of the reviewers and is recommending Acceptance. Authors are encouraged to incorporate rebuttal responses and experiments either into the main paper or into the supplementals.

Decision: Accept (Poster)