What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models
Authors: Keyon Vafa
Sarah Bentley
Jon Kleinberg
Sendhil Mullainathan
Venue: NeurIPS 2025 poster ()
Keywords: evaluation
human-in-the-loop
steerability
generative models
Primary area: evaluation
TL;DR: We introduce a mathematical framework and benchmark to quantify generative-model steerability, reveal large steerability gaps in LLMs and text-to-image models, and show that simple mechanisms can improve steerability.

Abstract: How should we evaluate the quality of generative models? Many existing metrics focus on a model's producibility, i.e. the quality and breadth of outputs it can generate. However, the actual value from using a generative model stems not just from what it can produce but whether a user with a specific goal can produce an output that satisfies that goal. We refer to this property as steerability. In this paper, we first introduce a mathematical decomposition for quantifying steerability independently from producibility. Steerability is more challenging to evaluate than producibility because it requires knowing a user's goals. We address this issue by creating a benchmark task that relies on one key idea: sample an output from a generative model and ask users to reproduce it. We implement this benchmark in user studies of text-to-image and large language models. Despite the ability of these models to produce high-quality outputs, they all perform poorly on steerability. These results suggest that we need to focus on improving the steerability of generative models.  We show such improvements are indeed possible: simple image-based steering mechanisms achieve more than 2x improvement on this benchmark.

What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models
Keyon Vafa Harvard University
Sarah Bentley MIT
Jon Kleinberg Cornell University
Sendhil Mullainathan
MIT
Abstract
How should evaluate the quality of generative models? Many existing metrics focus on a model's producibility, i.c. the quality and breadth of outputs it can generate. However, the actual value from using generative model stems not just from what can produce but whether a user with a specific goal can produce output that satisfies that goal. We refer to this property steerability. In this paper, we first introduce a mathematical decomposition for quantifying steerability independently from producibility. Steerability is more challenging to evaluate than producibility because it requires knowing a user's goals. We address this issue by creating benchmark task that relies a generative model and ask users to reproduce it. We implement this benchmark user studies of -i-ag and large language models. Despite the ability of these models to produce high-quality outputs, they all perform poorly on stecrability. These results suggest that we need focus on improving the steerability of genera- tive models. We show such improvements are indeed possible: simple image-based steering mechanisms achieve more than 2x improvement on this benchmark.
key idea: sample output from
Introduction
There is wedge between how we evaluate generative models and how we intend use them. For example, a common way to evaluate image generation models is to measure the quality of outputs they can generate, c.g. by measuring how realistic diverse the images they produce [70, 32, 46]. But when these models are used by people, their success depends on more than just what they 'r capable of producing: can users create the images they actually want?
In this paper, we introduce methods for quantifying model steerability: how well can a user with specific end goal guide model achieve that goal? Evaluating and benchmarking steerability is necessary for improving the real-world performance of generative models, in the same way that producibility benchmarks have been crucial for improving the quality of their outputs [19]. But creating a benchmark for steerability is challenging for two reasons. First, existing methods for evaluating models under human use [48, 39, 81] blend stecrability and producibility: if model doesn't i produce the image user wants, is it because it's incapable of producing it or because it can't be steered? Second, measuring steerability requires knowing human user's goals, which are often latent or difficult to articulate.
We first provide mathematical framework for modeling steerability and producibility. The key insight of this framework is to decompose model performance into two terms: a producibility term (how well can models produce the kinds of outputs human may want), and steerability term (how well can models be steered by humans towards the best output they' re capable of producing).
We then propose procedure that resolves the core challenges of benchmarking steerability. The procedure: sample an output from a model and instruct users to steer the model toward that output. This procedure is straightforward and can be applied to variety of generative models. For example, for
39th Conference on Neural Information Processing Systems (NeurIPS 2025).

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Figure 1: A goal image and attempts by users to steer toward it. To ensure the goal image is producible, it is sampled from the same model used for steering (here, DALL-EE Bolding added to prompts for emphasis. See Figure 10 for the full prompts.
text-to-image models, it calls for showing humans an image sampled from a model and then instructing them prompt that same model reproduce the image. This procedure addresses the two key challenges: first, as suggested by the framework, it evaluates generative model by how well humans can steer towards an output is capable of producing. Second, to overcome the lack of access to a user's s goal, it induces goal by instructing users to get as close to as possible to a provided output.
We implement this benchmark in large-scale large language models. Across the board, we find that the steerability of models is poor, both for normal participants and professional prompt engineers. For the image models, human annotators rate the attempted reproductions as dissatisfactory 60% of the time. Moreover, attempts to refine prompts do reliably improve outputs; after five tries, the final image is closer the goal image only 62% of the time (compared to a base rate of 50%).
study for two domains: text-to-image models and
Like many metrics, it is easy to optimize for stecrability alone. However, this could come at the expense of producibility. To assess whether differences between models reflect true steerability improvements, provide another mathematical decomposition that disentangles gains from better steering mechanisms and gains from producibility differences. This decomposition requires a predictive model of steerability; methods, and use it to analyze models, finding that steerability differences reflect both true improvements in steering mechanisms along with differences in producibility.
show that steerability can be predicted with machine learning
Finally, we show that the problem of poor steerability is addressable. We consider simple steering mechanism that uses two ingredients: First, after initial text prompt, enable users steer via suggested images rather than by prompt rewrites. Second, rather than suggesting images randomly, we use auxiliary model to suggest images. Despite the simplicity of this technique, it achieves more than 2x improvement over text steering.
Framework
We define generative model m over a domain X to have two components: producible set Sm S X and a steering mechanism that allows humans to produce an instance Sm- For example, the producible set of text-to-image model is all the images the model can generate, and its steering mechanism is the text interface that humans use to guide generations."
How do humans interact with the generative model? We assume that human is trying to generate an instance to satisfy a specific use case. For example, someone using an image generation model may be looking for a certain kind of image, and would try to prompt the model to produce such an image. To model a specific user's use case, we define family of reward functions R where each E R is function over instances X - R. Each use case is defined by single reward function.
We can also consider distributions over producible instances rather than single producible set without loss of generality; notation here for simplicity but will also consider distributions in Section

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A human interacts with generative model with the goal of producing an output that maximizes their reward. We define h(m,r) E Sm as the instance that is produced when a human with reward function r interacts with the model m. We refer to h as the steering f function; it describes how someone with a specific reward function would steer model to maximize it." This notation abstracts away details about how interactions are constructed (e.g. the number of attempts users have to prompt a model). While we exclude these details in our notation, our experiments will explicitly consider different settings,
A human's 's reward when using a generative model is r(h(m,)) This quantity describes how effective a generative model is under human interaction. However, blends model's steerability with the quality of its producible set Sm- To see this, we introduce additional notation to decompose the reward. For set of instances A, define r as the maximum reward when constrained to instances in A:
max r(x). xEA
Denoting by r'x the maximum reward over all possible instances, the largest possible reward for given reward function is r- A model's efficacy can be summarized by the gap between this largest possible reward and the model's 'ss reward: r - -r) This gap can be decomposed:
producibility gap
stcerability gap
x r(((m,)))) 1x
+ S...
r(((m.)))
The first term, rx -..," is the producibility gap; it captures how well the producible set of a model aligns with the set of all possible instances X. regardless of how steerable the model is. Meanwhile, the second term is the steerability gap; it describes how well a model can be steered by humans towards the best instance the model is cupable of producing.
The decomposition in Equation 2 describes a single reward function; in practice, model's total reward will be averaged over all reward functions that humans may have. Thus, the producibility gap can equivalently be characterized by considering only the set of instances T that humans may want to produce, i.e. those that maximize a feasible reward function (so that rxx = r for all r). This decomposition reveals a couple of results. The first that with perfect steering, model's 's performance can simply be evaluated by comparing the producible set Sm to the set of instances people may want to produce, T. However, with imperfect steering, model can in principle be capable of producing all instances humans may want (i.e. Sm = 7), and yet produce images h(m,)) with arbitrarily bad reward when steered by a human. Thus, both terms are needed to benchmark model quality under human use.
Many existing MI. benchmarks (e.g. image evaluation metrics like Inception Score [70] and Fréchet Inception Distance [32]) only measure producibility. This is partially due to the challenge of measuring stecrability, which relies on human reward functions that may be unknown. Recent benchmarks have been proposed for evaluating the success of human-Al interactions [48, 39, 81]. While important for understanding overall efficacy, these benchmarks combine producibility and steerability into single metric. Decomposing these effects important for improving models along each dimension, which are optimized with different pipelines; e.g. producibility may be improved through model architecture and training data, while steerability may be enhanced through new interfaces and alignment techniques.
Benchmark task. In this paper, we propose and implement a benchmark task for measuring a model's steerability independently of its producibility. This task is revealed in the framework above: measure how well humans can steer model toward the highest-reward instances it is capable of producing.
Evaluating model's steerability is challenging because we often don't 't have access to human reward functions. Even if we did have access to reward functions, evaluating the gap by finding the instance in model's 's producible set Sm that maximizes the reward function may be intractable. Here, we describe a simple procedure to induce reward functions that circumvents these challenges. For a given model m. first sample instance Xg P from a distribution P over the model's 's' producible set Sm- We refer to Xg as the goal instance. To induce a reward function, we show a human the goal instance xg and instruct them to use the generative model reproduce it. Specifically, we instruct them to generate an instance as close as possible to the goal instance Xg. as judged by other humans. In this sense, we're inducing an ideal point reward function [20]:
r (x) = xx(xx
In practice this function could differ between humans, even if they have the same reward function. For nota- tional simplicity report single function h. which can be interpreted as the aggregated function over humans.
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Similarity to goal Image
Perfect simiarity
Perfect similarity
All attempts
First attempt
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Figure 2: Human ratings (0-10 scale) measuring the similarity between human-steered text-to-image model outputs and their corresponding goal images. Left: For each model, the average similarity of all user gen-geeeeated images to their goal images. Right: For each model, the average similarity of users' first and last attempted generated images to their goal images. Bars represent single standard errors.
where .) )) E R$ is the distance function as judged by humans (with the property that d(x,xx) = 0). Therefore, for given goal instance Xg- the steerability gap is
S. r r)) min dxxg.xx') + d(xg. h(m,r)) = d(xg, ((m..)h(mr)))) r)), X'ESm.
where the equality is due to the fact that the goal instance Xg E Sm- Finally, to evaluate the steerability gap, we ask separate human annotators to rate the similarity of xg and h(m,r). r). We repeat this process across goal instances and human users to form an aggregate steerability measure for the model m.
This task is straightforward and can be applied to any domain where there's a natural notion of human similarity. For example, to implement this benchmark for text-to-image models, we first sample an image from the model's producible set. We then show this image to a human, and instruct them to prompt the model to produce the image (see Figure 1). We then take the image they produce and ask other humans to rate how close the two images are.
3 Benchmarking steerability
We now evaluate the steerability of image-genen generation models using the task described in Section 2. In a large-scale user study, we find that model steerability is poor, both for survey respondents and professional prompt engineers. Overall, annotators rate the attempted reproductions as dissatisfactory 60% of the time. To the extent that there is improvement, more than half of this improvement can be matched by blind steering mechanism that generates edits of user's first prompt without knowing the goal image. We find similar results in a smaller user study for steering LLMS (Appendix B).
Setup. We use the framework described in Section 2 to study the steerability of set-tto-imae models. These include models, such as Stable Diffusion [23], that are prompted via text to generate images. We refer to this type of stecring as text steering. We note that some models allow other mechanisms for steering, such as negative prompts [3] and image inpainting [82]. We focus our study on text prompting because it is the most common mechanism across these models, although we find similar results when we expand to other settings, such as allowing users to experiment with random seeds or control classifier-free guidance settings [34] (see Appendix B).
guidaaae
We study 10 text-to-image models. We consider four variants of the Stable Diffusion models: SD3-large, meedimm, SD3.5-large and SD3.5 SD3..-largeeturbo [23]. We also consider two versions of DALL-E: DALL-E 2 [67] and DALL-E 3 [5]. We also consider Flux-dev [6], Flux- -. -.-ro-..--r--utrr [6]. 1deogram-v2-turbbe [37], and Photon-flash [57]. We use publicly available APIs for each model: Stability AI for the stable diffusion models, the OpenAl API for the DALL-E models, and the Replicate AI API for all other models.
Benchmark. For each model, we sample a goal image from the model's producible set by prompting it with random image caption from the PixelProse dataset [73]. We then show the goal image to a human user and instruct them to generate an image as close as possible to the goal image. We give them 5 attempts to prompt the model. After each prompt, they are shown the image generated by the model and given the option to refine their prompt to improve the generated image. We repeat this process across goal images and users.

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Model
Imp
POM-1
POM-S
How close blind steering to human steering?
1.0
DALL-E2
0.60 (0.04)
0.64 (0.06)
0.72 (0.05)
perfomance attained
3
0,58 (0.04)
0.56 (0.07)
0.52 (0.06)
Frection of human
0.8
Flux-dev
0.66 (0.04)
0.48 (0.07)
0.64 (0.06)
Flux- Li-pro-ultaa
0.54 (0.05)
0.49
0.55 (0.06)
06
Ideogram-Y2-uun -2-turbo
0.61 (0.04)
0.46 (0.07)
0.70
Photon-flas flash
0.74 (0.04)
0.45 (0.07)
0.56 (0.06)
0.4
SD3-large
0.51 (0.05)
(0.07)
0.52 (0.06)
0.2
3D3.5-medium
0.66 (0.04)
0.64 (0.06)
0.68 (0.05)
Human performance
SD3.5--aagee S-ragge-urrb
0.69 (0.04)
0.56 (0.06)
0.61 (0.06)
0.0
SDae.-laare
0.63 (0:04)
0.54 (0.07)
(0.05)
7 Number of blind prompt rewrites (Humans have attempts reprompt)
Average
0.62 (0.04)
0.54 (0.07)
0.62 (0.06)
Figure 3: Left: Steerabilityy is poor for all models. Imp shows Improvement Rate and POM-1 and POM-5 show Trompt-Output Misalignment on users' 1st and Sth attempts, respectively (standard errors in parentheses). Right: When a blind LLM is given the same number of attempts as humans to rewrite prompt, it achieves more than half of the human improvement. Improvement is measured by the difference between user's first and best DreamSim score with the goal image (bars represent single standard errors).
Most of the models we consider rely on random seeds to generate images; the same prompt with different random seeds may result in different images. In our surveys, we don't provide users with the random seed used to generate the goal image. This reflects how these models are used in practice: if a model is able to generate an image a user wants, the user typically won't know the specific seed(s) necessary to generate that image. In Appendix B, we repeat our main experiment but allow users to choose random seeds in addition to prompts, finding no significant change in steering performance.
We consider various metrics for judging steering attempts. All metrics use separate human annotators from the ones who perform the steering. Satisfaction rate measures whether annotators would be satisfied with the steered image in trying to produce the goal image, according to a four-point scale (very unsatisfied to very satisfied). Image similarity rating asks the same question but 10-point scale. Improvement rate (Imp) is the percent of the time human raters judge the last attempted generated image be closer the goal image than the first attempt. Prompt-Outpic Misalignment (POM) measures the percent of the time human judges deem human steering prompts to be better descriptions of the goal images than the corresponding generated images. other words, POM measures the frequency with which the model's 's generated output fails to match what human would reasonably expect from their prompt for recreating goal image. We compute POM scores for both the 1st prompt (POM- 1) and the last prompt (POM-5) that humans attempt. See Figure 20 for a visual summary of these metrics.
We recruit survey participants on the Prolific platform [64]. We received an IRB review and exemption for this study. For all of our surveys, we paid respondents an implied rate of $12.50-$13.55 per hour, and the median survey completion time ranged from 9-15 minutes across tasks. See Appendix D for more survey details. In total, we collect data for 554 goal images, resulting in 2,770 total (goal image, generated image) pairs across 277 different survey-takers. We collect total of 18,550 ratings across the four metrics. We release all of the data we collect.²
Results. We find that steerability is poor across models. Annotators rate the attempted reproductions as unsatisfactory 60% of the time; moreover, 27% say they' dd be "very unsatisfied" with the attempted reproductions compared to only 10% saying they's d be "very satisfied". The image similarity results on a 10-point scale are depicted in Figure 2. The best model is Photon-flast while the worst model is DALL-E2 2. In general, there is not much difference between different-sized models in the same family, although larger ones perform marginally better. We find an average POM-1 score of 0.54, which means that more than half the time a human's description aligns with the goal image better than the generated image. See Figure I and Figure 11 for examples of steering attempts.
Poor steerability isn't due only to poor initial attempted generations. Figure 3 shows the improvement rate: only 62% of the time are images generated by a human's 's 5th attempt ranked as more similar to the goal image than images generated by human's 1st attempt (compared to a baseline of 50%). To assess the impact of longer-term steering experience, we also repeat the main experiment with a small group of professional prompt engineers recruited via Upwork [79]. Across 34 examples, professionals
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only slightly outperform non non-experts, with final similarity scores 10% higher (see Appendix B). However, they also show no reliable improvement over attempts, with even lower improvement rates.
Additionally, we find that even what appears to be improvement can be partially explained by the opportunity for users try out different prompts. To quantify this, we "blindly" generate edits of user's first prompt without knowing the goal image by instructing a large language model (LLM) to produce k variations of a user's first prompt. We then measure the maximum similarity score between the k images produced by the blind prompts and the goal image. The results in Figure 3 show that this blind form of steering attains a substantial portion of human improvement; when humans and blind LLM have the same number of attempts to rewrite prompt, the LLM achieves 52% of the human improvement. Further, 87% of the human improvement can be achieved by a blind LLM with 20 opportunities to rewrite the prompt. These results help calibrate the scale of user improvement by considering "lottery effects" of reprompting. We provide more details in Appendix C.I.
Because our metrics are based on human annotations, they might be subject to human variability. In Ap- pendix B, we repeat our study using model- similarity metrics; CLIP embedding cosine similarity [66] and DreamSim [25]. We observe patterns consistent with our human annotations (Figure 5).
Steerability vs. prompt-image alignment. While there exist metrics for prompt-image alignment like CLIP [31]. steerability measures something distinct: user intent. If many images are consistent with single prompt or if user has many desiderata that are infeasible to convey in single prompt, CLIP scores can be high with poor steerability. Still, a natural question is how much of steerability can be attributed to prompt-image (mis)aligament empirically. Figure 6 plots the relationship between each attempt's 's prompt-image alignment (measured via CLIP) and steerability score. The correlation low: 0.32. While poor prompt-image alignment is factor for poor steerability, it cannot fully explain it.
4 Comparing producibility and steerability
One way to artificially improve model's steerability is to limit the outputs it can produce. However, this could come at the expense of producibility, since the model wouldn't be able to generate as many outputs. Here we ask: is there way to assess whether steerability improvements reflect genuine improvements in steering mechanisms as opposed to differences in producibility?
We first study setting where there is controllable tradeoff between producibility and steerability: when steerability is artificially improved by constraining all images to come from the same random seed. Specifically, we consider different versions of Stable Diffusion 3.5 Large Turbo: the default model, along with three versions that constrain the number of random seeds the model can use to produce images. We measure steerability and producibility scores for each version. Figure 7 in Appendix C. demonstrates a tradeoff: as the model becomes more constrained, it's easier to steer, but less capable of producing. While there's s tradeoff' in this artificial setting, it's possible for a model to have better metrics than another for both producibility and steerability; indeed, while DALL-E 3 is more sophisticated than DALL-E 2, our empirical results show that it does not have worse steerability. Like many other metrics (e.g. Type-I and Type-Il errors), it's important to measure multiple dimensions producibility and steerability to evaluate and improve models.
We formalize this tradeoff with another decomposition. Consider two -aangeggnnratiinn models M and M2 with average steerability rewards Rj and Rg. Here we expand on the notation in Section 2 by considering each model's distribution over producible images: Pr (x) and p2(x). Each model also has a steering mechanism that results in some expected reward for each image being reproduced: denote by (x) the average reward user whose goal to produce image x receives when using Mi. The difference in steerability between the two models can be expressed as:
- R₂- Ri = f (1())) + I (2(x)) - f (x)x)
(4)
(x) (x
imprivement due to steering mechanism
improvement due to producible set
The first term holds the producible set constant and measures differences due to the steering mechanism alone. Meanwhile, if the difference is dominated by the second term, improvement
*Here each denotes image, although it can also be characterized as image and corresponding prompt.

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Dacomposing steerability improvement
16
Model
R-
MSE
Improvement
Improvement due to:
Steering machanism
Producible set
Bascline
0.000 (0.000)
1.061 (0.000)
10
Model only
0.082 (0.025)
0.974 (0.071)
Model + prompi
0.459 (0.040)
0.543 (0.034)
06
Model image prompt
0.493 (0.035)
0.508 (0.033)
Buader
ai3 ar3-lagge
Rox E-prouuttnn
S-laggs
phaton-far Aast
Bdeegran Audio
ad S-arge butto
add 5-medium
dak--33
Figure 4: Left: Predictive performance using CLIPP- methods to predict steerability (standard errors in parentheses). Right: Each model's steerability improvement DALL-E 2 broken into two components: one based on differences the steering mechanism, the other based on differences in the producible set (Equation 5).
PixelProse
Tiles
Avg. change
% improve
Avg. change
improve
Text steering
0.025 (0.010)
54.7% (3.6%)
0.029 (0.015)
56.2% (5.1%)
Image steering (random proposals) Image steering (learned proposals)
0.040 (0.007)
62.0% (4.9%)
0.053 (0.005)
66.8% (2.6%)
0.053 (0.008)
74.2% (5.4%)
0.072 (0.007)
70.7% (3.0%)
Table 1: Image steering outperforms text steering, with further improvements coming from learning proposal distributions. We show each method's improvement between the first and last generated image, both with the average magnitude of improvement (avg. change), and the percent of time there is an improvement (% improve). Similarity scores are measured with DreamSim [25]. Standard errors are in parentheses.
cannot be attributed to differences in the steering mechanism; only the producible set is changing. While Equation 5 isn't a causal quantity, accounts for how differences are distributed, and it's mathematically equivalent to decompositions of group differences used to study economic outcomes [24]. See Appendix A for a derivation.
Estimating the terms in Equation 5 involves training predictor (X) to predict steerability from inputs for each image-generation model. How well can ML methods predict steerability? We consider four predictive models: a baseline using only the data mean, a model using only the model being steered as a feature, model also using a user's prompt, and a model using both the goal image and the user's prompt. We split the data collected in Section into 80/20 train/test splits. To predict steerability, we encode the prompts and/or images with CLIP and fine-tune CLIP layers and regression head to predict DreamSim scores from the resulting embeddings and one-hot encoding of the model name. The training and analyses were performed on a single A 100 GPU.
Figure 4 shows the predictive performance of each model on held-out data. We find that the sighest-performing model can explain nearly half of the variance in stecrability scores. We imagine that more sophisticated prediction methods can do even better.
We now estimate Equation 5 using Monte-Carlo sampling. Figure 4 reports results using the best predictive model. We compare each model to DALL-E 2, since it is the only model for which other models have consistently better steerability. Across models, half of this improvement can be attributed to genuine improvements in steering mechanisms, while the other half is attributed to differences in producibility sets. This means, for example, that the images that DALL-E 3 is more likely to produce than DALLL-E are easier for people to steer to regardless of the model.
5 Steerability can be improved
The results Section suggest the need to focus on improving the steerability of generative models. Here, we consider a simple alternative form of steering image models, based on selecting images instead of refining prompts. In a user study with 500 human steerers, this new steering mechanism results in more than 2x improvements over text steering.
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Image steering, Our results in Section 3 suggest that prompting is an inefficient steering mechanism. Here, we consider a simple alternative form of steering. which we refer to as image steering. As before, humans begin by providing a prompt to a model, which is then used to generate an image. However; instead of relying on humans to rearticulate new prompt, the model returns variations of the image. The user can either select a preferred variation or stick with their original image, at which point the model suggests new variations. This procedure does not require rearticulating textual prompts; instead, the model does the "rearticulating" while the human chooses between options.
Specifically, given current imagex, x, new images are sampled from a steering distribution q(x [x) and suggested to the user. The efficacy of image steering depends on this steering distribution. Which image variations should be suggested? We consider two approaches: one is to suggest random perturbations in latent space. However, some sets of image variations will be more helpful to humans than others, regardless of the goal image they're re trying to generate. So we also consider a second approach, which learns steering distribution that suggests set of images likely to improve steering (independently of the goal image). We use a simple RI. technique that learns the optimal distance between current and suggested images by simulating human steerers offline; we describe more details in Appendix C.2.
Results. We use our benchmark to evaluate image steering. We focus on two sampling distributions to generate goal images. We first consider prompts from the PixelProse dataset to generate goal images (as in Section 3). To also understand the effects in domain where human articulation is more difficult, we consider images generated from prompts about abstract geometric patterns. Specifically, we prompt Claude-3.5-Sonnee to provide 25 variations of prompts involving geometric patterns on tiles and use these prompts to generate images. For the image steering methods, we only suggest two new images per round and do not allow users to update their text prompt. We also limit the number of attempts to 5 to allow for direct comparison to text steering.
Because our goal is to compare steering methods rather than generative models, we use a fixed model: Stable Diffusion 1.4 [68], which is open-source, allowing us to perform perturbations. Moreover, at 860M parameters, it large enough produce high-quality images but small enough to enable efficient image generation given our academic- computing constraints. We recruited 500 participants from Prolific to perform steering. Because image steering involves perturbing the model's latent space, we could not rely on black-box APIs. Instead, we used a server of 8 H100 GPUs to perform image generation and perturbation; we found the process to be efficient. We use DreamSim to rate the similarity of the generated and target images. Because all methods use prompting to generate the first image, we evaluate methods by their improvement between the first and last generated image.
The results are summarized in Table 1. Across both domains and evaluation metrics, image steering outperforms text steering. While learning the proposal distribution expands on these improvements, there is already ample improvement without it. In both domains, the average total improvement is more than 2x the improvement for text steering. Figure 9 shows how generated images improve as human users continue to steer. While the improvement for text steering is relatively flat, image steering has steeper improvements (see Figure 12 and Figure 13 for examples). These results demonstrate that good steering performance on our benchmark is attainable; the fact that this simple image steering mechanism attains good results suggests that more sophisticated procedures can do even better.
6 Related work
Our work on evaluating steerability is contextualized by large body of existing work focused on evaluating and improving human-Al interaction. For example, Lee et al. [48] develop a framework for evaluating human-language model interaction, and further research explores gaps in current human-AL AI interactions and proposes methods for improvement [13, 80, 44, 14, 56, 38, 10]. In the context of text-to-image generation models, many benchmarks involving steerability evaluate the process of editing images with generative models [82, 76, 4, 72, 26]. Editing is a useful strategy for steering these benchmarks clearly measure steerability. However, unlike our framework, they are not measuring steerability in isolation - in order to edit a generated image to perfectly match goal image, model must be capable of producing the goal image in the first place.
In the LLM literature, steerability and producibility are also typically evaluated in conjunction [11, 63, 58, 51]. For example, Sanfirescu-Pereiie et al. [85] conduct user study finding that non-Al experts struggle to make systematic progress prompt design with chatbots. While these evaluation frameworks are important for understanding overall efficacy, they combine producibility
8

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and steerability into a single metric. Decomposing these effects is crucial for improving models along each dimension, and our framework studies steerability in isolation.
Our main study focuses on evaluating the steerability of image generation models. There exist many text-to-image generation benchmarks for measuring the quality of generated images in con-interactive experiments [54, 62, 28, 353, some of which have found systematic failures (c.g. due to shared CLIP encodings) [78]. Other evaluation metries include measures of: (i) text-image alignment [31, 49, 50]; (ii) similarity to a reference image (often using embeddings from models like CLIP) [87, 83, 66, 9]; (iii) image quality [71]: and (iv) human preferences [25, 43, 84, 65, 47]. Most frequently, these metrics are used to measure producibility and not steerability, as is the goal of our paper.
In this paper we choose to evaluate text steering and image steering our primary mechanisms. However, prior work highlights many alternative steering mechanisms. Examples in image generation include negative prompting [3], manipulating prompts and their embeddings [17, 29], adding conditional controls [86], using human image edits [88], and generally leveraging textual and image inputs guide the model [61, 8, 30]. Examples for other generative tasks include extracting and moving along interpretable axes in the latent space [21, 7, 55, 75, 16], updating model weights [18, 15, 42], and editing prompts [41, 45]. These mechanisms are all amenable our framework and test; while we find that image steering outperforms text steering in our surveys, we do not attempt to suggest is the best overall steering mechanism. Future work should explore these other approaches.
Similar to how we learn steering distributions for image steering, existing approaches use reinforcement learning align models with human preferences [40]. For example, Hilgard et al. [33] develop a training procedure to optimize machine representations for human-model collaboration. Reinforcement learning from human feedback (RLHF) is also used improve the alignment of LLMs [12, 2, 52] or generated images [47, 53] with human preferences. While these methods are similar to our technique in that they use human preferences adjust model behavior, their goals are different; RLHF's is designed to align model outputs with human preferences, while our technique designed to align model steering with human intuitions.
Our work complements recent findings by Jahani et al. [39] and Vodrahalli & Zou [81]. who conduct large-scale experiments on human uses of generative models. Like our work, these experiments focus on how well humans can prompt key difference is that we focus on measuring steerability in isolation from producibility, so we only include reference images in a model's producible set.
models to produce certain goal images. A
7 Conclusion
This paper introduced a mathematical framework and benchmark for evaluating the steerability of gener- ative models. We implemented the benchmark, which revealed shortcomings in text-to-image models. We also showed stecrability can be improved, achieving progress with simple alternative mechanisms.
Our work has several limitations and future directions worth noting. While there are many methods for steering models, focused text steering and image steering. However our framework can accommodate many forms of steering, and future work should explore other approaches. Additionally, we only considered basic implementation for suggesting images that lead to better steering. The fact that even this basic implementation achieved improvements suggests that more sophisticated methods applied larger models could yield even greater benefits. Finally, while we've focused on whether steerability improves during single interactions, it would be interesting to study longer hore-horizons, e.g. over the course of months.
We designed the benchmark with the goal of avoiding copyright issues: cach image used in the benchmark is generated by text-to-image model, and is not drawn from any external, copyrighted dataset. Still, as with any use of text-to-image models, it's possible that a model may produce a copyrighted image. Moreover, improved steerability could also facilitate malicious uses. However, understanding and quantifying steerability is an important prerequisite for responsible deployment and mitigation planning.
References
[1] Anthropic, A. The Claude 3 model family: Opus, sonnet, haiku. Claude-3 Model Card, 1, 2024.

---

[2] Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al. Training helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprini arXiv:2204.088 05862, 2022.
[3] Ban, Y., Wang, R., Zhou, T., Cheng, M.. Gong, B., and Hsieh, C.-. Understanding the impact of negative prompts: When and how do they take effect? European Conference on Computer Vision, pp. 190-206. Springer, 2025.
[4] Basu, S., Saberi, M., Bhardwaj, S., Chegini, A. M., Massiceti, D., Sanjabi, M., Hu, S. X., and Feizi, S. Editval: Benchmarking diffusion based text-guided image editing methods, arXiv 2023.
preprint arXiv:
[5] Betker, J., Goh, G., Jing. I.., Brooks, T., Wang, J., Li, I.., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., al. Improving image generation with better captions. Computer Science. https://edn. openai. com/papers/dallle-3 pdf. 2(3):8, 2023.
[6] Black Forest Labs. Flux. https: //gitub... http://ggiiuuu...mbaaatttee
2023.
[7] Bricken, T., Templeton, A., Batson, J., Chen, B., Jermyn, A., Conerly, T., Turner, N. L., Anil, C., Denison, C., Askell, A., et al. Decomposing language models with dictionary learning. 2023.
[8] Brooks, T., Holynski, A., and Efros, A. A. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18392 18402, 2023.
[9] Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, Emerging properties in self-ss vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650-9660, 2021.
[10] Carter, S. and Nielsen, M. Using artificial intelligence to augment human intelligence. Distill, (12):e9, 2017.
[11] Chang, T., Wiens, J., Schnabel, T., and Swaminathan, A. Measuring steerability in large language models. In Neurips Safe Generative Al Workshop, 2024.
[12] Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D. Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30, 2017.
[13] Collins, K. M., Jiang, A. Q., Frieder, S., Wong, L.,, Zilka, M., Bhatt, U., Lukasiewicz, T., Wu, Y., Tenenbaum, J. B., Hart, W., et al. Evaluating language models for mathematics through interactions. Proceedings of the National Academy of Sciences, 121(24):2311122121, 2024.
[14] Collins, M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi-Xuan, T., Ho, M., Mansinghka, V., et al. Building machines that learn and think with people. Nature Human Behaviour, 8 (110))8851-0
2024.
[15] Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., and Liu, R. Plug and play language models: A simple approach to controlled text generation. arXiv preprint 19Xiv:111..02114, 2019.
[16] Daujotas, G. Interpreting and steering features in images, 2024. URL https://www. lesswrong. http:/tsssnnugyyssSSSoooo/e/eeee
wand-sttering-feature
2024.
[17] Deckers, N., Peters, J., and Potthast, M. Manipulating embeddings of stable diffusion prompts. arXiv preprint arXiv:2308.12053 2023.
[18] Dong, Y., Wang, Z., Sreedhar, M. N., Wu, X., and Kuchaiev, O. Steerlm: Attribute conditioned SFT as an (user-steerable) alternative to RLHF. arXiv preprini arXiv:2310.05344, 2023.
[19] Donoho, D. Data Science the Singularity. Harvard Data Science Review, 6(1), 2024. https://hdst.mitresss
[20] Downs, A. An economic theory political action in democracy. Journal of Political Economy, 6552)_135-150, 1957.
10

---

[21] Du, Y., Liu, X., Shah, N., Liu, S.. Zhang, J., and Zhou, B. Chemspace: interpretable and interactive chemical space exploration. 2022.
[22] Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten,
A., Yang, A., Fan, A., et al. The Llama 3 herd of models. arXiv preprint arXiv:2407.2178. 2024.
[23] Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first International Conference on Machine Learning. 2024.
[24] Fortin, N., Lemieux, T., and Firpo, S. Decomposition methods in economics. Handbook of labor economics, volume 4, pp. 1-102. Elsevier, 2011.
[25] Fu, S., Tamir, N., Sundaram, S., Chai, L., Zhang, R., Dekel, T., and Isola, P. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. arXiv preprint arXiv:2306.092: 2023.
[26] Fu, T.J., Hu, W., Du, X., Wang, W. Y., Yang, Y., and Gan, Z. Guiding instruction- based image editing via multimodal large language models. arXiv preprint arXiv:2309. 17/02, 2023.
[27] Google Deepmind. Gemini-2.0 2.-rai--.0-fash--exx. https://deepmind. wtw.a//gggooeeeeeeeolooiiioio gemini/flash/, 2024.
[28] Hall, M., Mañas, O., Askari, R., Ibrahim, M., Ross, C., Astolfi, P., Ifriqi, T. B., Havasi, M., Benchetrit, Y., Ullrich, K., et al. Evalgim: A library for evaluating generative image models. arXiv preprint 2rXiv:2412. 10604, 2024.
[29] Hao, Y., Chi, Z., Dong, L.. and Wei, F. Optimizing prompts for text-to- text-to-image generation. Advances Neural Information Processing Systems, 36, 2024.
[30] Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., and Cohen-Or, D. Prompt-to- prompt image editing with cross attention control. arXiv preprint arXiv:2208. 01626 2022.
[31] Hessel, J., Holtzman, A., Forbes, M., Bras, R. I.,, and Choi, Y. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXiv:2 104.0044000..00 087/18 2021.
[32] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, GANs trained by two time-scale update rule converge to local Nash equilibrium. Advances in Neural Information Processing Systems, 30, 2017.
[33] Hilgard, S., Rosenfeld, N., Banaji, M. R., Cao, J., and Parkes, Learning representations by humans, for humans. In International Conference on Machine Learning, pp. 4227-4238. PMLR, 2021.
[34] Ho, J. and Salimans, Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12595 2022.
[35] Huang, Sun, K., Xie, F., Z., and Liu, X. 12i-comphench: A comprehensive benchmark for open-world compositional text-to-image generation. Advances Neural Information Processing Systems, 36:78723-787777 2023.
[36] Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., Hayes, A., Radford, A., et Gpt-4o system card, arXiv preprint a1X22:2410.21226, 2024.
[37]
Ideogram AI. Ideogram. https://docs: ideogora.ddeegmammmmm wwt.graa.ai....m.. 2024.
[38] Inkpen, K., Chappidi, S., Mallari, K., Nushi, B., Ramesh, D., Michelucei, P., Mandava, V., Vepřek, L. H., and Quinn, G. Advancing human-Al complementarity: The impact of user expertise and algorithmic tuning on joint decision making. ACM Transactions on Computer- Human Interaction, 30(5):1-29, 2023.
[39] Jahani, F., Manning, B., Zhang, J., TuYe, H.-Y., Alsobay, M. A. M., Nicolaides, C., Suri, S., and Holtz, D. As generative models improve, we must adapt our prompts. Technical report, Center for Open Science, 2024.

---

[40] Jahanian, A., Chai, L., and Isola, P. On the "steerability" of generative adversarial networks. 2019.
arXiv preprint
[41] Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., and Socher, R. Ctrl: A conditional transformer language model for controllable generation. arXiv preprint MoXiv:/909.05888 2019,
[42] Khalifa, M., Elsahar, H., and Dymetman, M. A distributional approach to controlled text generation. arXiv preprint arXiv:2012. 1635, 2020.
[43] Kirstain, Y., Polyak, A., Singer, U., Matiana, S., Penna, J., and Levy, O. Pick-a-pic: An open dataset of user preferences for text-to-image generation. Advances Neural Information Processing Systems, 36:36652-36663 2023.
[44] Kleinberg. J., Lakkaraju, H., Leskovec, J., Ludwig, J., and Mullainathan, S. Human decisions and machine predictions. The quarterly journal of economics, 133(1): (:237299, 2018
[45] Konen, K., Jentzsch, S., Diallo, D., Schütt, P., Bensch, O., Baff, R. E., Opitz, D., and Hecking. T. Style vectors for steering generative large language model. arXiv preprint arXiv: 01618. 2024.
[46] Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T. Improved precision and recall metric for assessing generative models. Advances in Neural Information Processing Systems, 32, 2019.
[47] Lee, K., Liu, H., Ryu, M., Watkins, O., Du, Y., Boutilier, C., Abbeel, P., Ghavamzadeh, M., and Gu, S. Aligning text-to-image models using human feedback. arXiv preprint 2Xi2:2302..1192 2023.
[48] Lee, M., Srivastava, M. Hardy, A., Thickstun, J., Durmus, E., Paranjape, A., Gerard-Ursin, I., Li, X. L., Ladhak, F., Rong. F., et al. Evaluating human-language model interaction. arXiv preprint arXiv:2212.09744 2022.
[49] Li, J., Li, D., Xiong, C., and Hoi, S. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pp. 22888-12900. PMLR, 2022.
[50] Li, J., Li, D., Savarese, S., and Hoi, Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International Conference on Machine Learning, pp. 19730-19742. PMLR, 2023.
[51] Li, J., Mchrabi, N., Peris, C., Goyal, P., Chang, K.-W., Galstyan, A., Zemel, R., and Gupta, R. On the steerability of large language models toward data-driven personas. arXiv preprint arXiv:2311.0497
2023.
[52] Li, Z., Peng, B., He, P., Galley, M., Gao, J., and Yan, X. Guiding large language models via directional stimulus prompting. Advances in Neural Information Processing Systems, 36, 2024.
[53] Liang, Y., He, J., Li, G., Li, P., Klimovskiy, A., Carolan, N., Sun, J., Pont-Tuset, J., Young, S., Yang, F., et al. Rich human feedback for text-to- text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19401-19411 2024.
[54] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 62, 2014, Proceedings, Part 13, pp. 740-755. Springer, 2014.
[55] Liu, S., Wang, C., Lu, J., Nie, W., Wang, H., Li, Z., Zhou, B., and Tang, J. Unsupervised discovery of steerable factors when graph deep generative models are entangled. Transactions on Machine Learning Research, 2024.
[56] Ludwig, and Mullainathan, S. Machine learning as a tool for hypothesis generation. The Quarterly Journal of Economics, 139(2):751-82) 2024.
[57] Luma Labs. Photon. https: https://lumalaby ai/ ai/photon, 2024.
12

---

[58] Michling, E.. Desmond, M., Ramamurthy, K. N., Daly, E. M., Dognin, P., Rios, J., Bouneffouf, D., and Liu, M. Evaluating the prompt steerability of large language models. arXiv preprint 14Xi::411.12403 2024.
[59] Misra, R. News category dataset. arXiv preprint arXiv:2209.11 1429, 2022.
[60]
Misra, R. and Grover, J. Sculpting Data for MI.: The first act of Machine Learning. 01 2021. ISBN 9798585463570.
[61] Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint 12. 2021.
[62] Nilsback, M.-E. and Zisserman, A. Automated flower classification over large number of classes. In 2008 Sixth Indian conference on computer vision, graphics & image processing, pp- 722-729. IEEE, 2008.
[63] Noever, D. and Hyams, S. Al text-to-behavior: A study in steerability. arXiv preprint arXiv:2308.07322 07322, 2023.
[64] Palan, S. and Schitter, C. Prolific.ac-4 subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17:22 2018.
[65] Peng, Y., Cui, Y., Tang, H., Qi, Z., Dong, R., Bai, J., Han, C., Ge, Z., Zhang, X., and Xia, S.-T. Dreambench++: A human-aligned benchmark for personalized image generation. arXiv preprint arXiv:2406. 16855, 2024.
[66] Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pp. 8748-8763. PMLR, 2021.
[67] Ramesh, A.. Dhariwal, P., Nichol, A., Chu, C., and Chen, M. Hierarchical text-conditional image generation with clip latents. arXiv preprint 2rX.::2204.06122 1(2):3, 2022.
[68] Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer 2022.
Vision and Pattern Recognition, pp.
[69] Saharia, C., Chan, W. Saxena, S., Li. L., Whang, Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479-36494, 2022.
[70] Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. Improved techniques for training GANs. Advances in Neural Information Processing Systems, 29, 2016.
[71] Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35: 2022.
[72] Shi, J., Xu, N., Bui, T., Dernoncourt, F., Wen, Z., and Xu, C. A benchmark and baseline for language-drin driven image editing. In Proceedings of the Asian Conference Computer Vision, 2020.
[73] Singla, V., Yue, K., Paul, S., Shirkavand, R., Jayawardhana, M., Ganjdanesh, A., Huang, H., Bhatele, A., Somepalli, G., and Goldstein, T. From pixels prose: A large dataset of dense image captions. arXiv preprint 24Xiv:2406.1033 2024.
[74] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pp. 2256-2265. PMLR, 2015.
[75] Spingarn-Eliezerr N., Banner, R., and Michaeli, T. GAN "steerability" without optimization. arXiv preprint 2012:2201..05322 2020.
13

---

[76] Sun, S., Qu, B., Liang, X., Fan, S., and Gao, W. le-bench: Advancing the measurement of text-driven image editing for human perception alignment. arXiv preprint arXiv:2501.09927, 2025.
[77] Team, G., Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint M.XY::2000005536 2024.
[78] Tong, S., Jones, E.,, and Steinhardt, J. Mass-producing failures of multimodal systems with language models. In Neural Information Processing Systems, 2023.
[79] Upwork. Upwork: The freelance talent marketplace, 2025. URL https://www... http:://wwwwwwwor.ccoo com. Accessed: 2025-05-10.
[80] Vafa, K., Rambachan, A., and Mullainathan, Do large language models perform the way people expect? Measuring the human generalization function. International Conference on Machine Learning, 2024.
[81] Vodrahalli, K. and Zou, J. Artwhisperer: dataset for characterizing human-Al interactions in artistic creations. In International Conference on Machine Learning, 2024.
[82] Wang, S., Saharia, C., Montgomery, C.,, Pont-Tuset, J., Noy, S., Pellegrini, S., Onoe, Y., Laszlo, S.. Fleet, D. J., Soricut, R., et al. Imagen editor and editbench: Advancing and evaluating text-guided image inpainting. In Proceedings of the IEEE/CVF Conference Computer Vision and Puttern Recognition, pp- 18359-1 1369, 2023.
[83] Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. Image quality assessment: From error visibility structural similarity. IEEE Transactions on Image Processing, 13(4):00--11, 2004.
[84] Wu, X., Hao, Y., Sun, K., Chen, Y., Zhu, F., Zhao, R., and Li, H. Human preference score v2: A solid benchmark for evaluating human preferences text-to-image synthesis. arXiv preprint arXiv:2306.093:: 2023.
[85] Samfirescu-Pereing J., Wong, R. Y., Hartmann, B., and Yang, Q Why Johnny can't prompt: How non-Al experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1-21, 2023.
[86] Zhang, L.,, Rao, A., and Agrawala. M. Adding conditional control text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2023.
[87] Zhang, R., Isola, P., Efros, A. A., Shechtman, E.,, and Wang, O. The unreasonable effectiveness of deep features as perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-595, 2018.
[88] Zhu, J.-Y., Krähenbühl, P., Shechtman, E.,, and Efros, A. A. Generative visual manipulation on the natural image manifold. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October ll I-14, 2016, Proceedings, Part V 14, pp. 597-613. Springer, 2016.
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Model Rankings by Human vs. Automaleed Image Similarity Metrics
Rank lowest soore)
Human Rating
DreamSim
CLIP
4
dall-e-2
dali-e-3
Setr------uuie turbo
ktoggam- Butbo
Bux- 1-pro-uttra
ed 5-madium
set lrB-large
sd3 5-largo
Rux-day
Photen-fiash Riash
Model
Figure Rankings the steerability of image generation models according to human ratings, DreamSim, and CLIP embedding cosine similarity.
A Derivation of Equation 5.
Here we derive Equation 5. Consider two models, my and my, and denote each model's distribution over producible instances: (x( and p2(x). Each model also has steering mechanism that results in some expected reward for each image being reproduced: denote by (1 the average reward user whose goal is to produce image x receives from model with (2(x) the analogous quantity for model 2. Formally, H4 (x) = ( fx)) x, M mz), where the reward function is subscripted by the goal instance
make its dependence explicit. To simplify notation, we will define Sm (x) = (h(m,r r))), so that (x) { (x) = m,].
Denote by Ri the average steerability for model and R₂ the analogous quantity for model 2. By the law of iterated expectation,
R = S[SM M = mi) = ((M x m = m,] Epoco (X))).
(6)
Therefore, we can write
R₂ Ri = =pp -pp
f =f
(2(W))2(Wide - f (1(s)m((sdds.
=
Add and subtract the "cross-term" (2(x)
R₂ Ri f p2(x))2(x)dx f (2)x) / (x)x) f pi pi())
p2(x)(H2(x) - (x)) / /
(x)x) f (((x))i(x)dx (x) - Eppcolu(()) (x)())la
improveent due steering mechanism
improvement due to producible
B
Additional results
In this section we present additional analyses of our steerability benchmarks for text-to-image models and LLMss.
B.1
Comparing human-judged metries to automatic metries.
For our main experiment, use human ratings to evaluate model similarity. In Table 2 we consider algorithmic similarity metrics: DreamSim [25] and CLIP [31]. shown Figure 5, we find that the DreamSim and CLIP image similarity metries are relatively aligned with human ratings.
15

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Table 2: DreamSim scores CLIP embedding cosine similarities generated and goal images from benchmark on steering texi-to-image generative models. DreamSim Avg and CLIP Avg describe the average similarity of each model's 's generated images with their corresponding goal images.
Model
DreamSim Avg
CLIP Avg
DALL-E-2
0.52 (0.01)
0.75 (0.01)
DALL-E-3
0.62 (0.01)
0.78 (0.01)
Flux-dev
0.70 (0.01)
0.83 (0.01)
Flux-L.1-pro-ultze
0.66 (0.01)
0.82 (0.01)
Ideogram-v2-turbe
0.66 (0.01)
0.83 (0.01)
Photon-flash
0.68 (0.01)
0.85 (0.01)
SD3-large
0.68 (0.01)
0.84
SD3.5 --e5-mm
0.67 (0.01)
0.81
3D3.5-largeetumi
0,65 (0.01)
0.82 (0.01)
SD3.5-large
0.67 (0.01)
0.82 (0.01)
Average
0.65 (0.00)
0.82 (0.00)
Prompt-image align steerability
Correlation 0.32
Prompt-image alignment
0.
02
0.4
0.6 0,8
Steerability score
Figure 6: Stecrability scores only weakly correlated with prompt prompt-image alignment scores based CLIP. We calculate CLIPScore computing the cosine similarity between normalized CLIP embeddings of the prompt image. While cosine similarity is on scale from -1 1, we find most scores are between and 0.4, aligning with prior work [31].
B.2 LLM steerability.
We also apply our framework to evaluate the stecrability of large language models (LLMs). Just as in image generation, prompt an LLM to output the goal text. We prohibit users from using words in the goal text to prevent them from simply prompting the LLM to repeat it. We study 5 LLMs: Gemini-1.5-flast [77], Gemini- 2.0-flash-exp [27], GPT-4o [36], Souude-3.5-Sonee [1], and Llama-3.3 3.B- 1..3-70--Insrrune Instruct-Turrbo [22].
provide users goal instance piece of text and instruct them to iteratively
For each LLM, we sample goal text from its producible set by prompting to rewrite news headline from the Kaggle News Category dataset using a particular style and tone [59, 60). provide users with both the original and goal headlines, and instruct them to prompt the L.M to rewrite the original headline order to output the goal headline. Importantly, users are allowed to use words from the original but not the goal headline in their prompt, so they cannot directly instruct the LLM to output the goal headline. We give users attempts to prompt the model. See Table 4 for an example. More survey details are in Appendix D.
We use two kinds of human annotations to judge the steerability LLMs: satisfaction rate 4-point scale from "very unsatisfied" to "very satisfied") and improvement rate (as before). We recruit survey participants on Prolific, yielding 132 respondents, 118 goal headlines, and 2,030 total ratings. We again find poor steerability: human raters are very satisfied with the generated headline only 17% of the time. Additionally, the average improvement rate includes 50% its margin of error, indicating that improvements over steering attempts occur only half the time.
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Few users very satisfied with the generated headlines produced from steering LLMs, and human raters find improvements between the first and last generated headline about half the time. "Satisfaction Rating" " shows the average satisfaction of raters on a scale from (very unsatisfied) to 4 (very satisfied). "Very Satisfied" describes the fraction of satisfaction ratings equal to 4, and "Improvement" measures the fraction of time user's last headline is rated as more similar than their first.
Table 3:
Model
Satisfaction Rating
Very Satisfied
Improvement
2.54 (0.06)
0.15 (0.02)
0.59 (0.06)
Gemini-1.55 1.5--1..--Fattt
2.59 (0.07)
0.21 (0.03)
0.45 (0.06)
Semini-2.0-Flash--E.
2.48 (0.07)
0.17 (0.03)
0.44 (0.05)
GPT-4o
2.65 (0.07)
0.17 (0.03)
0.40 (0.06)
Llama-3.3-70B 1es-77----nttut--Tuie
2.48 (0.08)
0.19 (0.03)
0.42 (0.06)
Overall
2.54 (0.03)
0.18 (0.38)
0.47 (0.03)
Table 4: In our benchmark on the steerability of LLMs, hurnas are instructed to prompt an LLM to rewrite the original headline as the goal headline, without using words that appear in the goal headline but not the original. Below we show five rounds of user instructions and the resulting model outputs.
Item
Text
Original
What James Gandolfini's 's Son Learned Last Night
Headline
Goal
Progeny of 'Sopranos' Teon: A Critical Analysis of Michael Gandolfini's 's Artistic Evolution
Hcadline
Prompt
I will give you instructions to rewrite this news headline: "What James Gandolfini's 's Son Learned Last Night'. Only return the rewritten headline. Here are my instructions:
Preamble
Attempt
Instructions (User Input)
Generated Headline (Model Output)
Try make sound more like scientific article. looking the matter with dose of skepticism: make it sound like the artist's 's creation novel, as collected from previous creations presented something more.
Recent Analysis Suggests Novel Patterns in Gandolfini Heir's Artistic Evolution: A Critical Examination
Same as abov,, thou Now, more artsy, creative way, imply that the artist represents class of itself; imply parent-chie child relationship with art; highlight the high-pitched voice of the artist.
part of ritle, in
High Notes Heredity: A Scientific Investigation into Next- Generation Gandolfin''s Novel Artistic Synthesis
3
Same as above, thew: Now make less scientific and more like a journal title.
The High Notes of Legacy: How Gandolfini 's Heir Reimagines the Family Opus
4
Same as above, then: Now use a bit loss fancy words after: say the artist's name surname after.
The Sopranos' Echo: How Son's High Notes Carry His Father's Legacy
5
Same as above, then: Now use less fancy words; include the artist's name end.
A New Voice in Artistic Lineage: Michael Gandolfini Explores Depths Character
In Table 3 we show complete results from this experiment. Table contains examples of headlines.
B.3 Allowing users to choose seeds during text steering.
To evaluate text steering in its most flexible form, we conduct an additional survey in which we allow users to provide seeds in addition to text prompts when generating images. Thus if a user happens to find a seed with generated images that have a similar style to the goal image, they can continue using that seed. If not, they can try different seeds. In contrast, in our prior text steering survey we did not specify seeds, so each iteration of image generation used a random seed. The seed-choosing survey includes all image generation models in our study except DALL-E and DALL-E 3, which do not allow the specification of seeds in their API. We find that allowing users to specify the seed results in no statistically significant changes in their average DreamSim scores. We show the results in Table 5.
B.4 Steering with classifier-free guidance.
To evaluate text steering with more sophisticated steering mechanism, we conduct an additional survey allowing users to specify classifier-free guidance (CFG) settings to Stable Diffusion 3.5 Medium. Classifier-free guidance is a technique used in generative models, where the model generates samples conditioned on both the input and a guidance signal, without relying on an explicit classifier, to improve the alignment of generated outputs with user intentions [34]. We focus specifically on
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Table The average DreamSim score per attempl when users can vs. cannot choose their own seed during image generation. We find no statistically significant change the similarity of generated images to goal images, as measured by DreamSim.
Attempt
Choosing Seed
Not Choosing Seed
0.663 (0.017)
0.639 (0.008)
2
0.671 (0.017)
0.661 (0.007)
3
0.665 (0.019)
0.683 (0.007)
0.682 (0.018)
0.684 (0.007)
5
0.690 (0.017)
0.695 (0.007)
Average
0.674 (0.008)
0.672 (0.003)
Stable Diffusion because it is one of the few that allows CFG inputs in its API. In the survey, we allow users to choose a number from 10 for the guidance scale. We instruct them to choose how strictly they want the image generation process to adhere to their prompt text, with higher values keeping their images closer to their prompts, but potentially resulting unrealistic images. We recommend users default value of 4. Using this survey, we can compare the steerability of Stable Diffusion 3.5 Medium with and without CFG. Our results are shown in Table 6. We find no statistically significant improvement in steering when users can specify CFG parameters.
Table 6: Comparison of average steering performance with and without CFG across attempts, as measured by DreamSim.
Attempt
Without CFG
With CFG
1
0.638 (0.020)
0.664 (0.018)
2
0,662 (0.020)
0.677 (0.017)
3
0.676 (0.015)
0.667 (0.017)
4
0.681 (0.015)
0.703 (0.015)
5
0.693 (0.015)
0.682 (0.017)
Average
0.670 (0.008)
0.679 (0.007)
B.5 Steering performance of prompt engineers.
How much does training or experience in steering generative models improve steerability? To answer this question, we compare the steering performance non-experts and prompt engineers. Our main experiment was conducted by non-experts on the Prolific platform, so we repeat our experiment with prompt engineers recruited on the Upwork platform [79]. To reduce noise from different models and while having a direct comparison between the user groups, we restrict steering to Stable Diffusion 3.5 Large Turbo. Our results are shown in Table 7. Across 34 steering rounds we found prompt engineers were better than the average steerer, but only slightly: final similarity scores were only 10% higher than for non-experts. Moreover, we find that professional prompt engineers don't 'tt reliably improve between their first and last attempts, with even lower improvement rates than among the non-expert cohort.
C Additional details
C.1 Evaluating blind steering.
To evaluate the extent to which users' steering improvement rates can be attributed to learning (gaining an understanding of how to steer the model) vs. random chance, prompt an LLM to blindly
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Table 7: Per-attempl DreamSim scores for regular users and prompt engineers over five attempts.
Attempt
Regular users
Prompt engineers
0.631 (0.019)
0.767 (0.014)
2
0.636 (0.020)
0.772 (0.010)
0.669 (0.018)
0.777 (0.014)
4
0.654 (0.019)
0.772 (0.014)
0.670 (0.016)
0.741 (0.025)
Average
0.652 (0.008)
0.766 (0.007)
steer image generation models. Specifically, we randomly sample a set of 100 human steering traces from our prior experiment, each of which contains goal image and five prompts and generated images, corresponding to the human's is 5 steering attempts. To have an LLM blindly perform steering, we prompt GPT-4o produce variations of the user's s first attempt prompt for all 100 goal images, then use these variations to generate images. Importantly, we do not provide GPT-4o any additional information about the goal image. For each of the generated images, we calculate the DreamSim similarity to the goal image to evaluate the effectiveness of blind steering.
We repeat this experiment four times, prompting GPT-4o to produce 4. 7, 10, and 20 variations of the first attempt prompts. For example, to produce variations of a user's s first attempt prompt, we prompt GPT-4o as follows: "Provide four different, more detailed variations of this description and label them (1), (4): We calculate the maximum DreamSim score (measuring the similarity of the generated images the goal image) among the LLM's prompt variations and calculate how much it improves over the user's first attempt DreamSim score. For each goal image, we consider the LLM's improvement score be the difference between its highest DreamSim score and the human's first attempt DreamSim score. If this difference is negative, meaning the LLM's best score is worse than the human's first attempt, the improvement O. Likewise, we consider the human's improvement score to be the difference between their best score and their first attempt score, according to DreamSim. To calculate the expected human improvement due to random chance, we divide the mean LLM improvement score by the mean human improvement score. Thus this quantity summarizes the percentage of human steering progress that can be achieved through arbitrarily varying their first prompt. The results are shown in Figure 3.
Artificially improving steerability hurts producibility. Constraining all images to be generated by the same random seed might artificially improve steerability while worsening producibility since the model wouldn't be able to generate as many outputs. To study the relationship between model steerability and producibility in Figure 7, we considered different versions of Stable Diffusion 3.5 Large Turbo: the default model, along with three versions that constrain the number of random seeds the model can use to produce images. To compute steerability, we ran the survey described in Section but constrained the goal image and all user- esser-generated images to be in the relevant set of random seeds. When we constrain to one seed, this means that each image is generated using the same random seed as the goal image. When we constrain two seeds, this means that the goal image is generated using a single random seed (e.g. 42), and each attempt the user has to generate the goal image either uses that same random seed (42) or a different, fixed random seed (e.g. 43); for each attempt, this choice is sampled uniformly at random. We consider 1, 2, 3, and the default set of random seeds (4294967294). We measure stecrability using the DreamSim similarity score between goal and generated images, averaging over rounds and attempts.
In contrast, producibility here refers to how similar the closest image in a model's set of producible images comes to some goal image. To measure this, we first sample a prompt uniformly at random from PixelProse [73], which we then use to generate an image from a non-Stable Diffusion model. We then ask: how close can the Stable Diffusion model under consideration come to generating this image? Intuitively, models that are able to produce more random seeds can produce a larger set of images and could come closer. However, finding the optimal image in the model's 's producible set is an optimization challenge, because it requires combinatorial optimization over possible prompts. Instead, we approximate this optimization by searching over random variations of the prompt used
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Tradeoff between stcerability and producibiity
Producibility score
0.74
Defauit model
0.72
0.70
Constrained seed
0.66
0.68
0.70
0.72
Steerability score
Figure 7: While artificially decreasing the number of images model can produce improves steerability, it results in worse producibility. Each point represents Stable Diffusion 3.5 Large Turbo constrained to different number seeds. Similarity scores are calculated with DreamSim [25].
lleration
lteration
0.112
First turn
0,110
0.12
0.108
0.108
Value
0.10
Previous
Previous stay
0.104
0,102
0.100
0.08
0.888
0.2
0.4
0.8
0.8
1.0
0.2
0.4
0.8
0.8
1.0
Propossl step size
Propossi step size
lleration
lteration
0.13
0.125
0.120
0.12
Value
Previous switch
0.115
Previcus switch
0.11
Previous stay
0.110
Previous stay
0.100
0.09
0.395
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
Proposal slep size
Proposal step size
Figure The value function for each round of image generation.
to generate the original image. Specifically, we prompt an LLM to generate slight variations of the original prompt used to generate the image (using the same method as in Appendix B), and then generate images for each prompt variation and each random seed. We then find the generated image that is closest to the original image using DreamSim, which we report as our similarity score. When there are more than 30 possible (prompt, seed) variations, we subsample to only include 30 make the problem tractable. Overall, we perform this procedure for 50 different goal images for each of the 4 Stable Diffusion variations under consideration. The results are depicted in Figure
C.2 Reinforcement learning.
In this section, provide implementation details for our reinforcement learning technique for suggesting images to users.
The efficacy of image steering depends on the steering distribution q(x' [x). How should variations of images be suggested to human users? The steering distribution depends on the generative model being used, and here we consider models like diffusion models [74] that are based on transformations of noise vectors and text embeddings. That is, denote an image x = f(z), where E RD is a vector of random noise concatenated with prompt embedding. One possible steering distribution is to
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Improvement first attempt
0.08
Stowring
Image Steening
0.06
Image Stawring (RI)
0.04
0.02
0.00
5
Attempt Number
Figure 9: Improvement with image steering is more than twice that of text steering on the tiles dataset. Similarity measured with DreamSim. Single standard errors parentheses.
sample N(H, od) with e RD and then to decode new image x' = f(2+ This is the random sampling mechanism we consider.
However, some sets of suggested image variations will be more helpful to humans than others, regardless of the goal image they're 're trying to generate. For example, consider sets of suggested images, one of which contains images that humans find similar and the other that contains images that humans find distinct from one another yet are all similar to the current image. The variety of the latter set makes it more likely that a human can reach their goal image.
Instead of using random sampling (RL) technique that learns a steering distribution in order to maximize human steering capabilities. Specifically, parameterize the steering distribution q (x'[x) with parameters ø. Slightly modifying notation from Section 2 to make the steering function's dependence on 9s explicit, denote by h(m, rl Sm the instance that produced when human with reward function interacts with model m with steering distribution 4 The goal is to optimize
steering distribution, we propose reinforcement learning
arg max B, [r(h(m,((mmmp))) rlg))))
We consider learning a simple steering distribution that suggests image variations as function of the human steerer's 's behavior. We consider learning the optimal size of the perturbation. Specifically, we define the steering distribution as (x'1xx,,, s,),, where / E N is the attempt number and s E {0, 1) indicates whether the user stayed with the previous image suggestion or chose new one. Rather than perturbing both the text embeddings and latent representations, we found that focusing solely on the latent space was sufficient. For given latent vector z, we generate variations using a mixture of the original latent and random noise vector:
(1 s)2 ()-
(
where s E [0. 1] is the mixture scale and E ~ N(O, 1) is standard Gaussian noise. The normalization factor ensures the variance of z' matches that of z. This perturbation scheme preserves the model's learned manifold better than additive noise while allowing controlled exploration.
In principle, the policy & can be optimized with humans-in-the-loww e.g. by measuring and optimizing the reward for different stecring distributions when humans use them. However, this can be expensive as it requires many human users. Instead, we simulate human's 's behavior with agent; given human's prompt for goal image (collected offline), the agent always chooses the image suggestion it deems most similar to the goal image, as measured by the DreamSim similarity metric [25]. The reward for a single episode is then the similarity between the final image and the goal image. Importantly, while we have access the goal image train time (e.g. to choose the image that's 's most similar), in order to mimic real world use, the policy does not depend on the goal image.
We implement this procedure by discretizing the continuous mixture scale space into 10 equally spaced buckets between 0. and 1.0. The policy must choose a mixture scale for each of the 4 rounds of interaction. To handle the combinatorial nature of this sequential decision problem, decompose
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the policy into independent decisions per round. We use Thompson Sampling to balance exploration and exploitation. For each (round, bucket) pair, we maintain empirical reward statistics (counts and means). The posterior for each pair is approximated as normal distribution:
Mr.b N pr.b
ny,b +
where ar,b is the empirical mean reward, fl,b b is the number of times bucket b was chosen in round r, and σ² is a prior variance parameter (set to 1.0 in our experiments).
During training,
1. Sample a mean from each bucket's posterior for each round
2. Choose the bucket with highest sampled mean
3. Play an episode using the corresponding mixture scales
4. Update statistics for chosen (round, bucket) pairs using the episode reward
The reward for an episode is the improvement in similarity between the final generated image and the goal image, compared to the initial generated image.
We trained for 60,000 episodes using Stable Diffusion 1.4 as the base model. Each episode used a different prompt and reference image sampled from our dataset. Episodes interaction and image variations per round. We used the DreamSim perceptual similarity metric both for the simulator's choices during training and for evaluation.
with 4 rounds of
D Survey details
We recruited survey participants on the Prolific platform [64]. For all of our surveys, we paid respondents an implied rate of $12.50$$13.50 per hour, and the median survey completion time ranged from 9-15 minutes across tasks. We recruited different users for each survey. We received an IRB review and exemption for this study. We conducted the following surveys on text steering for image generation models: steering (Figure 14, Figure 15), generated vs. goal image similarity ratings on 10-point scale (Figure 16), improvement ratings (Figure 18), generated image satisfaction ratings 4-point scale (Figure 17), prompt-output misalignment ratings (Figure 19), steering with allowing users to choose seeds, and steering with constrained number of seeds. We conducted three analogous surveys on text steering for L.Mss: steering, generated vs. goal headline satisfaction ratings on 4-point scale, and improvement ratings. For all experiments involving rewriting multiple prompts, always used rewrites from scratch. That is, user sees their previous prompt, they 'r given the opportunity to edit it, and then submit to the model. Finally, we conducted surveys for image steering with and without RL, and in both the general and tiles domains. See Figure 14 and Figure 15 for examples.
Since it can be difficult to rate the similarity of images, we calibrate users in the similarity surveys by (1) consecutively showing them generated images for the same goal image (in a random order) and (2) providing them at least one duplicate (goal image, goal image) pair for each of the goal images they see (in a random order). Providing users duplicates gives them chance to see how a rating of 10 should look, and ordering goal images consecutively allows users to go back and forth between images to be more consistent in their ratings.
E
Additional examples
Table shows user's is attempt steering an LLM toward rewriting headline as the corresponding goal headline. Meanwhile, Figure 12 shows examples of users performing image steering. Additionally, Figure 13 shows examples of users leveraging image steering specifically reproduce goal images of tile patterns.
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book
image shones join two comperiment
the back lobal
Asheal with book
The compuraient hao fiice booko
varioua shades ef blue. From lightoct
booka companiment. the
compcrtio has tiree books
athacles colcur narge from lelt graaed atruday right (carlonat antude)
books vartous anadee
gets darier ecroso the thrce books, The
right
to As movo loft siude of true
you move the shace
which has the sook not dhate of biun Ae
torlight liihtest share
gets books
throe nod books.
has these books. This compartment has three books.
Attarge
Alweyg:
Goal image
Abokk containing books grackered stsce from bigt to dert. Aeekiz partiion anall
one bookkcan shell contwanngg only bumm baance gradiattdd and then tookk.
sbel combening only booke
partiton ahai and than net borice
Wooder booksholl soctons, cuch containng early amunged
Create image of books in sheff On the will be three books and
right Soation has four boons shades red. booils are events o ted.
wonden bookodee secilone, ench contensig nearty smmmged
right mndile vide thesw abr books into those aeput
upright
woodon
beszes secton secton hes thrwe booke achecies of red
own tono bic wood grain. The lighting soft and eeen, cacsing suiiii offect. Iigh quality tocuree with the books appwering emcot and sighity glowey.
Figure 10: Full prompts the examples in Figure
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magg
Atere!!
Abeel
Aboutd
Alrep
"Cew puma ahould thie oue
phara it utudi
"Oheu phat reaappre wheets ofiic
Trrae ope cinder hewe tandd:"
new sin dar offee whadl The whadd and Alwept
mage
Ataret
Abumet
Abbeet
Altente
TIRANAF PAPE
soffii the lidd Sran..
siright and risd are paln tsee
and neal roal thre rilaed big paloo
"Aling
soough can the nas thes dagi dign.
and carss red the Bige
lnn
big yellow righ.
mage
Afweet
Ahwwet
Albeet
Alreet
Abrert
cendithittess oorte whilightdd
tik ouo palr biiken bacigggaaa
suu goller the shaps with
this goble the shape with
seen reat putind becigroedd
Aboret
Alsept
slince spate wi salle.
shant pain soure
sows slend iee
bor aboss baih iese the
ture abaee bath atant
Norils New enpey negs
adiee
baciees
adtel lig
and mereraaeee
len
tht de
mibliookie caner taie,
lig
cine The burkee
cdjccrud offoret
whee twee eiject tilecholk.
dgs thee Blaa capechile
nega ln theee Flek dotectool
obaccia signyy
reciar
uteed
saeee The macie ciued a
cooar macee clacc a
la
cola. The ridile clacc
crand The renigedle ovaiig
aledd a tamed aightp tverda
educ lamed dighto mevada
cbacc auteed liooo lonads
ligh
and oveliig diocioed Ahere
let and cocolingg miscuve Adere
brows der brow busk
Figure 11: Examples of steering text-to-image generative models using text.
24

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Ood TOO
Kurps
Made ofre yor yolloe
Tag
Adwrpt
Abrp?
Abrp4
Khre
Sooing
Kare
Wel ON Hal 88.8
Figure 12: Examples steering image generation models with image steering; each iteration, humans have the choice to accept or reject a suggested edit.
25

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Abrea
KSPCS
SiAA trags Veloo Vingle
Chad evip
Atriiin aeerni by clereed
gelt.
Iago
Adarpis
Aamp2
Kargs
frrm CRCIM S2uCam
Figure 13: Users' attempts at using image steering to reproduce goal images of tile patterns that are difficult to articulate.
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Reference Image Preview (Round 1/2)
The image above is your reference image for this round (it may take few seconds to load).
What's next: On the following screen, you'll see this same image again, along with a text box where you can enter your descriptions. You'll have multiple attempts refine your description and get the Al-generated image to match this reference image as closely as possible.
Figure 14: Instructions for an image generation survey.
27

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iiies OT vines, creaung sense or uepur and symmeuy acioss uhe roiing landscape. the far background, the vineyard transitions seamlessly into more greenery, blending with the horizon.
Generate Images
Iteration 3 /
Here are edits suggested by Al. can either select one edits ('Move here") or stay with the previous image you selected. You should select the image that will be most helpful for getting the to generate your reference image.
Stay here
Move here
Move here
Figure 15: Example screen for image generation survey with image steering.
28

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Round 4730
Reforance Image
Al-Genstated
Rent estoriaction with the genwected brseed mattshee refersenca
Flutx: Pariinapptr muy fiw swoorille palcccs tau puotion!!
Somowhat nottie
Very Sattatled.
quasittie
image.
Figure 16: Example screen for the generated vs goal image similarity survey.
Reand 1/20
Ronk your satifaction th the Aingenoreted innage basod on woll matcheo ieforencs
image
you orgin catting.
Kotor the Images may toto fawv socondit food, plocoo exttionet
Very insotiatioul
Somewhatt
Sommentutt
Vary
setteited
setieded
Figure 17: Example sereen for the satisfaction rating survey.
29

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Round 8/30
Torget Imoge
motel
dillen motel
geggorootdd imge you
you trying
genendte referance image
You con toup the folkowing image quail lave mindi contant style, orglee satting mooe.
Mori Phagas toko flo sacordle to fooel placse bo pastiane!
BETT MOTEL
Motel
Love
Figure 18: Example screen for the improvement rating survey.
Choose di-genarcted image that best manches the prompt You can knep the lollowing image quec thes in minet content atyle, ongin, sotting mood (ltte
toart prstens!
Prompp
beekground
Image
terage
Figure 19; Example sereen for the prompt-output misalignment survey.
30

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Goal image
Image
Image
Prompt 5
singli sheel on
Asing' woeden the rucohe. the beit
divider
wooden diiddr in the booics on the right.
right are three books with
Improvement
Image
Image
closer
Goal image
Prompt-Output Misalignment (1)
Is
Prompt
better description of
Image
Goal image
shelf veccden book shell with lic
maddle Three tooko
the lott and thoo rod
Prompt-Output Misalignment (5)
Is
Prompt
a better description of
Image
Goal image
Asagg wooden with wooder dividr siveder beskss with bike oovers To the (od covers.
Figure Metrics used evaluating the steerability text-to-image models. See Section 3 for more details.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Our main contributions are developing mathematical framework decompose producibility and steerability (Section 2), using the framework to implement benchmarks on the steerability of text-to-image and language models (Section 3), analyzing how models achieve steerability (Section 4), and showing improvements are possible using a simple image-based steering mechanism (Section 5). We state those contributions in the abstract.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer:
[Yes]
Justification: We discuss the limitations of our work in Section 7, as well as throughout the text. For example, in Section we discuss how, while we focus on steering image models with text prompts because it is the most common, many other steering mechanisms have been proposed in prior work [3, 82].
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
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3. Theory assumptions and proofs
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Justification: We provide all the data we collected for the steerability benchmarks on text-to-image models and LLMs, as well as the scripts we used analyze this data and reproduce our results. We also describe our methodology for collecting this data in Section 3 and Appendix D. For the analysis in Section 4, we explain detail how we trained models predict steerability. For image steering, provide detailed explanation of our methodology in Appendix C.2.
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(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but should be possible for other researchers to have some path to reproducing or verifying the results.
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Justification: We provide all data we collected for the steerability benchmarks on text-to- image models and LLMs, well as the scripts used to analyze this data and reproduce our results. Our code is well-documented and includes a ReadME with instructions.
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Justification: We provide training and test details for the models we train to predict steerability in Section 4. We also provide details on learning the steering distribution for image steering in Appendix C.2.
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The answer NA means that the paper does not include experiments.
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Question: For each experiment, does the paper provide sufficient information the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
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The analyses in Section 4 were performed on a single A GPU, as noted in the paper. We describe the compute necessary for learning the steering distribution in Section 5.
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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
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Justification: This paper presents work whose goal is to advance the field of Machine Learning. While this paper is about maximizing steerability, an important caveat that without good safety regime in place, good steerability could reduce safety. It's important to find ways to only improve steerability for intentions that align with societal values. For example, one possibility is to highlight prompts that might pose risk and decrease steerability for those.
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Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
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Justification: We credit the creators of the models we evaluated Section 3, as well as the original creators of DreamSim [25], CLIP [66], and CLIPScore [31].
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for Answer: [Yes]
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Reviews:
[{"review_id": "9WXjt651ue", "reviewer": "Reviewer_QagF", "summary": "The paper deals with evaluating generative model performance, seeking to define the measure of steerability - meaning, how well can a human guide the model to produce a specific result. This notion is separate from the more commonly depicted metrics, that do not separate the question of how much the produced output fits the description from the quality of the produced image or text. A framework of human assessment is suggested to tackle this task, and findings appear to show that all tested models have rather low steerability - meaning, only around half the time humans are satisfied with the results or find them similar to the intended goal. This measurement technique is applied both to image and text generation, and later used also to measure the effectiveness of steering image generation using image selection.", "questions": "While I understand the theoretic need for separating producibility from steerability, I wonder, if we repeated the same experiment with any type of goal image (e.g., a collection of stock photos), would we see the same metric values, or would we see lower satisfcation numbers because the images are not reproducible? I think this is an important comparison that would add to the paper's claims.", "limitations": "Limitations are adequately discussed.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The paper has good insights into generative models, and captures a real gap between the focus of existing evaluation strategies and human usage of these models. The definitions of steerability are sound, and the experiment is valuable in terms of showing a true gap of following all user instructions - that generative models, especially text-to-image models, have a hard time with. The measurements are made using several models, and the metric is validated by showing that the superior method of using images to steer indeed achieves higher results in the metric. \nHowever, the metric being heavily human-dependent, and thus expensive to compute or improve on. More work can be put into automating parts of the assessment and proving the automation does not hurt precision. \nI also found the LLM assessment to be somewhat lacking. LLMs are rarely used in this scenario of reproducing a text in exactly the same way, but rather to generate text in a broader context. There's a very large difference between an image depicting the correct number of books, and a text using the exact same word. I think the metric does not fit the LLM scenario and this part should not have been included in the paper.", "was_revised": true, "final_justification": "I voted to accept the paper initially, while raising some points that were adequately addressed by authors. I repeat my accept vote.", "rebuttal": "Thank you for your positive review. We're glad you found the paper insightful and empirically valuable. We respond to your comments below; to summarize the main changes, we've added:\n- An experiment where users try to steer images toward stock images, and we find that the steering scores are indeed worse than in the original survey\n- More analysis of how similar the human ratings are to the automated ones\n\n> _However, the metric being heavily human-dependent, and thus expensive to compute or improve on. More work can be put into automating parts of the assessment and proving the automation does not hurt precision._\n\nThis is a great suggestion. The main metrics in the paper rely on humans as annotators and steerers. While we use humans because measuring steerability involves measuring people, as you point out, this makes the metrics harder to automate. We've included analysis below, which expands on the analysis in the appendix to compare human-based annotations to automated ones (based on CLIP and DreamSim scores). We find that the automated annotations have high correlation with the human-based ones: \n\n| Model | Average human rating (10-point scale) | Average DreamSim | Average CLIP | \n| - | - | - | - | \n| DALL-E 2 | 2.97 | 0.52 | 0.75 |\n| DALL-E-3 | 4.08 | 0.62 | 0.78 | \n| Flux-dev | 4.66 | 0.70 | 0.83 | \n| Flux-1.1-pro-ultra | 4.34 | 0.66 | 0.82 | \n| Ideogram-v2-turbo | 4.27 | 0.66 | 0.83 | \n| Photon-flash | 4.84 | 0.68 | 0.85 | \n| SD3-large | 4.45 | 0.68 | 0.84 |\n| SD3.5-medium | 4.37 | 0.67 | 0.81 |\n| SD3.5-large-turbo | 4.13| 0.65 | 0.82 |\n| SD3.5-large | 4.48 | 0.67 | 0.82 | \n| **Correlation to average human rating** | --- | 0.969 | 0.904 | \n\nWe also tried comparing human ratings to MLLM-based ratings like GPT-4o, which also found high correlation (0.67), although not as high as for DreamSim or CLIP (see response to hQSC).  \n\nReplacing human steerers with automated methods is trickier, because there is less evidence that large multimodal models can capture human behavior with high fidelity. Still, in our image steering experiments, we use a simulated human as part of an RL method to learn the images to suggest to people. While our results show that the simulated human is accurate enough to result in improvement over a baseline, we think more work is needed in order to assess whether automated steering can be a good proxy metric for human steering. \n> _While I understand the theoretic need for separating producibility from steerability, I wonder, if we repeated the same experiment with any type of goal image (e.g., a collection of stock photos), would we see the same metric values, or would we see lower satisfcation numbers because the images are not reproducible? I think this is an important comparison that would add to the paper's claims._\n\nThis is a great suggestion. Based on your suggestion, we ran an experiment during the rebuttal period where the goal images were randomly sampled stock images (so they weren't necessarily producible by the model), and users were prompted to steer DALL-E 3 toward them. Across 130 attempts, we found that the similarity scores (based on CLIP) were indeed uniformly smaller than the model's scores in the main survey (when the goal image was sampled from the model's producible set):\n\n|  | Overall average similarity | First attempt similarity | Last attempt similarity | \n| - | - | - | - |\n| Goal image is stock image | 0.727 | 0.731 | 0.726 |\n| Goal image is from model's producible set | 0.784 | 0.778 | 0.778 | \n\nFor reference, every model's CLIP score in the main survey was in the range of 0.75 - 0.85, so the stock image setting would be worse than any of the 10 models in the survey. We agree this comparison is important, and we believe it will strengthen the paper. \n\n> _I also found the LLM assessment to be somewhat lacking. LLMs are rarely used in this scenario of reproducing a text in exactly the same way, but rather to generate text in a broader context... I think the metric does not fit the LLM scenario and this part should not have been included in the paper._\n\nWe completely agree that our metrics are a more natural fit for the image models than LLMs, which is why we focus on image models. Our goal with the LLM experiment is to capture how LLMs are often used in writing: to rewrite a passage so it looks a certain way but without being able to verbalize what exactly the final text should be. We agree with your suggestion: we'll move the LLM experiments to the appendix, and spend more of the main text focusing on analysis.\n\n> _I commend you for adding standard errors in tables, please be more consistent in labeling them in the captions of all tables._\n\nThank you. We will add more labels in the captions of the tables to point to the standard errors."}, {"review_id": "cHNKjWBgq5", "reviewer": "Reviewer_WAEQ", "summary": "Evaluating generative models is an open question. Many current metrics evaluate the quality and diversity of the generated outputs. However they miss a key characteristic that the authors call \"steerability\": the ability of a model to produce an output matching the user's goal. The authors propose to evaluate steerability separately from producibility by designing a simple benchmark task: can a human steer the model to generate a specific image (that the model is known capable of producing). Overall current models have poor steerability. Alternative ways of interacting with the model, such as using image samples instead of prompt rewrites, can improve steerability.", "questions": "1. Can you clarify how the prompt rewrites are made? Is this as part of a conversation or is it always from scratch? As I understand it, for image generation, each prompt attempt is made independently (the model is reinitialized between each attempt) but for text generation, the prompt rewrites is part of a conversation (Table 4). To me, doing a conversation might pose a problem: are you sure that the producibility of a given text is the same when using a single prompt (for the goal headline) and when doing a conversation (for the attempts)? Personally I'm not sure these texts have the same producibility. Please clarify this point in the paper.\n2. Could you clarify the random image steering? Is it that you propose variations of the images with random perturbations in latent space, then the human chooses the best, you generate again variation of this chosen image, and repeat for several rounds?\n3. Note: there is an hyperlink issue line 673.", "limitations": "limitations discussed in the paper", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1. The idea to decouple producibility and steerability as characteristics of generative models is interesting and well justified.\n2. The benchmark task is simple and well executed.\n3. Good quality experiments.\n4. Proposition of methods to improve steerability.\n\n**Weaknesses**\n\n1. No discussion on the causes of poor steerability. Here is a key reference that is missing [1]. It shows that text-to-image models based on CLIP encoder (which is the case for at least Stable Diffusion, I have not checked for the other models used in the paper) share \"systematic failures\": they struggle with text containing e.g. negation and quantities. \nTo me, this explains the main results of the paper: 1. the process shown on Figures 1 and 10 would never lead to the expected result (except by chance), because by construction the generative models struggle with notions of e.g. negation and quantity, 2. the low similiarity scores and very low improvements from first attempt to last attempt (Figure 2), 3. the competitiveness of blind steering (Figure 3).\nStudying the potential causes of poor steerability would have improved the benchmark, which in its current form basically gives the humans an impossible task. For instance, it would have been useful to inform the humans about the \"systemic failures\" of generative models beforehand, to improve the effect of the prompt rewrites.\n2. Lack of a reference baseline. Because the goal is too hard, humans fail to accomplish the goal, no matter the model used (cf. Figure 2: similarities are quite low (mostly below 5 out of 10)). To me, they should not be compared to \"Perfect similarity\" but a different reference value which can be based on e.g. variations of the target image with the same prompt but different seed. It would give a better \"ideal value\" than a perfect 10/10.\n\n\n\n[1] Tong et al. (2023), \"Mass-Producing Failures of Multimodal Systems with Language Models.\"\n(https://proceedings.neurips.cc/paper_files/paper/2023/hash/5d570ed1708bbe19cb60f7a7aff60575-Abstract-Conference.html)", "was_revised": true, "final_justification": "The idea of studying the steerability aspect of generative models is interesting but I find the definition in the paper too challenging for current models. It limits many results of the paper to “steerability is hard”.", "rebuttal": "Thank you for your positive review of our paper. We're glad that you appreciated the ideas, methods, and experiments described in the paper. We respond to your comments below; to summarize the main changes, we've added:\n- New analysis that includes comparisons with different success metrics\n- Analysis of time taken as a possible cause of poor steering\n- Clarification about experimental design\n\n**We hope our comments have addressed your questions. If not, please let us know if you have any more questions we can address in the follow-up.**\n\n> _Here is a key reference that is missing [1]. It shows that text-to-image models based on CLIP encoder (which is the case for at least Stable Diffusion, I have not checked for the other models used in the paper) share \"systematic failures\"... Studying the potential causes of poor steerability would have improved the benchmark... For instance, it would have been useful to inform the humans about the \"systemic failures\" of generative models beforehand, to improve the effect of the prompt rewrites._\n\nThank you for providing the reference -- we will add it to the paper. We think it brings up an important and relevant point: one factor of poor steerability is that CLIP's representations share systematic failures. It's tough to fully determine how much CLIP factors into the failures we see because most models have not released details about whether they use CLIP, but we will add a discussion of this point to our main results.\n\nWe find that another important factor is that prompt-image alignment metrics (like CLIP) between a user's prompt and their reproduced photo is not very predictive of steerability: the correlation between CLIP and steerability scores is only 0.32. This suggests that intent is not well-captured by prompt-image alignment metrics, e.g. if many images are consistent with a single prompt or if a user has many desiderata that are infeasible to convey in a single prompt. \n\nWe've also performed more analysis focusing on the role of time: we measured effort levels by measuring how long different kinds of steering (image vs text) take. We find that not only is image steering more effective, but it's also faster than text prompting (14 minute vs 10 minute median length). This suggests one cause of poor steerability: writing careful text prompts is time-consuming, and people might get tired:\n\n| Percentile | Text steering minutes | Image steering minutes | \n| - | - | - | \n| 20th percentile | 8.6 | 7.3 | \n| 40th percentile | 10.9 | 8.8 | \n| 60th percentile | 14.1 | 11.0 | \n| 80th percentile | 16.8 | 15.3 |\n\nInforming humans of the systemic failures of T2I models is an interesting idea. We recruited professional prompt engineers -- who all had experience with T2I models and their failures -- to perform the benchmark task via Upwork. We found that professionals were only slightly better than non-professionals: final similarity scores were only 10% higher than for nonprofessionals. Moreover, we find that professional prompt engineers don't reliably improve between their first and last attempts, with even lower improvement rates than among the nonprofessional cohort. This suggests that just being aware of the failures of T2I models doesn't result in significant improvement. \n\nWe will add these analyses and more to the discussion in our paper. \n\n> _Because the goal is too hard, humans fail to accomplish the goal, no matter the model used (cf. Figure 2: similarities are quite low (mostly below 5 out of 10)). To me, they should not be compared to \"Perfect similarity\" but a different reference value._\n\nYou make a great point: users struggle to steer when they use text as a mechanism. This motivated us to look at other mechanisms, and we found that although no mechanism results in perfect performance, improvement is possible -- for example, image steering (especially with RL) shows improvement. \n\nAdditionally, the blind LLM in Figure 3 provides one kind of baseline: a steering mechanism that corresponds to randomly rewriting prompts without access to the goal image. The fact that this baseline achieves half of the human improvement helps calibrate the human performance against a baseline. \n\nStill, we don't expect users to exactly create their goal image, so we've also performed a new experiment that simulates users being equally happy with multiple goal images instead of one. Specifically, instead of measuring performance by distance to the goal image, we say that a steerer has succeeded if they produce any image within some distance. The table below shows the success rate for different thresholds:\n\n| Distance that counts as success (DreamSim) | Success rate | \n| - | - | \n| 0.50 | 0.87 |\n| 0.60 | 0.68 | \n| 0.70 | 0.41 | \n| 0.80 | 0.12 | \n| 0.90 | 0.00 |\n\nTo get more than 50% success, the threshold needs to be 0.666 DreamSim score or lower. Empirically we find pairs of images at this threshold are not very similar. Steering remains challenging even with this relaxed definition of success.\n\n\n\n> _Can you clarify how the prompt rewrites are made? Is this as part of a conversation or is it always from scratch?_\n\nThis is a good point, and our paper should have been more clear: Prompt rewrites are always from scratch. That is, a user sees their previous prompt, they're given the opportunity to edit it, and then submit it to the model. We completely agree that doing a conversation might pose a problem, which is why we have each prompt attempt made independently, for both text and image generation. We'll clarify this in the revision. \n\n> _Could you clarify the random image steering? Is it that you propose variations of the images with random perturbations in latent space, then the human chooses the best, you generate again variation of this chosen image, and repeat for several rounds?_\n\nYes this is exactly right. Importantly, we choose the same number of rounds as in text steering so that the results are comparable. We provide more details in C.2 but we'll make this more clear and move it to the main text in the revision. \n\n> _Note: there is an hyperlink issue line 673._\n\nGood catch. This should read \"Figure 3\". We'll fix it in the revision."}, {"review_id": "NFEo1Ioq7B", "reviewer": "Reviewer_hQSC", "summary": "This paper introduces a new framework for evaluating generative models by disentangling two key dimensions: producibility (what a model can generate) and steerability (how well a user can guide the model toward a desired outcome). The authors propose a benchmark task that samples an output from a model and asks users to reproduce it, enabling the measurement of steerability independently from producibility. Through large-scale user studies on text-to-image and language models, they find that current models, despite their high output quality, exhibit poor steerability. The paper further shows that steerability can be predicted using machine learning and can be significantly improved through alternative interaction mechanisms, such as image-based steering. These findings highlight the importance of steerability in real-world usability and call for a shift in how generative models are evaluated and developed.", "questions": "Human Prompting Variance in T2I Tasks:\nGiven that the T2I benchmark requires users to prompt a model based on a target image, how do the authors account for the high variance caused by differing levels of user familiarity with prompt engineering or specific model behaviors? Could the authors report or control for user background (e.g., prior experience with prompting or specific models)? A clearer characterization of user profiles and possibly stratified analysis would improve confidence in the benchmark’s robustness.\n\nLack of Iterative Editing in Steering Protocol:\nMany modern T2I workflows involve multi-round editing or prompt refinement (e.g., iterative inpainting or variation selection). Why does the benchmark focus only on one-shot or limited prompt-based steering? Have the authors considered evaluating more realistic multi-turn interaction paradigms, and if not, could they discuss potential effects this simplification may have on the reported steerability performance?\n\nEvaluation Metrics and Granularity of Assessment:\nThe use of CLIP similarity and DreamSim captures high-level alignment but may overlook fine-grained fidelity. Since steerability often depends on whether specific details (e.g., exact number of objects, spatial arrangements) can be reproduced, would the authors consider incorporating or comparing against MLLM-based evaluators (e.g., GPT-4o, Qwen2.5-VL) that could better judge localized attribute matching? Evidence that CLIP-based metrics align well with human or MLLM-based fine-grained evaluation would strengthen the current claims.\n\nNecessity of Steerability Evaluation in LLMs:\nOne broader question is whether LLMs should be evaluated for steerability in the same way as T2I models. In my view, LLMs are already commercially successful largely because of their strong steerability in natural language interactions. Therefore, the practical need to rigorously benchmark LLM steerability may be less pressing. The authors could clarify what specific failure modes or gaps in LLM usability motivated their inclusion in this study, and what new insights this part of the benchmark contributes beyond prior understanding.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n1. The paper presents a novel and valuable perspective on steerability in generative models, distinguishing it from the commonly emphasized producibility. The proposed framework and benchmark provide a concrete and actionable methodology for evaluating steerability, which is likely to inspire further research in this direction.\n\n2. The authors conduct comprehensive experiments on both state-of-the-art text-to-image models and large language models, offering a broad and reproducible analysis.\n\nWeaknesses:\n\n1. The design of the T2I (text-to-image) steerability benchmark relies heavily on users' ability to craft effective prompts. Since users are shown a target image and asked to prompt the model to reproduce it, the outcomes are significantly influenced by users' familiarity with the model and prompt engineering experience, potentially introducing high variance into the evaluation.\n\n2. Many T2I models benefit from multi-step or iterative editing to refine outputs. However, the current benchmark setup focuses on single-prompt-based steering and does not consider this interactive refinement process, which limits its realism.\n\n3. The evaluation metrics, such as CLIP embedding cosine similarity and DreamSim, largely capture global semantic alignment rather than fine-grained fidelity. Yet, effective steerability often hinges on reproducing subtle details. Incorporating vision-language models like GPT-4o or Qwen2.5-VL as evaluators might provide more nuanced and reliable assessments.", "was_revised": true, "final_justification": "The rebuttal addressed my main questions by providing additional analyses on metric–human correlation, user background effects, and the rationale for the one-shot steering setup. \n\nHowever, I still have some reservations about the GPT‑4o evaluation results, as the discrepancy with human ratings might be influenced by the prompt design for GPT‑4o. \n\nWhile these concerns remain, I find the work’s exploration of steerability to be a meaningful and timely contribution. I therefore maintain my borderline accept recommendation.", "rebuttal": "Thank you for your positive review of our paper. We're glad that you found the framework novel and valuable, and the experiments to be comprehensive and \"likely to inspire further research in this direction.\" \n\nWe respond to your comments below. **We hope our comments have addressed your questions. If not, please let us know if you have any more questions we can address in the follow-up.**\n\n> _The use of CLIP similarity and DreamSim captures high-level alignment but may overlook fine-grained fidelity. Since steerability often depends on whether specific details (e.g., exact number of objects, spatial arrangements) can be reproduced, would the authors consider incorporating or comparing against MLLM-based evaluators (e.g., GPT-4o, Qwen2.5-VL) that could better judge localized attribute matching? Evidence that CLIP-based metrics align well with human or MLLM-based fine-grained evaluation would strengthen the current claims._\n\nIt's a good idea to compare automated alignment metrics with those of humans. We've included analysis below comparing human-based annotations to automated ones (based on CLIP and DreamSim scores), which finds that the automated annotations have high correlation with the human-based ones: \n\n| Model | Average human rating (10-point scale) | Average DreamSim | Average CLIP | \n| - | - | - | - | \n| DALL-E 2 | 2.97 | 0.52 | 0.75 |\n| DALL-E-3 | 4.08 | 0.62 | 0.78 | \n| Flux-dev | 4.66 | 0.70 | 0.83 | \n| Flux-1.1-pro-ultra | 4.34 | 0.66 | 0.82 | \n| Ideogram-v2-turbo | 4.27 | 0.66 | 0.83 | \n| Photon-flash | 4.84 | 0.68 | 0.85 | \n| SD3-large | 4.45 | 0.68 | 0.84 |\n| SD3.5-medium | 4.37 | 0.67 | 0.81 |\n| SD3.5-large-turbo | 4.13| 0.65 | 0.82 |\n| SD3.5-large | 4.48 | 0.67 | 0.82 | \n| **Correlation to average human rating** | --- | 0.969 | 0.904 | \n\nCorrelations with human ratings are high for both DreamSim and CLIP. Based on your suggestion, we also considered using GPT-4o as an MLLM-based evaluator. Because of API costs, we collected similarity scores from GPT-4o using a sample of 500 responses. The average scores for the human ratings and GPT-4o ratings on this sample are below:\n\n| Model | Average GPT-4o rating | Average human rating | \n| - | - | - | \n| DALL-E 2 | 2.85 | 2.83 | \n| DALL-E 3 | 3.63 | 4.18 | \n| Flux-1.1-pro-ultra | 4.50 | 4.06 |\n| Flux-dev | 4.25 | 4.02 | \n| Ideogram-v2-turbo | 4.19 | 4.03 | \n| Photon-flash | 4.19 | 4.85 | \n| SD3-large | 4.19 | 4.54 | \n| SD3.5-large | 3.89 | 4.31 |\n| SD3.5-large-turbo | 3.55 | 4.24 |\n| SD3.5-medium | 4.74 | 4.39 |\n| - | - | - | - | \n| **Correlation to average human rating** | **0.67** | -- |\n\nSo the MLLM-based evaluation also has a strong correlation with the human ratings, but not as strong as CLIP or DreamSim. \n\n\n> _Given that the T2I benchmark requires users to prompt a model based on a target image, how do the authors account for the high variance caused by differing levels of user familiarity with prompt engineering or specific model behaviors? Could the authors report or control for user background (e.g., prior experience with prompting or specific models)?_\n\nGreat suggestion. While the crowdsourcing platform we use does not allow us to filter by prompting expertise, we also recruited professional prompt engineers to perform the benchmark task via Upwork and compared them to the nonprofessional group. We found that professionals were better than the average steerer from the nonprofessional survey, but only slightly: final similarity scores were only 10% higher than for nonprofessionals. Moreover, we find that professional prompt engineers don't reliably improve between their first and last attempts, with even lower improvement rates than among the nonprofessional cohort. We think the comparison between these two groups is quite interesting, and will emphasize it in the revised paper. \n\n\n> _Many modern T2I workflows involve multi-round editing or prompt refinement (e.g., iterative inpainting or variation selection). Why does the benchmark focus only on one-shot or limited prompt-based steering?_\n\nIt's a good idea to consider multi-round editing or inpainting. However we faced a practical challenge: most of the models were only available via their APIs. Because our goal was to compare models, we focused on features that were shared across APIs, which is why we didn't include multi-turn interactions. There are many possible steering mechanisms and we don't mean to suggest that our paper considers all of them; only the common ones that are shared across APIs. We will clarify this in the paper. \n\nTo understand whether users were struggling to steer because of lack of control to more advanced features, Table 6 in the Appendix includes an experiment where we allowed users to specify classifier-free guidance (CFG) settings in addition to text prompting. We could only test Stable Diffusion models for this because not all APIs had CFG. Users show no significant improvement with or without CFG (DreamSim similarity score, higher is better, SE in parentheses):\n\n| Attempt | Without CFG | With CFG |\n|-|-|-|\n| 1| 0.638 (0.020) | 0.664 (0.018) |\n| 2| 0.662 (0.020) | 0.677 (0.017) |\n| 3| 0.676 (0.015) | 0.667 (0.017) |\n| 4| 0.681 (0.015) | 0.703 (0.015) |\n| 5| 0.693 (0.015) | 0.682 (0.017) |\n| **Average** | 0.670 (0.008) | 0.679 (0.007) |\n\n\n> _One broader question is whether LLMs should be evaluated for steerability in the same way as T2I models... The authors could clarify what specific failure modes or gaps in LLM usability motivated their inclusion in this study, and what new insights this part of the benchmark contributes beyond prior understanding._\n\n\nOur goal with the LLM experiments is to capture one frequent use-case for LLMs in writing: to rewrite a passage for a user who can identify the style they want when they see it, but is unable to articulate what exactly that style is. For example, a student wishing to use an LLM to edit academic, technical writing may not get what they're looking for if they prompt an LLM to provide edits that match \"academic, technical writing\". However, even if they don't know exactly how to describe the writing they want, they are usually able to identify the examples they're happy or unhappy with. Our results found that this aspect of steerability is difficult for LLMs. We think this results adds to the conversation about LLM steerability: like you suggested, there are many uses where LLMs might be steerable (e.g. solving math problems), but in this focused aspect of writing to match a certain tone, they are less steerable. However, we don't aim to suggest that writing/editing is the only use-case for LLMs, and we will rewrite that section to emphasize these points."}, {"review_id": "MlQNebMB0E", "reviewer": "Reviewer_k56Q", "summary": "The paper proposes a formal decomposition that separates a generative model's producible set from its steerability gap, gives a human-in-the-loop benchmark that samples a model's own output as a goal and asks users to reproduce it, and reports large-scale user studies on 10 text-to-image systems and 5 LLMs. Empirically, users fail to reach the targets: annotators judge 60% of image attempts unsatisfactory and prompt refinement helps only 62% of the time. For LLMs, \"very satisfied\" ratings drop to 17%. A CLIP-based predictor explains roughly half of the variance in steerability, and a simple \"image-steering\" interface that proposes visual variations yields more than a two-fold improvement over text prompting.", "questions": "1. I need clarity on the benchmark protocol. You cap each reproduction attempt at five prompts and forbid negative prompts or ControlNet style constraints. Why were those particular limits chosen, and how sensitive is the failure rate to relaxing them? A small ablation that doubles or halves the attempt budget, or that enables commonly used guidance knobs, would establish whether the headline 60 percent unsatisfied rate is intrinsic or policy driven.\n2. The LLM study forbids any lexical overlap with the target text. That constraint is not typical of real usage, where reusing phrases is often encouraged to preserve semantics. Could you report satisfaction scores when overlaps are allowed, or justify why the overlap prohibition models an important practical scenario?", "limitations": "discussed above", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Isolating steerability from producibility is conceptually useful; related work has evaluated controllability but typically conflates the two dimensions. The benchmark is sufficiently large to matter and the released data could become a reference point. That said, the decomposition is mathematically straightforward once the goal is stated, and the benchmark design (i.e., reproduce the model's own sample) is a narrow proxy for real user intent. Compared with recent evaluation surveys, the contribution is incremental on the measurement side and modest on the algorithmic side.\nThe framework is sound but largely descriptive; no theoretical guarantees or identifiability claims are offered. The predictor uses CLIP embeddings, yet CLIP correlates only 0.32 with human judgments, so relying on it to partition gains risks substantial error propagation. The experimental protocol omits potentially decisive variables: random seeds are hidden, negative prompts and control-net style constraints are disabled, and only five attempts are allowed. These design choices may artificially depress steerability and inflate the headline 60% failure rate. The LLM study forbids using words from the goal text, a restriction that is unlikely in real usage and may explain the low 17% satisfaction figure. No ablations quantify the sensitivity of results to these constraints.\nThe user studies are large, but the statistical treatment is thin. Aside from standard errors, there is no modelling of worker or item-level variance, no correction for repeated-measures correlations, and no preregistered stopping rule. The claim that image steering \"more than doubles\" improvement is true on DreamSim deltas, yet the baseline improvement is itself tiny, so the practical effect size is unclear. The blind-LLM baseline achieves 52% of human improvement with no goal awareness, implying that the benchmark rewards prompt lottery effects rather than systematic steering; this undercuts the core thesis.\nThe authors collected 18 k human ratings but provide no analysis of demographic effects or annotation consistency. Asking workers to replicate potentially copyrighted output raises legal issues. Improving steerability may also facilitate malicious uses; this is acknowledged only briefly.", "was_revised": true, "final_justification": "I've updated my score to a borderline accept. The rebuttal was clear and the authors acknowledged and correctly responded to all my questions.", "rebuttal": "Thank you for your insightful review. Your comments were very constructive and have improved the paper. To summarize the main changes, we've added:\n- Analysis for different numbers of allowed rewrite attempts\n- Further comparison of CLIP/Dreamsim to human metrics\n- Clarification about experimental design\n\n> _You cap each reproduction attempt at five prompts and forbid negative prompts or ControlNet style constraints. Why were those particular limits chosen, and how sensitive is the failure rate to relaxing them? A small ablation that doubles or halves the attempt budget, or that enables commonly used guidance knobs, would establish whether the headline 60 percent unsatisfied rate is intrinsic or policy driven._\n\nIt's a good idea to look at how sensitive results are to the number of attempts. We've added results below across different numbers of prompts, which we'll add to the paper: \n\n| Attempt | Percent unsatisfied | Dreamsim score (higher is better) | \n| - | - | - | \n| 1 | 0.64 (0.02) | 0.639 (0.008) | \n| 2 | 0.62 (0.02) | 0.661 (0.007) | \n| 3 | 0.56 (0.02) | 0.683 (0.007) |\n| 4 | 0.58 (0.02) | 0.684 (0.007) |\n| 5 | 0.60 (0.02) | 0.695 (0.007) |\n\nFor all of the number of rounds, unsatisfactory rates are within 4% of the headline result (60% dissatisfaction). The paper considered five attempts because we found only marginal improvement after the third attempt. We also feel that five is a realistic number of times a user would try rewriting prompts. We completely agree that longer time horizons (e.g. multiple days of weeks) would be interesting, and we don't mean to suggest that people cannot improve over time. We'll emphasize these points in our revision. \n\nWe focused on features that were shared across APIs, which is why we didn't include negative prompts or ControlNet style constraints. We did try an experiment (Table 6 in the appendix) where we allowed users to specify classifier-free guidance (CFG) settings in addition to text prompting. We could only test Stable Diffusion models for this because not all APIs had CFG. Users didn't show significant improvement with or without CFG (DreamSim similarity score, higher is better, SE in parentheses):\n\n| Attempt | Without CFG | With CFG |\n|-|-|-|\n| 1| 0.638 (0.020) | 0.664 (0.018) |\n| 2| 0.662 (0.020) | 0.677 (0.017) |\n| 3| 0.676 (0.015) | 0.667 (0.017) |\n| 4| 0.681 (0.015) | 0.703 (0.015) |\n| 5| 0.693 (0.015) | 0.682 (0.017) |\n| **Average** | 0.670 (0.008) | 0.679 (0.007) |\n\nWe think testing more advanced features would be interesting. These mechanisms are all amenable to the framework in the paper, and we hope that future work will study additional features. \n\n> _The predictor uses CLIP embeddings, yet CLIP correlates only 0.32 with human judgments, so relying on it to partition gains risks substantial error propagation._\n\nThanks for bringing this up. We wanted to clarify a potential confusion: the 0.32 CLIP correlation shows that the CLIP score between a user's prompt and the resulting image has a low correlation with the steerability score for that image. This suggests that CLIP scores can be high with poor steerability, e.g. if many images are consistent with a single prompt or if a user has many desiderata that are infeasible to convey in a single prompt. Meanwhile, the predictor in the decomposition in Section 4 uses CLIP not as a score but as an embedding to a larger model and uses it not for the user's reproduced image but for the goal image (the goal is to predict how steerable a given goal image is, so it's not a function of reproduced images). We find that these models achieve an R^2 of almost 0.50, explaining almost half of the variance in poor steerability. We realize the writing wasn't as clear as it should've been, and we'll update this in the main paper. \n\nWhen we use CLIP elsewhere in the paper, it's to compare its scores to human scores. We agree that it would be risky to rely on CLIP as a proxy for humans without benchmarking, so we use human annotations for our main experiments in Section 3. We've also included analysis below (which expands on results in the appendix) to compare human-based annotations to automated ones (based on CLIP and DreamSim scores), which finds that the automated annotations have high correlation with the human-based ones: \n\n| Model | Average human rating (10-point scale) | Average DreamSim | Average CLIP | \n| - | - | - | - | \n| DALL-E 2 | 2.97 | 0.52 | 0.75 |\n| DALL-E-3 | 4.08 | 0.62 | 0.78 | \n| Flux-dev | 4.66 | 0.70 | 0.83 | \n| Flux-1.1-pro-ultra | 4.34 | 0.66 | 0.82 | \n| Ideogram-v2-turbo | 4.27 | 0.66 | 0.83 | \n| Photon-flash | 4.84 | 0.68 | 0.85 | \n| SD3-large | 4.45 | 0.68 | 0.84 |\n| SD3.5-medium | 4.37 | 0.67 | 0.81 |\n| SD3.5-large-turbo | 4.13| 0.65 | 0.82 |\n| SD3.5-large | 4.48 | 0.67 | 0.82 | \n| **Correlation to average human rating** | --- | 0.969 | 0.904 | \n\n> _The claim that image steering \"more than doubles\" improvement is true on DreamSim deltas, yet the baseline improvement is itself tiny, so the practical effect size is unclear._\n\nWe agree that the baseline improvements look small in DreamSim score, and that DreamSim units can be hard to interpret. For a more interpretable metric, note that the improvement rate for text steering is ~55% compared to ~72% for image steering. This is an improvement from 5% over chance to 22% over chance, which we feel is sizable. \n\n> _The LLM study forbids any lexical overlap with the target text. That constraint is not typical of real usage, where reusing phrases is often encouraged to preserve semantics. Could you report satisfaction scores when overlaps are allowed, or justify why the overlap prohibition models an important practical scenario?_\n\nOur goal with the LLM experiments is to capture one frequent use-case for LLMs in writing: to rewrite a passage for a user who can identify the style they want when they see it, but is unable to articulate what exactly that style is. For example, a student wishing to use an LLM to edit academic, technical writing may not get what they're looking for if they prompt an LLM to provide edits that match \"academic, technical writing\". However, even if they don't know exactly how to describe the writing they want, they are usually able to identify the examples they're happy or unhappy with when they see them. Our results found that this aspect of steerability is difficult for LLMs. We think this results adds to the conversation about LLM steerability: like you suggested, there are many uses where LLMs might be steerable (e.g. solving math problems), but in this focused aspect of writing to match a certain tone, they are less steerable. However, we don't aim to suggest that writing/editing is the only use-case for LLMs, and we will rewrite that section to emphasize these points. \n\n> _The blind-LLM baseline achieves 52% of human improvement with no goal awareness, implying that the benchmark rewards prompt lottery effects rather than systematic steering._\n\nThis is a great point. The purpose of the blind LLM is to serve as a baseline. We want to clarify that the blind LLM does not do well, and it helps calibrate human improvement, which is already small. This could be seen as analogous to image classification experiments, where an image classifier that always predicts the majority class is included to show how well an uninformative model can perform. We feel the LLM is a useful baseline, but another option is to limit the number of human attempts to 1, so there is no lottery effect. This is an important point and we'll add it to our discussion. \n\n> _The authors collected 18 k human ratings but provide no analysis of demographic effects._\n\nWhile the data we recorded does not include demographics, we also recruited professional prompt engineers to perform the benchmark task via Upwork and compared them to the nonprofessional group. We found that professionals were better than the average steerer from the nonprofessional survey, but only slightly: final similarity scores were only 10% higher than for nonprofessionals. Moreover, we find that professional prompt engineers don't reliably improve between their first and last attempts, with even lower improvement rates than among the nonprofessional cohort.\n\n> _That said, the decomposition is mathematically straightforward once the goal is stated, and the benchmark design (i.e., reproduce the model's own sample) is a narrow proxy for real user intent._\n\nWe completely agree that the decomposition is mathematically straightforward. We feel this simplicity is a strength: a simple decomposition is easier to interpret than a more complicated formula. You're also correct that the goal image is sampled from the same model used for steering. As discussed in Section 2, this ensures that steerability is independent of producibility: if a user can't reproduce the goal image, it's not about the quality of the model's images, just its steerability. \n\n> _Asking workers to replicate potentially copyrighted output raises legal issues. Improving steerability may also facilitate malicious uses; this is acknowledged only briefly._\n\nThanks for raising this concern. We designed the benchmark with the goal of avoiding copyright issues: each image used in the benchmark is generated by a text-to-image model, and is not drawn from any external, copyrighted dataset. Still, as with any use of text-to-image models, it's possible that a model may produce a copyrighted image, and we will bring this up in our discussion. On the broader question of potential misuse, we agree that improved steerability could be dual use. That said, we believe that understanding and quantifying steerability is an important prerequisite for responsible deployment and mitigation planning. We'll expand on these points in our revision."}]

Author rebuttal:

Meta-review: The paper argues that evaluating generative models should separate producibility from steerability, with the latter defined as how well can human guide the model to produce a specific result, as opposed to what the model can generate. It proposes a simple, general benchmark: sample an output from a model’s own producible set and ask users to steer the same model to reproduce it. The authors introduced a framework of human assessment for both image and text generation. Through large-scale user studies, they find that only around half the time humans are satisfied with the results or find them similar to the intended goal. Further, the paper shows that steerability can be predicted using CLIP-based predictor and can be improved through the use of image samples instead of prompt rewrites.

The work is original in its steerability focus and framework, and practically relevant. The benchmark is simple yet revealing, the human studies are large, and the proposed image-steering mechanism shows meaningful gains. The author addressed many important points in the rebuttals, especially on the meaningfulness of the benchmark, clarity of protocol, evaluation, variance of user familiarity with prompting,. While there are still some room for improvements, I believe that the papers’ contribution on the timely focus, formulation, framework, evaluation protocols, and its actionable insights would be valuable to the community.

__Strengths__
- Isolating steerability from producibility is sound, useful, interesting, and timely
- The proposed framework and benchmark are simple, actionable, sufficiently large, and could become a reference point
- Experiments are comprehensive and showing the true gap of following all user instructions
- The authors further showcase additional method of using images to steer to achieve higher results in steerability

__Weakness__
- The framework's definition of steerability is too challenging to be meaningful. In the rebuttal, the authors propose a relaxed, automated measure, and the resulting image pairs still appear insufficiently similar. While I agree that this dissimilarity might signify that steering remains challenging, it could be that the dissimilarity is due to the fact that the DreamSim measurement is not emphasizing fine-grained fidelity, thus limiting the relaxation irrelevant to the steerability issue. Exploring a more suitable relaxation—e.g., via MLLM-based evaluation—could enhance the impact of this work.
- The evaluation relies heavily on human prompting, which introduces variance. In addition, the utilization of the detailed PixelProse captions in the framework, also impose strong prompting requirements. This suggests that the current benchmark still leaves room for future improvements, e.g. on limiting the human prompting variance by rendering the prompting requirement constrained.
Decision: Accept (poster)