Title: What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: evaluation

human-in-the-loop

steerability

generative models
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

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.

1 Introduction

There is a wedge between how we evaluate generative models and how we intend to use them. For example, a common way to evaluate image generation models is to measure the quality of outputs they can generate, e.g. by measuring how realistic or diverse the images they produce are [70, 32, 46]. But when these models are used by people, their success depends on more than just what they're 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 a specific end goal guide a model to 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 steerability and producibility: if a model doesn't produce the image a user wants, is it because it's incapable of producing it or because it can't be steered? Second, measuring steerability requires knowing a human user's goals, which are often latent or difficult to articulate.

We first provide a 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 a human may want), and a steerability term (how well can models be steered by humans towards the best output they're capable of producing).

We then propose a 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 a variety of generative models. For example, for

Sendhil Mullainathan MIT

A book shelf with 3 blue

books on the left side

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right side with a wooden

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Attempt 1

A book shelf with 3 blue

books on the left side

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There are only 3 books

on each side,

DO NOT

put more than 3 total

books.

Attempt 2

Attempt 3

Attempt 1

Attempt 2

Attempt 3

Other users

(see appendix for prompts)

Goal image

Example user

text-to-image models, it calls for showing humans an image sampled from a model and then instructing them to prompt that same model to reproduce the image. This procedure addresses the two key challenges: first, as suggested by the framework, it evaluates a generative model by how well humans can steer it towards an output it is capable of producing . Second, to overcome the lack of access to a user's goal, it induces a goal by instructing users to get as close to as possible to a provided output.

We implement this benchmark in a large-scale user study for two domains: text-to-image models and 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 not reliably improve outputs; after five tries, the final image is closer to the goal image only 62% of the time (compared to a base rate of 50%).

Like many metrics, it is easy to optimize for steerability alone. However, this could come at the expense of producibility. To assess whether differences between models reflect true steerability improvements, we provide another mathematical decomposition that disentangles gains from better steering mechanisms and gains from producibility differences. This decomposition requires a predictive model of steerability; we show that steerability can be predicted with machine learning methods, and use it to analyze models, finding that steerability differences reflect both true improvements in steering mechanisms along with differences in producibility.

Finally, we show that the problem of poor steerability is addressable. We consider a simple steering mechanism that uses two ingredients: First, after an initial text prompt, we enable users to steer via suggested images rather than by prompt rewrites. Second, rather than suggesting images randomly, we use an auxiliary model to suggest images. Despite the simplicity of this technique, it achieves more than 2x improvement over text steering.

2 Framework

We define a generative model 𝑚 over a domain 𝑋 to have two components: a producible set 𝑆 𝑚 ⊆ 𝑋 and a steering mechanism that allows humans to produce an instance in 𝑆 𝑚 . For example, the producible set of a 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. 1

How do humans interact with the generative model? We assume that a 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 a family of reward functions R where each 𝑟 ∈ R is a function over instances 𝑟 : 𝑋 → R . Each use case is defined by a single reward function.

A human interacts with a generative model with the goal of producing an output that maximizes their reward. We define ℎ ( 𝑚,𝑟 ) ∈ 𝑆 𝑚 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 function ; it describes how someone with a specific reward function would steer a model to maximize it. 2 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.

Ahuman's reward when using a generative model is 𝑟 ( ℎ ( 𝑚,𝑟 )) . This quantity describes how effective a generative model is under human interaction. However, it blends a model's steerability with the quality of its producible set 𝑆 𝑚 . To see this, we introduce additional notation to decompose the reward. For a set of instances 𝐴 , define 𝑟 ∗ 𝐴 as the maximum reward when constrained to instances in 𝐴 :

Denoting by 𝑟 ∗ 𝑋 the maximum reward over all possible instances, the largest possible reward for a given reward function is 𝑟 ∗ 𝑋 . A model's efficacy can be summarized by the gap between this largest possible reward and the model's reward: 𝑟 ∗ 𝑋 -𝑟 ( ℎ ( 𝑚,𝑟 )) . This gap can be decomposed:

The first term, 𝑟 ∗ 𝑋 -𝑟 ∗ 𝑆 𝑚 , 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 capable of producing .

The decomposition in Equation 2 describes a single reward function; in practice, a 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 𝑟 ∗ 𝑋 = 𝑟 ∗ 𝑇 for all 𝑟 ). This decomposition reveals a couple of results. The first is that with perfect steering, a model's performance can simply be evaluated by comparing the producible set 𝑆 𝑚 to the set of instances people may want to produce, 𝑇 . However, with imperfect steering, a model can in principle be capable of producing all instances humans may want (i.e. 𝑆 𝑚 = 𝑇 ), and yet produce images ℎ ( 𝑚,𝑟 ) with arbitrarily bad reward when steered by a human. Thus, both terms are needed to benchmark model quality under human use.

Many existing ML 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 steerability, which relies on human reward functions that may be unknown. Recent benchmarks have been proposed for evaluating the success of human-AI interactions [48, 39, 81]. While important for understanding overall efficacy, these benchmarks combine producibility and steerability into a single metric. Decomposing these effects is 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 a model toward the highest-reward instances it is capable of producing.

Evaluating a model's steerability is challenging because we often don't have access to human reward functions. Even if we did have access to reward functions, evaluating the gap by finding the instance in a model's producible set 𝑆 𝑚 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 , we first sample an instance 𝑥 𝑔 ∼ 𝑃 from a distribution 𝑃 over the model's producible set 𝑆 𝑚 . We refer to 𝑥 𝑔 as the goal instance . To induce a reward function, we show a human the goal instance 𝑥 𝑔 and instruct them to use the generative model to reproduce it. Specifically, we instruct them to generate an instance as close as possible to the goal instance 𝑥 𝑔 , as judged by other humans. In this sense, we're inducing an ideal point reward function [20]:

dall-e-2

dall-e-3

sd3.5-large-turbo

flux-1.1-pro-ultra

sd3.5-medium

sd3-large

sd3.5-large

flux-dev

photon-flash

0

2

4

6

8

10

Similarity to goal image

Perfect similarity

All attempts

dall-e-2

dall-e-3

sd3.5-large-turbo

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sd3.5-medium

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sd3.5-large

flux-dev

photon-flash

0

2

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6

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10

Perfect similarity

First attempt

Last attempt

where 𝑑 (· , ·) ∈ R + is the distance function as judged by humans (with the property that 𝑑 ( 𝑥, 𝑥 ) = 0). Therefore, for a given goal instance 𝑥 𝑔 , the steerability gap is

where the equality is due to the fact that the goal instance 𝑥 𝑔 ∈ 𝑆 𝑚 . Finally, to evaluate the steerability gap, we ask separate human annotators to rate the similarity of 𝑥 𝑔 and ℎ ( 𝑚,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-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 a blind steering mechanism that generates edits of a 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 text-to-image models. These include models, such as Stable Diffusion [23], that are prompted via text to generate images. We refer to this type of steering 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).

We study 10 text-to-image models. We consider four variants of the Stable Diffusion models: SD3-large, SD3.5-medium, SD3.5-large, and SD3.5-large-turbo [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-1.1-pro-ultra [6], Ideogram-v2-turbo [37], and Photon-flash [57]. We use publicly available APIs for each model: Stability AI for the stable diffusion models, the OpenAI 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 a 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.

able 1:

Model

Imp

POM-1

POM-5

DALL-E 2

0.60 (0.04)

0.64 (0.06)

0.72 (0.05)

DALL-E 3

0.58 (0.04)

0.56 (0.07)

0.52 (0.06)

Flux-dev

0.66 (0.04)

0.48 (0.07)

0.64 (0.06)

Flux-1.1-pro-ultra

0.54 (0.05)

0.49 (0.07)

0.55 (0.06)

Ideogram-v2-turbo

0.61 (0.04)

0.46 (0.07)

0.70 (0.05)

Photon-flash

0.74 (0.04)

0.45 (0.07)

0.56 (0.06)

SD3-large

0.51 (0.05)

0.52 (0.07)

0.52 (0.06)

SD3.5-medium

0.66 (0.04)

0.64 (0.06)

0.68 (0.05)

SD3.5-large-turbo

0.69 (0.04)

0.56 (0.06)

0.61 (0.06)

SD3.5-large

0.63 (0.04)

0.54 (0.07)

0.68 (0.05)

Average

0.62 (0.04)

0.54 (0.07)

0.62 (0.06)

Steerability is poor for all models in our benchmark. Imp shows Improvement Rate and POM-1 and ost of the models we consider rely on random seeds to generate images; the same prompt with

andom seed used to generate the goal image. This reflects how these models are used in practice: if model is able to generate an image a user wants, the user typically won't know the specific seed(s) ecessary to generate that image. In Appendix A, we repeat our main experiment but allow users to hoose random seeds in addition to prompts, finding no significant change in steering performance. e consider the following metrics for judging how close steerers come to their goal. All metrics use 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.

ifferent random seeds may result in different images. In our surveys, we don't provide users with the eparate human annotators from the humans who perform the steering:

•

•

•

Satisfaction rate : For each image produced by a human steerer, we ask annotators to rate how satisfied they'd be if, in trying to generate the corresponding goal image, they instead got the one produced by human steering. We use a four-point scale: 4 is very satisfied, 3 is somewhat satisfied, 2 is somewhat unsatisfied, and 1 is very unsatisfied. Image Similarity Rating : A rating on a 10-point scale for how similar a given image is to the goal image (with 10 being the most similar). Improvement Rate (Imp) : The percent of the time human raters judge the 5th attempted generated image to be closer to the goal image than the first attempt. For each comparison, we take the majority over 3 human raters. See Figure 20 for a visual summary of improvement 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 on a 10-point scale. Improvement rate (Imp) is the percent of the time human raters judge the last attempted generated image to be closer to the goal image than the first attempt. Prompt-Output 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. In other words, POM measures the frequency with which the model's generated output fails to match what a human would reasonably expect from their prompt for recreating a 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.

•

prompt for recreating a 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 prompt-output misalignment. e recruit survey participants on the Prolific platform [67]. We received an IRB review and xemption for this study. For all of our surveys, we paid respondents an implied rate of $12.50-$13.50 er hour, and the median survey completion time ranged from 9-15 minutes across tasks. See ppendix C for more survey details. In total, we collect data for 554 goal images, resulting in 2,770 Results. We find that steerability is poor across models. Annotators rate the attempted reproductions as unsatisfactory 60% of the time; moreover, 27% say they'd be 'very unsatisfied' with the attempted reproductions compared to only 10% saying they'd be 'very satisfied'. The image similarity results on a 10-point scale are depicted in Figure 2. The best model is Photon-flash, while the worst model is DALL-E 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 1 and Figure 11 for examples of steering attempts.

rate. Prompt-Output Misalignment (POM) : The percent of the time that human judges deem human steering prompts as better descriptions of the goal image than of the image actually generated by the prompt. In other words, POM measures the frequency with which the model's generated output fails to match what a human would reasonably expect from their 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-$13.50 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 a total of 18,550 ratings across the four metrics. We release all of the data we collect. 3

otal (goal image, generated image) pairs across 277 different survey-takers. We collect a total of 8,550 ratings across the four metrics. We release all of the data we collect in hopes that it will ead to further evaluation and improvement of model steering. esults. We find that across the board, model steerability is poor. Annotators rate the attempted eproductions as unsatisfactory 60% of the time; moreover, 27% say they'd be 'very unsatisfied' with 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 5th attempt ranked as more similar to the goal image than images generated by a 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

he attempted reproductions compared to only 10% saying they'd be 'very satisfied'. The image he worst model is DALL-E 2. In general, there is not much difference between different-sized models

xamples of steering attempts.

n the same family, although larger ones perform marginally better. See Figure 1 and Figure 11 for e also analyze prompt-output misalignment (POM) in Table 1. Recall that POM-1 measures the

raction of the time that the first prompt a user attempts is rated as a better description of the goal

only slightly outperform 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 to try out different prompts. To quantify this, we 'blindly' generate edits of a 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 a blind LLM have the same number of attempts to rewrite a 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.1.

Because our metrics are based on human annotations, they might be subject to human variability. In Appendix B, we repeat our study using model-based 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 a single prompt or if a user has many desiderata that are infeasible to convey in a 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)alignment empirically. Figure 6 plots the relationship between each attempt's prompt-image alignment (measured via CLIP) and steerability score. The correlation is low: 0.32. While poor prompt-image alignment is a factor for poor steerability, it cannot fully explain it.

4 Comparing producibility and steerability

One way to artificially improve a 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 a way to assess whether steerability improvements reflect genuine improvements in steering mechanisms as opposed to differences in producibility?

We first study a setting where there is a 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.1 demonstrates a tradeoff: as the model becomes more constrained, it's easier to steer, but less capable of producing. While there's a 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-II 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 image-generation models 𝑀 1 and 𝑀 2 with average steerability rewards 𝑅 1 and 𝑅 2 . Here we expand on the notation in Section 2 by considering each model's distribution over producible images: 𝑝 1 ( 𝑥 ) and 𝑝 2 ( 𝑥 ) . 4 Each model also has a steering mechanism that results in some expected reward for each image being reproduced: denote by 𝜇 𝑖 ( 𝑥 ) the average reward a user whose goal is to produce image 𝑥 receives when using 𝑀 𝑖 . The difference in steerability between the two models can be expressed as:

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

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/bracehext when steerability is artificially improved by constraining all images to come from the same random seed.

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/bracehext cannot be attributed to differences in the steering mechanism; only the producible set is changing.

mechanism alone.

Meanwhile, if the difference is dominated by the second term, improvement

(See Appendix A for more details and a derivation.)

Estimating the terms in Equation 5 involves training a predictor image and the user's prompt (we never use the images produced by the prompts). We split the data

8

Model

/u1D445

2

MSE

Baseline

0.000 (0.000)

1.061 (0.000)

Model only

0.082 (0.025)

0.974 (0.071)

Model + prompt

0.459 (0.040)

0.543 (0.034)

Model + image + prompt

0.493 (0.035)

0.508 (0.033)

ˆ

/u1D707

/u1D456

(

/u1D44B

)

to predict steerability from

inputs for each image-generation model. How well can ML methods predict steerability? We consider

four possible predictive models: a baseline using only the model being steered as a feature, a model

also using the reference image, a model using a user's prompt, and a model using both the reference

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collected in Section 3 into 80/20 train/test splits.

To predict steerability, we encode the prompts quite predictable: the highest-performing model can explain nearly half of the variance in steerability

scores. Still, we imagine that more sophisticated prediction methods can do even better. We release all data in hopes that predicting steerability can be a new kind of benchmark.

3

and corresponding user prompt.

Here we consider each

cannot be attributed to differences in the steering mechanism; only the producible set is changing. While Equation 5 isn't a causal quantity, it 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 a predictor ˆ 𝜇 𝑖 ( 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, a 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 3 into 80/20 train/test splits. To predict steerability, we encode the prompts and/or images with CLIP and fine-tune CLIP layers and a regression head to predict DreamSim scores from the resulting embeddings and a one-hot encoding of the model name. The training and analyses were performed on a single A100 GPU.

Figure 4 shows the predictive performance of each model on held-out data. We find that the highest-performing model can explain nearly half of the variance in steerability 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 DALL-E 2 are easier for people to steer to regardless of the model.

5 Steerability can be improved

The results in Section 3 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.

)

.

/u1D451/u1D465

(4)

(5)

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 a 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 a current image 𝑥 , new images 𝑥 ′ 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 trying to generate. So we also consider a second approach, which learns a steering distribution that suggests a set of images likely to improve steering (independently of the goal image). We use a simple RL 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 a domain where human articulation is more difficult, we consider images generated from prompts about abstract geometric patterns. Specifically, we prompt Claude-3.5-Sonnet 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 a 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 is large enough to produce high-quality images but small enough to enable efficient image generation given our academic-level 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 a large body of existing work focused on evaluating and improving human-AI interaction. For example, Lee et al. [48] develop a framework for evaluating human-language model interaction, and further research explores gaps in current human-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 so 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 a goal image, a 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, Zamfirescu-Pereira et al. [85] conduct a user study finding that non-AI experts struggle to make systematic progress in prompt design with chatbots. While these evaluation frameworks are important for understanding overall efficacy, they combine producibility

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 non-interactive experiments [54, 62, 28, 69, 35], some of which have found systematic failures (e.g. due to shared CLIP encodings) [78]. Other evaluation metrics 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 as 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 to 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 to our framework and test; while we find that image steering outperforms text steering in our surveys, we do not attempt to suggest it 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 to 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 to 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 to adjust model behavior, their goals are different; RLHF is designed to align model outputs with human preferences, while our technique is 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 image-generation models to produce certain goal images. A 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.

7 Conclusion

This paper introduced a mathematical framework and benchmark for evaluating the steerability of generative models. We implemented the benchmark, which revealed shortcomings in text-to-image models. Wealso showed steerability 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, we focused on 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 a 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 to 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 time-horizons, e.g. over the course of months.

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. Moreover, improved steerability could also facilitate malicious uses. However, understanding and quantifying steerability is an important prerequisite for responsible deployment and mitigation planning.

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Model Rankings by Human vs.Automated Image Similarity Metrics

Rank(1=lowest score)

10

1

HumanRating

DreamSim

8

CLIP

9

4

2

dall-e-2

dall-e-3

sd3-large

flux-dev

sd3.5-large

sd3.5-medium

photon-flash

sd3.5-large-turbo

flux-1.1-pro-ultra

Model

A Derivation of Equation 5.

Here we derive Equation 5. Consider two models, 𝑚 1 and 𝑚 2 , and denote each model's distribution over producible instances: 𝑝 1 ( 𝑥 ) and 𝑝 2 ( 𝑥 ) . Each model also has a steering mechanism that results in some expected reward for each image being reproduced: denote by 𝜇 1 ( 𝑥 ) the average reward a user whose goal is to produce image 𝑥 receives from model 1, with 𝜇 2 ( 𝑥 ) the analogous quantity for model 2. Formally, 𝜇 𝑖 ( 𝑥 ) = E [ 𝑟 𝑋 ( ℎ ( 𝑀,𝑟 𝑋 ))| 𝑋 = 𝑥, 𝑀 = 𝑚 𝑖 ] , where the reward function is subscripted by the goal instance 𝑥 to make its dependence explicit. To simplify notation, we will define 𝑆 𝑚 ( 𝑥 ) = 𝑟 𝑥 ( ℎ ( 𝑚,𝑟 𝑥 )) , so that 𝜇 𝑖 ( 𝑥 ) = E [ 𝑆 𝑀 ( 𝑥 )| 𝑀 = 𝑚 𝑖 ] .

Denote by 𝑅 1 the average steerability for model 1 and 𝑅 2 the analogous quantity for model 2. By the law of iterated expectation,

Therefore, we can write

Add and subtract the 'cross-term' ∫ 𝑝 2 ( 𝑥 ) 𝜇 1 ( 𝑥 ) 𝑑𝑥 :

B Additional results

In this section we present additional analyses of our steerability benchmarks for text-to-image models and LLMs.

B.1 Comparing human-judged metrics to automatic metrics.

For our main experiment, we use human ratings to evaluate model similarity. In Table 2 we consider algorithmic similarity metrics: DreamSim [25] and CLIP [31]. As shown in Figure 5, we find that the DreamSim and CLIP image similarity metrics are relatively aligned with human ratings.

Prompt-image alignment vs. steerability

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B.2 LLMsteerability.

We also apply our framework to evaluate the steerability of large language models (LLMs). Just as in image generation, we provide users a goal instance - a piece of text - and instruct them to iteratively 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-flash [77], Gemini2.0-flash-exp [27], GPT-4o [36], Claude-3.5-Sonnet [1], and Llama-3.3-70B-Instruct-Turbo [22].

For each LLM, we sample a goal text from its producible set by prompting it to rewrite a news headline from the Kaggle News Category dataset using a particular style and tone [59, 60]. We provide users with both the original and goal headlines, and instruct them to prompt the LLM to rewrite the original headline in 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 5 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 of LLMs: satisfaction rate (a 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% in its margin of error, indicating that improvements over steering attempts occur only half the time.

In Table 3 we show complete results from this experiment. Table 4 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 2 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 a 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

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 1 to 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 in unrealistic images. We recommend users a 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.

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 on non-experts and prompt engineers. Our main experiment was conducted by non-experts on the Prolific platform, so we repeat our experiment with 5 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 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, we prompt an LLM to blindly

steer image generation models. Specifically, we randomly sample a set of 100 human steering traces from our prior experiment, each of which contains a goal image and five prompts and generated images, corresponding to the human's 5 steering attempts. To have an LLM blindly perform steering, we prompt GPT-4o to produce variations of the user'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 4 variations of a user'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 to 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 to 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 is 0. 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 3 but constrained the goal image and all user-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 to 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 steerability 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 producible set is an optimization challenge, because it requires a combinatorial optimization over possible prompts. Instead, we approximate this optimization by searching over random variations of the prompt used

0.66

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Constrained

to 1 seed

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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 to 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 7.

C.2 Reinforcement learning.

In this section, we 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 𝑧 ∈ R 𝐷 is a vector of random noise concatenated with a prompt embedding. One possible steering distribution is to

1

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3

4

5

Attempt Number

0.00

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Improvement over first attempt

Text Steering

Image Steering

Image Steering (RL)

sample 𝜖 ∼ N( 𝜇, 𝜎 2 ) with 𝜖 ∈ R 𝐷 and then to decode a new image 𝑥 ′ = 𝑓 ( 𝑧 + 𝜖 ) . 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 trying to generate. For example, consider two 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 as a steering distribution, we propose a reinforcement learning (RL) technique that learns a steering distribution in order to maximize human steering capabilities. Specifically, we parameterize the steering distribution 𝑞 𝜙 ( 𝑥 ′ | 𝑥 ) with parameters 𝜙 . Slightly modifying notation from Section 2 to make the steering function's dependence on 𝑞 𝜙 explicit, denote by ℎ ( 𝑚,𝑟 | 𝑞 𝜙 ) ∈ 𝑆 𝑚 the instance that is produced when a human with reward function 𝑟 interacts with a model 𝑚 with steering distribution 𝑞 𝜙 . The goal is to optimize

We consider learning a simple steering distribution that suggests image variations as a function of the human steerer's behavior. We consider learning the optimal size of the perturbation. Specifically, we define the steering distribution as 𝑞 ( 𝑥 ′ | 𝑥, 𝑡, 𝑠 ) , where 𝑡 ∈ N is the attempt number and 𝑠 ∈ { 0 , 1 } indicates whether the user stayed with the previous image suggestion or chose a new one. Rather than perturbing both the text embeddings and latent representations, we found that focusing solely on the latent space was sufficient. For a given latent vector z, we generate variations using a mixture of the original latent and a random noise vector:

where 𝑠 ∈ [ 0 , 1 ] is the mixture scale and 𝜖 ∼ N( 0 , 𝐼 ) 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 𝜙 can be optimized with humans-in-the-loop, e.g. by measuring and optimizing the reward for different steering distributions when humans use them. However, this can be expensive as it requires many human users. Instead, we simulate a human's behavior with an agent; given a human's prompt for a 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 to the goal image at train time (e.g. to choose the image that'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 0.1 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, we decompose

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 a normal distribution:

where ˆ 𝜇 𝑟,𝑏 is the empirical mean reward, 𝑛 𝑟,𝑏 is the number of times bucket b was chosen in round r, and 𝜎 2 is a prior variance parameter (set to 1.0 in our experiments).

During training, we:

Sample a mean from each bucket's posterior for each round

Choose the bucket with highest sampled mean

Play an episode using the corresponding mixture scales

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 were run with 4 rounds of interaction and 2 image variations per round. We used the DreamSim perceptual similarity metric both for the simulator's choices during training and for evaluation.

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 a 10-point scale (Figure 16), improvement ratings (Figure 18), generated image satisfaction ratings on a 4-point scale (Figure 17), prompt-output misalignment ratings (Figure 19), steering with allowing users to choose seeds, and steering with a constrained number of seeds. We conducted three analogous surveys on text steering for LLMs: steering, generated vs. goal headline satisfaction ratings on a 4-point scale, and improvement ratings. For all experiments involving rewriting multiple prompts, we always used rewrites 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. 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 5 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 6 goal images they see (in a random order). Providing users duplicates gives them a 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 4 shows a user's attempt at steering an LLM toward rewriting a 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 to reproduce goal images of tile patterns.

A wooden bookshelf with two sections,

each containing neatly arranged

hardcover books. The left section has

four books in shades of blue, while the

right section has four books in shades

of red. The books are evenly aligned,

standing upright with their spines facing

outward. The wooden shelf has a warm

brown tone with visible wood grain. The

lighting is soft and even, casting subtle

shadows on the books and shelf for a

realistic effect. The image has a sharp,

high-quality texture, with the books

appearing smooth and slightly glossy.

Create an image of 6 books in a shelf.

On the left will be three blue books and

the right three red books. A wooden

structure at the middle of the shelf to

divide these six books into three eqaul

A wooden bookshelf with two sections,

each containing neatly arranged

hardcover books. The left section has

three books in shades of blue, while the

other section has three books in shades

of red

one  bookcase shelf containing only 3

blue books graduated in shade from

light to dark.  A vertical partition shelf

and then three red books

A bookcase shelf containing 3 blue

books graduated in shade from light to

dark.  A vertical partition shelf and then

three red books

a shelf containing only 3 blue books

graduated in shade from light to dark.  A

vertical partition shelf and then three

red books

A book case with two compartments.

The left compartment has three books

of various shades of blue. From left

which is the lightest shade of blue, to

right which has the darkest shade of

blue. As you move left the shade of blue

gets darker across the three books. The

right compartment of the books shelf

also has three books. This time with

three red books.

A shelf with 6 book on it, 3 books a

various shade of blue. The 3 other

books are various shades of red. The

shades of colour range from left

(lightest shade) to right (darkest shade).

An image of a part of a book case. The

image shows just two compartments of

the book case with a total of six books,

split in two compartments with three

books in each compartment. The left

compartment has three books of

various shades of blue. From left which

is the lightest shade of blue, to right

which has the darkest shade of blue. As

you move left the shade of blue gets

darker across the three books. The right

compartment of the books shelf also

has three books. This compartment has

three red books.

Goal image

Attempt 1

Attempt 2

Attempt 3

Attempt 1

Attempt 2

Attempt 3

Attempt 1

Attempt 2

Attempt 3

TIRANAFFICPRFE

Attempt 1

Attempt 1

"A road with traffic and next to the road are palm trees. "

Attempt 1

Attempt 1

"There are three object. Each object is a square with a smaller, rectangular "handle" on the top of the square. Both the square and the handle have a slightly raised edge to them. Each object has a different colour. The one farthest to the left is a light taupe, the middle object is a darker taupe, and the far right object is white. The white object is turned slightly inwards."

Attempt 2

Attempt 2

Attempt 2

Attempt 2

"There are three objects. Each object is a square. Each square has an added  rectangular portion that has been added to the top of the square that looks like a handle. Both the square and the rectangle have a slightly raised edge to them. Each object has a different colour. The one farthest to the left is a light brown colour. The middle object is a medium brown colour, and the far right object is white. The white object is turned slightly inwards towards the middle object. The perspective is looking down on the objects from above."

Attempt 3

Attempt 3

Attempt 3

Attempt 3

"There are three objects as viewed from above. Each object is in the shape of a a square. Each square has an added  rectangular portion that has been added to the top of the square that looks like a handle. Both the square and the rectangle have a slightly raised edge to them. Each object has a different colour. The one farthest to the left is a light brown colour. The middle object is a medium brown colour, and the far right object is white. The white object is turned slightly inwards towards the middle object. "

Attempt 4

Attempt 4

Attempt 4

Attempt 4

"There are three objects as viewed from above. Each object is in the shape of a a square. Each square has an added  rectangular portion that has been added to the top of the square that looks like a handle. Both the square and the rectangle have a slightly raised edge to them. Each object has a different colour. The one farthest to the left is a light brown colour. The middle object is a medium brown colour, and the far right object is white. The white object is turned slightly inwards towards the middle object. The light source is coming from the left and is creating shadows where the edge is blocking the light. "

Attempt 5

Attempt 5

Attempt 5

Attempt 5

A drawing of a dog surrounded by

sharks. Black coloured

Attempt 1

Attempt 2

Attempt 3

Attempt 4

Attempt 5

Middle of new york city with yellow

cabs

Attempt 1

Attempt 2

Attempt 3

Attempt 4

Attempt 5

Colorful art piece with a lady as a

drawing

Attempt 1

Attempt 2

Attempt 3

Attempt 4

Attempt 5

the image shows a silver sedan car

(BMW-ish). the tires are black with

yellowish knots on the rim. the rim

is also silver, however, the mirror

has a small touch on blue. the car

is zoomed in to show 2 doors from

one side and the front on the car

(bonnet) just enough to sow the

Attempt 1

Attempt 2

Attempt 3

Attempt 4

Attempt 5

ILKN404G

front tire

a 4 square image with blue and

yellow triangles

Attempt 1

Attempt 2

Attempt 3

Attempt 4

Attempt 5

A lattice of emerald like diamond

shaped green pieces of glass joined

by a waven and smooth structure of

gold.

Attempt 1

Attempt 2

Attempt 3

Attempt 4

Attempt 5

yellow tiles 4 blue tiles triangle

shape in the centre forming a

square

Attempt 1

Attempt 2

Attempt 3

Attempt 4

Attempt 5

it is a blue and white image.

consists of many squares in total

16. in the middle of each sqaure

there is a flower like structure

that is blue and on each ege there

is a blue structure that when the

squares meet and overlapp becomes a

circular structure

Attempt 1

Attempt 2

Attempt 3

Attempt 4

Attempt 5

Reference Image Preview (Round 1/2)

The image above is your reference image for this round (it may take a 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 to refine your description and get the Al-generated image to match this reference image as closely as possible.

Instructions for an image generation survey.

landscape. In the far background, the vineyard transitions seamlessly into more greenery, blending with the horizon.

Generate Images

Iteration 3 / 4

Here are the edits suggested by the Al. You can either select one of its edits ('Move here') or stay with to generate your reference image.

Stay here

Movehere

Movehere

Round 4/30

ReferenceImage

image.

You can keep the following image qualities in mind as you rank: content, style, angle, setting.

mood.

Note:theimagesmaytakeafewsecondstoload.pleasebepatient!

Unsatisfled.

Somewhat satisfied.

No qualities in common.

Half of qualities in common.

Exact Same Image.

5

Round 1/30

Al-GeneratedImage

Rank your satisfaction with the Al-generated image, based on how wellit matches the reference

image.

You can keep the following image qualities in mind as you rank: content, style, angle, setting.

moodl.

Note:theimagesmaytakeafewsecondstoload.pleasebepatient!

Very

Somewhat

Somewhat

Very

unsatisfied

unsatisfied

satisfied

satisfied

Round5/30

TargetImage

motel

heart

uol!!p

motel.

Choose the Al-generated image that you would be most satisfied with if you were trying to

generate thereference image.

Note: theimages may take a few seconds to load, please be patient!

Lebpw1

Image 2

SHueartgf

heartof

MOTEL

Motel

VESDILLON

APUDILLON

5ORSTNG

Love

Round2/30

Choose the Al-generated image that best matches the prompt. Note:theimages may takea fewsecondsto load, please bepatient!

Prompt

a womanswhitebeltred background insnailform

Image1

Image2

Goal image

Image 1

Image 5

…

A single shelf on a

wooden book shelf, with a

wooden divider in the

middle. Three blue books

on the left and three red

books on the right.

Prompt 1

A single wooden shelf

with a wooden divider in

the middle. To the left of

the divider is three books

with blue covers. To the

right are three books with

red covers.

Prompt 5

Improvement

Is

Goal image

Image 1

Image 5

Prompt-Output Misalignment (1)

Is

A single shelf on a

wooden book shelf, with a

wooden divider in the

middle. Three blue books

on the left and three red

books on the right.

Prompt 1

a better description of

Image 1

or

Goal image

?

?

Prompt-Output Misalignment (5)

Is

A single wooden shelf

with a wooden divider in

the middle. To the left of

the divider is three books

with blue covers. To the

right are three books with

red covers.

Prompt 5

a better description of

Image 5

or

Goal image

?

or

closer to

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 a mathematical framework to 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 claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

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.

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 3 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

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: For our theoretical framework in Section 2, we state assumptions and provide derivations. For Equation 5 we provide a full derivation and state assumptions in Appendix A. Guidelines:

The answer NA means that the paper does not include theoretical results.

All assumptions should be clearly stated or referenced in the statement of any theorems.

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Theorems and Lemmas that the proof relies upon should be properly referenced.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

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 to 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 in detail how we trained models to predict steerability. For image steering, we provide a detailed explanation of our methodology in Appendix C.2.

Guidelines:

The answer NA means that the paper does not include experiments.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

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(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) Werecognize that reproducibility may be tricky in some cases, in which case authors are welcome to 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 it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes]

Justification: We provide all the data we collected for the steerability benchmarks on text-toimage models and LLMs, as well as the scripts we used to analyze this data and reproduce our results. Our code is well-documented and includes a ReadME with instructions.

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The answer NA means that paper does not include experiments requiring code.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

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The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

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6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

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.

Guidelines:

The answer NA means that the paper does not include experiments.

The full details can be provided either with the code, in appendix, or as supplemental material.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: The paper reports standard errors for experimental results.

Guidelines:

The answer NA means that the paper does not include experiments.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

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It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: The analyses in Section 4 were performed on a single A100 GPU, as noted in the paper. We describe the compute necessary for learning the steering distribution in Section 5.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: Our research conforms with the code of ethics.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

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 is that without a 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.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [Yes]

Justification: The paper does not release any data or models that pose a high risk for misuse.

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The answer NA means that the paper poses no such risks.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

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Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: We credit the creators of the models we evaluated in Section 3, as well as the original creators of DreamSim [25], CLIP [66], and CLIPScore [31].

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The answer NA means that the paper does not use existing assets.

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If this information is not available online, the authors are encouraged to reach out to the asset's creators.

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Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: We release our code and data as supplementary materials. We provide details on how the data was collected in Appendix D, and further documentation in the code.

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Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [Yes]

Justification: We used LLMs for two of our experiments: benchmarking the steerability of LLMs and 'blind steering' (Section 3). We describe all necessary details in those sections and Appendix C.1.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nIsolating 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\ndiscussed above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nHuman 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nEvaluating 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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)\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nlimitations discussed in the paper", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nThe 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.\n\nQUESTIONS:\nWhile 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\nLIMITATIONS:\nLimitations are adequately discussed.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}