# On the Challenges and Opportunities in Generative AI

Laura Manduchi, 1 Kushagra Pandey††footnotemark: , 2 Robert Bamler,3 Ryan
Cotterell,1
Sina Däubener,4 Sophie Fellenz,5 Asja Fischer,4 Thomas Gärtner,6 Matthias
Kirchler,5 7
Marius Kloft,5 Yingzhen Li,8 Christoph Lippert,7 9 Gerard de Melo,7 9 Eric
Nalisnick,10
Björn Ommer,11 Rajesh Ranganath,12 Maja Rudolph,13 14 Karen Ullrich,15
Guy Van den Broeck,16 Julia E Vogt,1 Yixin Wang,17 Florian Wenzel,18 Frank
Wood,19
Stephan Mandt, 2 Vincent Fortuin††footnotemark:  20 21

1ETH Zürich; 2UC Irvine; 3University of Tübingen; 4Ruhr-University Bochum;
5RPTU Kaiserslautern-Landau; 6TU Wien; 7Hasso Plattner Institute; 8Imperial
College London;
9University of Potsdam; 10Johns Hopkins University; 11LMU Munich; 12New York
University;
13Bosch Center for Artificial Intelligence; 14University of Wisconsin-Madison;
15Meta AI; 16UCLA; 17University of Michigan; 18Mirelo AI;
19University of British Columbia; 20Helmholtz AI; 21TU Munich
Equal contribution. Correspondence to<EMAIL_ADDRESS>contribution.

###### Abstract

The field of deep generative modeling has grown rapidly and consistently over
the years. With the availability of massive amounts of training data coupled
with advances in scalable unsupervised learning paradigms, recent large-scale
generative models show tremendous promise in synthesizing high-resolution
images and text, as well as structured data such as videos and molecules.
However, we argue that current large-scale generative AI models do not
sufficiently address several fundamental issues that hinder their widespread
adoption across domains. In this work, we aim to identify key unresolved
challenges in modern generative AI paradigms that should be tackled to further
enhance their capabilities, versatility, and reliability. By identifying these
challenges, we aim to provide researchers with valuable insights for exploring
fruitful research directions, thereby fostering the development of more robust
and accessible generative AI solutions.

##  1 Introduction

Generative AI has recently gained unprecedented attention with the emergence
of Large Language Models [LLMs; 19, 32, 138, 159] and their dialogue agents,
such as ChatGPT [18] and LaMDA [203]. These works have demonstrated that
scaling relatively simple generative models [211], exposing them to large
amounts of data, and incorporating preferences via the form of human feedback
[142, 255], yields immensely powerful AI tools that have the potential to
impact society profoundly. In contrast to traditional discriminative ML
learning tasks, the output space of generative AI is often high dimensional,
leading to unique problems in regards to efficient inference, pruning, and
quantization.

Fundamentally, LLMs model probability distributions over a discrete state
space, i.e., text tokens. Analogously in the continuous domain, diffusion
models [71, 191] have become the de-facto model family for high-quality image
synthesis, surpassing generative adversarial networks [GANs; 58, 93, 176] and
variational autoencoders [VAEs; 96, 166, 208]. Similar to LLMs, the
combination of a simple model [187] coupled with a successful network
architecture [43, 168], a large quantity of data, and human feedback in the
form of text has led to breakthroughs in image synthesis tasks like large-
scale text-to-image generation [162, 167, 171]. However, the impact of
generative AI extends far beyond text and image generation and across diverse
research domains, spanning from biology [88] to weather forecasting [163].
Within an array of applications—ranging from code generation [28, 112] to
video creation [236, 73, 184, 17], audio synthesis [14, 117], and even
artistic and musical composition [77]—we witness a consistent acceleration of
generative AI-driven progress.

With the current advancements and excitement surrounding generative AI, a
question naturally arises: Are we on the brink of an AI utopia? Are we close
to defining a _perfect generative model_ —capable of modeling (i) any joint
and conditional distribution over real-world data and (ii) its underlying
causal structure—that could theoretically solve every conceivable AI task,
from discriminative challenges to reinforcement learning and beyond? We
believe the answer, however, is a resounding no. The realization of such a
model, one that would fundamentally transform the field of AI, is still a
distant vision, hampered by substantial theoretical, practical, and ethical
challenges.

We argue that scaling up current paradigms is _not_ the ultimate solution in
isolation and that the current state of generative AI falls short of the
efficiency, inclusivity, transparency, and overall usability required for its
widespread integration across domains, particularly in high-stakes decision
problems. We, therefore, collected views and opinions from different
communities to identify key unresolved challenges and to guide future research
towards what we perceive are the most critical and promising areas. More
specifically, we discuss key challenges in (a) broadening the scope and
adaptability of Deep Generative Models (DGMs)111In this paper, we refer to
Generative AI as a collection of large-scale DGMs and use the term DGM
henceforth. by overcoming current limitations and providing an outlook on
promising, and largely unexplored research directions (Section 2); (b)
improving their efficiency and resource utilization, to lower the memory and
computational requirements and enhance accessibility and sustainability in
their adoption (Section 3); and, finally, addressing _ethical and societal
concerns_ that are crucial for responsible deployment (Section 4).

This paper emerged as a result of the Dagstuhl Seminar on _Challenges and
Perspectives in Deep Generative Modeling_ 222https://www.dagstuhl.de/23072,
held in Spring 2023. Our goal is to provide an overview of research and
development priorities to guide the forthcoming research toward areas of
significant potential impact. By outlining a comprehensive roadmap, we hope to
empower researchers and practitioners alike, fostering the development of
generative AI models that are not only more robust and reliable but also
accessible to a wider audience.

##  2 Expanding Scope and Adaptability

While remarkable progress has been made by scaling DGMs to massive datasets
and model sizes (for instance, in text and high-resolution image synthesis),
we argue that true innovation extends beyond this achievement. It is crucial
to move beyond evaluations that rely exclusively on state-of-the-art
leaderboard rankings [10] and understand these models’ inherent and often
hidden constraints [137]. This section analyzes some of these challenges in
the context of large-scale DGMs from the lens of their generalization
capabilities (Section 2.1) and the lack of transparency in their underlying
modeling assumptions (Section 2.2). We examine these fundamental challenges
and provide research directions that could broaden the adaptability of DGMs to
promote long-term progress in the field.

We also explore two promising avenues that have the potential to greatly
enhance the scope of generative models: (i) integrating causal representation
learning (Section 2.3) and (ii) the development of a versatile, generalist
agent capable of handling heterogeneous data types (Section 2.4).

###  2.1 Generalization and Robustness

To ensure reliability across various domains, DGMs must generalize effectively
to shifts in data [out-of-distribution (OOD) robustness; 8, 9, 158, 224, 156]
and be resilient to minor variations in input [adversarial robustness; 119].
Without proper generalization, generative models may produce unrealistic or
biased outputs, limiting their practical utility and trustworthiness in real-
world applications [3].

Large-scale generative models trained on a wide variety of data show promise
in achieving OOD robustness [215]. However, these models still face challenges
in accurately capturing rare events, a difficulty that lies in effectively
modeling the _long tail_ of information [92]. This limitation indicates a gap
in their ability to fully represent the vast and diverse spectrum of real-
world scenarios, especially those that are less common but equally
significant. Retrieval-augmented language models represent a promising
direction to improve rare fact learning and go beyond scaling up datasets
[92].

DGMs are also prone to adversarial vulnerability, often due to the presence of
highly predictive but non-robust features that are used as a _shortcut_ for
prediction [45, 157, 219]. This poses a significant threat to various
downstream scenarios, especially those of safety-critical applications [155,
215]. Several approaches to mitigate the effect of shortcut learning are based
on dataset refinement, also known as data-centric approaches [225], or on
model refinement. In the latter, work has been done towards improving
robustness via adversarial training [256], ensembling [33], contrastive
learning [30], and the direct integration of prior knowledge [79].

In the majority of applications, however, foundation models are often adapted
to specific tasks and downstream datasets. Standard fine-tuning techniques
often overemphasize the target task, leading to catastrophic forgetting [201]
and a loss in the general robustness of the upstream model [198]. Therefore, a
significant challenge is to develop robust adaptation methods that adequately
solve the target task but still maintain the beneficial robustness properties
of the upstream model (e.g., robustness to distribution shifts of the target
dataset) [7, 45, 65, 120]. Additionally, the economization of the inference
and memory cost of DGMs requires the distillation of large foundation models
into smaller and cheaper models. In this context, it is important to develop
robust distillation methods that do not sacrifice the robustness of the model
[46, 254]. Overall, attaining models that are robust and interpretable will
require explicitly encoding human priors into the training process [79].

###  2.2 Overcoming Implicit Assumptions

#### Silent Assumptions.

Current generative models often rely on implicit assumptions and inductive
biases that are rarely questioned or investigated in detail [246]. For
example, while data are often assumed to be drawn independently, they are
often correlated, such as in time-series data or through repeated measurements
or related individuals. Traditional statistical methods commonly model data
dependencies with random effects [85] or latent variables. In normalizing
flows, dependencies can be incorporated directly into the likelihood objective
[98], an approach that might be extended to other probabilistic approaches
such as VAEs and diffusion models [199]. It is also possible to directly model
latent variables leading to data dependencies with causal models [149].

We argue that it is important to scrutinize commonly overlooked assumptions in
generative models that are silently accepted but may require a more in-depth
investigation. For example, most generative models assume that latent
distributions can be modeled on simple topological structures, while they
might actually benefit from more expressive approaches [195]. Assumptions on
noise and latent distributions also usually follow out of convenience and may
hinder proper data fit for certain target distributions. An example is given
by heavy-tailed distributions, which are addressed by only a few principled
generative approaches so far [114, 103, 78].

The impact of model misspecifications in traditional statistical analyses can
be immense [21], while the impact on downstream applications in DGMs still
needs to be thoroughly explored. Models that rely heavily on the training data
distribution may exhibit bias and decreased performance if not properly
corrected by meaningful modeling assumptions [50].

#### Incorporation of Prior Knowledge.

Recent major breakthroughs in deep generative models (DGMs) have primarily
been achieved in settings where models could be trained on internet-scale data
[138, 167]. However, many real-world applications, such as drug design [210],
material engineering [221], personalized medicine [125], and protein
biochemistry [12], often have much smaller datasets due to the high cost of
data generation. In these areas, domain experts often possess troves of
detailed prior knowledge, often filling entire textbooks, raising the question
of whether this prior knowledge could be used to enable more data-efficient
learning in generative AI models.

Indeed, it has been shown in the context of VAEs that incorporating domain
prior knowledge can significantly improve model performance [52, 83] and even
unlock their use for tasks that were previously impossible [51, 126, 127].
However, this relied on the fact that VAEs are Bayesian models and, therefore,
offer a natural paradigm for specifying a prior distribution over their latent
space.

Specifying priors for diffusion models is less straightforward than for VAEs.
While it might seem at first glance that the Gaussian sampling distribution of
the diffusion process is equivalent to the Gaussian latent prior in a VAE, in
the diffusion case, it arises from the central limit theorem. This
distribution is not easily changed, even for Gaussian diffusion noise or any
finite-variance additive noise [192]. Recent works have attempted to enhance
the space of diffusions through auxiliary dimensions [144, 185]. Moreover,
mixing in many existing diffusion models breaks the relationship between the
Gaussian sampling distribution and the observed sample space. Existing
attempts to imbue diffusion models with novel priors have therefore sought to
at least change the mean and covariance of the latent Gaussian by adding
deterministic drift terms to the diffusion process [62, 105] or to replace
parts of the process with a normalizing flow [240]. Unfortunately, neither of
those approaches offers nearly the same flexibility of prior specification as
the Bayesian priors in the latent space of VAEs, so further research into
priors for diffusion models is sorely needed.

###  2.3 Causal Representations

Going beyond learning mere statistical correlations and understanding how
underlying factors influence the generative process is the main objective of
learning a causal representation of data [151]. Such representation can be
used to reason about hypothetical scenarios in the world, understand the
effect of interventions, and perform counterfactuals [150], thus facilitating
informed decision-making. Although there have been attempts to develop methods
for learning the optimal generative structure of deep latent variable models
from data [68, 128], current generative models often neglect the underlying
causal dependencies in their generative processes, making them prone to
shortcut learning and spurious associations [64, 99, 129].

Causal representation learning in deep generative models has the potential to
offer distribution-shift robustness, fairness, and interpretability [99, 178,
218]. Current open challenges include, but are not limited to, scalable and
robust causal discovery from observational data [165, 250, 133],
identifiability of deep generative models under weaker forms of supervision
[2, 122, 213], lack of benchmark datasets and metrics to evaluate
counterfactual quality [134], strong assumptions that are often violated in
real-world applications [99], and, finally, the integration of diffusion
models, a field that is currently under-explored but has tremendous growth
potential [132, 145, 174, 175]. We suggest that the integration of causal
principles in DGMs could pave the way for the development of more robust,
interpretable, and actionable generative AI systems [251].

###  2.4 Foundation Models for Heterogeneous Data Types

Large vision-language models (VLMs) have demonstrated significant capabilities
in processing both textual and image data simultaneously [124, 55, 34].
However, as the scope of application widens to encompass a broader range of
data modalities, a variety of challenges emerge. These challenges are
particularly pronounced in specialized fields such as healthcare and chemistry
[160, 100].

In healthcare, generation based on diverse data types—including imaging,
health records, and genomics—poses challenges in interoperability, data
privacy, and security [135]. Time series generation, particularly, requires
addressing irregularly sampled data, missing values, seasonality, and long-
term dependencies [194]. In chemistry, physics, and chemical engineering,
generative models have huge potential, not just for molecule, drug, and
material design, but also in data augmentation, property prediction, and
reaction prediction [227, 1, 25, 76]. Data in these fields are often sparse,
heterogeneous, correlated, and have high uncertainties. On the other hand,
they provide a vast body of physical and chemical domain knowledge, ranging
from (strict) laws of nature and boundary conditions to (soft) empirical
correlations and human experience. Therefore, developing hybrid (ML + domain
knowledge) foundation models is a particular challenge [212, 87, 86, 75].

The overarching goal is to integrate information from diverse sources and
understand complex relationships across different types of data [106, 164,
44]. However, to effectively interact with the physical world, embodied agents
must integrate perception, reasoning, and planning across vision and language,
along with executing physical actions and environmental interactions. While
datasets for natural language or images are relatively accessible [66, 107,
108], a comparable dataset for control tasks is lacking, suggesting generative
simulation as a potential solution [230, 49].

##  3 Optimizing Efficiency and Resource Utilization

Efforts to scale deep generative models (DGMs) for tasks like language
modeling and text-to-image synthesis often involve training large models with
billions of parameters, which demands significant computational resources.
This leads to practical issues such as high energy costs [228] and expensive
inference, limiting access for many users. There is a clear need to reduce the
memory and computational requirements of large-scale DGMs to enhance
accessibility and sustainability [10].

In this context, we discuss the efficiency-related challenges in current DGMs.
We focus on minimizing training and inference costs (Section 3.1), as well as
highlighting challenges in designing evaluation metrics for DGMs (Section
3.2), which greatly affect the computational resources needed for model
selection and tuning.

###  3.1 Efficient Training and Inference

#### Network Architecture.

Optimizing the network architecture, which forms the backbone of modern
machine learning, is crucial for efficient training and inference in DGMs.
However, despite recent progress in improving LLMs [19], a principled
investigation of the role of different underlying neural network modules is
still lacking. For instance, several popular LLMs like PaLM [32] and LLaMA
[205, 206] still largely reuse the original transformer architecture from
Vaswani et al. [211] with some additional modifications [182, 196, 241].
However, the self-attention [5] operation in transformers traditionally scales
quadratically in the context length, making inference computationally
expensive, especially for long-context modeling. While recent work has focused
on computationally improving attention mechanisms [200], these variants remain
empirically ineffective at scale across domains. Therefore, exploring
alternative autoregressive sequence-modeling frameworks with favorable
properties, such as scalability and linear complexity in the context length,
remains an interesting direction for future work [60, 61].

Similarly, several popular large-scale text-to-image diffusion models like
DALL-E 2 [162] and StableDiffusion [167] largely reuse the popular UNet [168]
backbone from Ho et al. [71], which has high memory costs. Therefore, we
believe that a principled study of the impact of different network components
in large-scale generative models is crucial for efficient training and
inference. Some recent works [74, 95, 152, 153] already explore architectural
design choices for reducing diffusion model sizes, thereby improving training
dynamics while enabling faster inference with a lower memory footprint.

#### Model Quantization.

The goal of model quantization is to reduce the precision of model weights and
activations, enabling faster, memory-efficient training and inference, ideally
without losing performance on downstream tasks. The most common quantization
approaches are Post-Training Quantization (PTQ), which applies quantization to
a pre-trained large model to enable faster and memory-efficient inference, and
Quantization-Aware Training (QAT), which involves training a quantized model
from scratch [101].

Despite some progress in developing PTQ and QAT methods for LLMs [37, 121,
231, 239] and large-scale text-to-image diffusion models [110], the existing
methods are far from perfect. For instance, OPTQ [54], a PTQ-based approach,
can perform inference for a quantized LLM (in this case OPT [244]) with 175B
parameters on a single A100 GPU with 80GB of memory without degradation in
accuracy. Though impressive, even this quantized model would likely have
limited utility on a consumer-grade GPU device, let alone on standard edge
devices. Similarly, QAT-based approaches can often achieve lower bitrates but
trade off additional training for this efficiency. This can be a major
computational bottleneck for large generative models. Therefore, we believe
that investigating the impact of model quantization at low bitrates in large-
scale generative models is a crucial direction for the practical deployment of
these models.

#### Design Challenges.

While currently dominant paradigms in generative modeling like diffusion
models [71] and LLMs [19] exhibit excellent sample quality, there exist
several crucial challenges associated with the design of the generative
process itself. For instance, the iterative nature of the multi-stage
denoising process in diffusion models slows down inference considerably, often
requiring hundreds to thousands of network function evaluations (NFE) to
generate high-quality samples [71, 191]. Similarly, the autoregressive
structure in LLMs can lead to slow inference due to sequential token
generation. This is in contrast to alternative generative models like VAEs and
GANs, which require only a single NFE but suffer from problems like blurry
sample generation [42] and mode collapse [4].

Therefore, speeding up inference in diffusion models is a fundamental research
problem spanning multiple (possibly complementary) directions. Some notable
approaches include: developing training-free samplers [189, 118, 123, 243, 94,
146], designing better diffusion processes [185, 40, 144, 94], and combining
other model families with diffusion models [145, 249, 232, 235]. Additionally,
training a diffusion model in the latent space of a lossy transform [209, 167]
not only improves memory requirements and sampling efficiency but also
provides access to a more interpretable low-dimensional latent representation.
A lossy transform (such as VQ-GAN [48]) can drastically reduce data
dimensionality while retaining the perceptually relevant details of high-
resolution images. Designing more efficient lossy compression operations in
the context of diffusion models has received less attention in the community
and is an important direction for further work [238, 67, 237]. However,
despite these advances, sampling from diffusion models remains computationally
challenging, typically requiring 25-50 NFEs to generate high-quality samples.
While approaches based on progressive distillation [172, 130] can further
speed up inference, they trade off additional training for faster sampling.
Therefore, there is a need for DGMs that inherit all the advantages of
diffusion models while supporting one-step sample generation by design (e.g.,
see consistency models [193, 190] for recent work in this direction).

For LLMs, in addition to an expensive self-attention operation in transformer-
based autoregressive models, sequential token generation in a left-to-right
fashion in these models makes inference more expensive. In contrast, diffusion
models amortize the sequential generation cost among all tokens, leading to
faster inference than their autoregressive counterparts. While this
observation can potentially open up interesting research directions for
sequence modeling using diffusion models [39, 229, 109], these approaches
inherently lack the inductive bias for contextual generation, which has been
shown to work well empirically for sequential modeling tasks. This affects
their performance in downstream tasks that might require long-context
modeling, such as video synthesis [236, 72]. While diffusion models can be
incorporated within the autoregressive framework for such tasks, the resulting
models can be very expensive during inference (due to the cost of synthesizing
a single token using diffusion across multiple tokens). Therefore, we identify
a potential tradeoff between long context modeling and efficient inference,
with the diffusion and autoregressive modeling paradigms falling on the
opposite ends of this tradeoff. Hence, designing generative modeling paradigms
that can optimally balance this tradeoff remains challenging.

###  3.2 Evaluation Metrics

Evaluation metrics are crucial in guiding the research directions, as the
conclusions derived from empirical studies are highly dependent on the chosen
metrics for model comparison. In modern ML, evaluation metrics are a key
component in hyperparameter tuning and model selection, affecting
computational resources required during large-scale training. However,
designing robust evaluation metrics for DGMs is challenging for several
reasons.

#### Evaluation Metric Design.

While likelihood-based metrics have been extensively used in evaluating DGMs,
they do not guarantee a precise assessment of the quality of the generations
[202]. Additionally, many popular generative models do not allow for tractable
likelihood computation, so other evaluation metrics are necessary for
evaluating the quality of generated images. For instance, in evaluating image
synthesis models, several notable metrics, like the Fréchet inception distance
[FID; 70], compare the distribution of the generated samples with the
train/test data samples [173, 11]. However, these approaches are far from
perfect. First, to compute these metrics, one generates a large set of samples
(around 50k). This can be computationally demanding for generative models with
a sequential inference process like diffusion and autoregressive models.
Second, since these methods typically rely on a model pre-trained on ImageNet
[35], they are more suited for evaluating generative models of natural images
[102] and, therefore, might disregard important features in other domains,
e.g., medical images.

Similar considerations are relevant for evaluating text generated from LLMs
[139, 140]. While FID has also been adapted to measure the quality of
generated text [179], the most popular metrics are based on n-gram matching
such as BLEU [147], or ROUGE [116]. Additionally, Zhu et al. [253] proposes to
complement BLEU with self-BLEU to promote diversity. However, there is often a
quality-diversity tradeoff [20], where a model that generates high-quality
text might have low diversity and vice versa.

Subjective aspects in generation. Another major challenge underlying the
evaluation of generation quality is the subjective nature of sample
attributes, such as realism and style (especially for multimedia data). For
instance, while human inspection [36, 252] is typically the gold standard for
evaluating generated images, human evaluators might have subjective opinions
on what is considered realistic in the target domain (e.g., medical images or
industrial optical inspection). This also holds for conditional synthesis
tasks like text-to-image generation [161, 167, 171], where there is usually no
reference distribution of real images. For this reason, a common approach is
to set up a benchmark for evaluation based on human judgment [171, 81].
However, in many cases, human judges can disagree over which samples have
better quality333http://tinyurl.com/parti-prompts. In addition, human
evaluation focuses on individual images and does not necessarily measure how
well the generative model reflects the data distribution. Therefore, since
many desired properties of generative models cannot be evaluated
programmatically, learning a reward function from human preferences has gained
importance [142]. A public benchmark of the learned reward function would
provide a useful evaluation suite for generative models.

Robustness. There is a pressing need to design robust evaluation metrics
[139]. For instance, while commonly used in image synthesis, FID can be
sensitive to minor perturbations in the input data [148] (see Chong and
Forsyth [31] for additional discussion on sources of bias associated with FID
and IS and Borji [13] for more related evaluation metrics). Similarly, in
language modeling, expected calibration error [63] is also an evaluation
metric of interest. While pre-trained large models are often well calibrated
[138, 90], fine-tuning or RLHF often hurt calibration accuracy [204].
Evaluating issues in calibration [233] has led to work on mitigating these
issues (e.g., Wang et al. [216] for fine-tuning and Tian et al. [204] for
RLHF), highlighting the importance of robust evaluation.

Model Selection. Model selection is essential in training large-scale models
but can be computationally expensive. While evaluation metrics with scaling
laws are now used to predict early on in a training run whether it is likely
to be successful [138], this can still be computationally inefficient. We also
believe that more effort should be invested in analyzing the _performance-
complexity_ tradeoff, an important yet under-investigated measure for real-
world applications at scale. This tradeoff refers to the balance between model
performance and computational complexity, to identify the model families that
lie in the associated Pareto set that optimizes this tradeoff [38, 15, 29,
16]. The naïve approach—training well-performing models in each class and
computing their respective complexities—is time- and resource-intensive.
Therefore, alternative evaluation metrics should be investigated by utilizing
tools from information and learning theory [e.g., 234].

##  4 Ethical Deployment and Societal Impact

With the current excitement around the scope and application of large-scale
generative models, we are also witnessing a growing apprehension, fueled by
media reports, of adverse outcomes surrounding the rapid advancement of
generative AI. These concerns add to the conceptual and practical
considerations discussed so far, and encompass a range of issues, including
the spread of misinformation, the absence of regulatory frameworks [131],
unintended harm [59], and debates over open-source versus closed-source
technologies [26], among others. Here we identify key challenges concerning
the responsible deployment of large-scale deep generative models. More
specifically, we discuss several aspects, including the dissemination of
misinformation (Section 4.1), violation of privacy and copyright (Section
4.2), presence of biases (Section 4.3), lack of interpretability (Section
4.4), and constraint satisfaction (Section 4.5).

###  4.1 Misinformation

As the quality of generated data synthesized using large-scale generative
models increases, it can become increasingly hard to distinguish between real
and generated samples, especially for uninformed consumers [53]. Consequently,
this can enable the spread of misinformation (e.g., by deepfakes [69]). To
ensure the trustworthiness of information, we need algorithmic solutions that
are on par with the advances in generative models and allow us to robustly
detect and mark synthetic data. One common approach to the detection of
synthetic text and image data is watermarking, where the goal is to manipulate
the generated sample, (ideally) with minimal effects on sample quality, with
the ability to detect the injected watermark in downstream tasks. There have
been several approaches to watermark synthetic data generated using LLMs [97]
and diffusion models [247, 223]. However, current watermarking methods are far
from perfect [170]. Moreover, it is unclear if the effects of watermarking can
be reduced by downstream transformations (like paraphrasing or image
manipulation) and to what extent. Therefore, designing efficient frameworks
for detecting synthetic data can be an interesting direction for further work.

###  4.2 Privacy and Copyright Infringement

Interestingly, recent works show that publicly available LLMs and large-scale
text-to-image models can implicitly “memorize” training data, in the sense
that points from the dataset can be (almost exactly) reconstructed, which
potentially infringes on data privacy [23, 22, 188, 136]. This connects to the
open question of whether it is possible to train generative models while
preserving data privacy. To alleviate this problem, approaches like training
generative models with differential privacy (DP) constraints offer an
attractive theoretical framework to ensure privacy [111, 41]. However, DP-
based approaches suffer from a tradeoff between privacy and utility. Moreover,
in the context of image generation, scaling such approaches to high-resolution
datasets remains elusive. Therefore, building privacy constraints in large-
scale training of generative models can be a promising direction for further
research.

In addition to privacy, recent advances in large-scale generative modeling can
lead to unauthorized distribution or replication of training data resulting in
copyright infringement
liabilities444https://www.nytimes.com/2023/12/27/business/media/new-york-
times-open-ai-microsoft-lawsuit.html. While some recent work tries to
alleviate this issue [113, 248], there are several outstanding technical
challenges ranging from mitigating copyright infringements during dataset
curation [24] to reliable detection of copyright violations in generated
samples.

###  4.3 Fairness

Large-scale generative models are often trained on massive datasets containing
billions of samples scraped from the internet. While preprocessing such large
datasets often involves tagging or removing toxic content, a variety of other
societal biases are often harder to detect. Consequently, the trained models
can reflect biases and produce outputs that may be deemed toxic or harmful.
For instance, Weidinger et al. [222] outline a series of harms that may result
from the use of LLMs that produce discriminatory or exclusionary language,
e.g., by amplifying stereotypes. Similarly, multimodal models may exhibit
biases about gender, ethnicity, and religion, among others [82]. While
numerous approaches have been proposed to mitigate such biases in a post-hoc
manner [6, 56], the achieved changes are often merely superficial [57],
leaving the possibility of remnant biases. Further research is necessary to
train models that can readily account for multiple forms of fairness with
regard to diverse criteria. More importantly, we also need robust and
interpretable evaluation metrics to quantify different forms of biases that
can adapt to closed-source generative models [245, 197, 104].

###  4.4 Interpretability and Transparency

Developing reliable methods of evaluating interpretability in DGMs is of
crucial importance [169, 84]. In particular, while scaling up large generative
models has been shown to improve performance and sample efficiency, their
highly complex architecture gives rise to unpredictable effects [220, 177] and
demonstrates capabilities that have not been anticipated by model developers.
Their lack of transparency and interpretability raises concerns in critical
applications such as healthcare [27] or finance, where understanding model
decisions may be crucial for building trust and ensuring safety. Transparency
in how the model works is essential to building trust. For example, while LLMs
seem to be able to explain their outputs, their explanations are highly
unreliable [207]. Research has also questioned the use of attention patterns
as reliable explanations [80, 226, 180], potentially due to the heavy use of
residual architectures in modern transformers.

An important open question for future research is how and why emergent
abilities occur in large language and vision models (or whether the seemingly
emergent behavior is due to the selection of metrics as suggested by Schaeffer
et al. [177]). The fundamental challenge here is to develop explanation
methods that are both well-understood by humans and faithful to the underlying
model behaviors. For the former, apart from core developments in ML methods,
more scientific methods for user studies and human evaluations can be borrowed
from research on user experience design and human-computer interaction [115,
217, 154]. For the latter, efforts should be made to further develop and
robustify explainable methods such as counterfactual explanations [214, 186].

###  4.5 Uncertainty Estimation and Constraint Satisfaction

Language models and other generative models of symbolic sequences have a long
history that can be traced back to early studies on prediction probabilities
for written language, such as those by Shannon [181]. Their traditional next-
token likelihood objective emphasizes just plausibility [91]. However, recent
services such as ChatGPT, as well as the models deployed in large commercial
search engines, are increasingly expected to serve as universal question-
answering engines. With this comes an increasing need for models that produce
statements that may be deemed factually accurate and trustworthy while
avoiding confabulation, often also referred to as _hallucinations_ [91].
Encouragingly, recent studies show that models often do possess the ability to
assess the truthfulness of their own statements [89]. Additional evidence can
be brought in from external knowledge sources [143]. Another potential
direction might be to emphasize probabilistic consistency [47], e.g., between
a statement and its negation. This indicates that significant improvements can
still be achieved in estimating uncertainty.

Generative models such as ChatGPT are used by millions of people and deployed
across diverse use cases. For many of these, they are expected to satisfy
additional constraints. In some cases, these merely stem from a desire to have
a more controlled form of generation, such as when a generated image is
conditioned on a given depth map [242]. In other cases, ethical and societal
considerations are key concerns. For instance, widespread calls exist for
generative models to avoid toxicity and adopt bias mitigation mechanisms
[222]. Current models are also expected to avoid outputs that may lead to
harmful effects, for instance, by refraining from responding in ways that
could pose a risk to the mental health of human interlocutors and by refusing
to carry out tasks related to illegal activities. Currently, this is often
achieved using reinforcement learning from human feedback [141], but further
research is necessary to mitigate the risk of such constraints being
circumvented [183].

Thus, new frameworks that combine uncertainty estimation and constraint
satisfaction have the potential to yield AI models that better adhere to our
human expectations.

##  5 Conclusion

Despite recent excitement and hype around the advancements in generative AI,
learning a _perfect_ generative model is far from its realization. To this
end, we identify several core challenges with the current generative modeling
frameworks from the lens of adaptability across different domains, efficient
resource utilization, and reliable and safe deployment. We believe solving or
alleviating these challenges to a large extent, coupled with recent advances
in scaling, can unlock the true potential of generative models with major
technological and societal implications.

We identified generalization and robustness as major hurdles, demanding
methods to handle unseen data and adversarial attacks. Moreover, hidden
assumptions regarding data independence and limited representational power
necessitate the exploration of more expressive models and the incorporation of
prior knowledge, especially in data-scarce scenarios. Moving beyond mere
correlation, integrating causal reasoning into DGMs holds promise for enhanced
interpretability and robustness. We also emphasized the escalating
computational demands and associated barriers to the widespread adoption of
DGMs. Efficiencies in training and inference pose key challenges, urging the
exploration of alternative network architectures and low-bitrate model
quantization. Subjective evaluation metrics for generated content, such as FID
and n-gram matching, present limitations, prompting the search for robust,
domain-agnostic alternatives.

We also addressed the challenges and considerations surrounding the
responsible deployment of large-scale generative models, pointing to the
rising concerns related to misinformation, unintended harm, and lack of
trustworthiness. Challenges include combating misinformation, ensuring privacy
in data curation, addressing fairness issues, enhancing interpretability, and
estimating uncertainty while satisfying ethical constraints.

By confronting the limitations explored, we can transform DGMs from data
replicators to transformative tools across various domains. Imagine, for
instance, AI agents in healthcare seamlessly integrating diverse medical data
to uncover causal relationships, empowering personalized medicine, and
accelerating drug discovery. We hope that our paper will point to directions
that ultimately contribute to these goals.

## Acknowledgments and Disclosure of Funding

This paper emerged as a result of Dagstuhl Seminar #23072 “Challenges and
Perspectives in Deep Generative Modeling”, held in Spring 2023. LM is
supported by the SDSC PhD Fellowship #1-001568-037. KP acknowledges support
from the Hasso Plattner Research School at UC Irvine. AF acknowledges support
by DFG under Germany’s Excellence Strategy – EXC-2092 CASA – 390781972. TG
acknowledges partial support from FFG through the AI4SAR project (885324) in
the ASAP programme. MK acknowledges support by the Carl-Zeiss Foundation, the
DFG awards KL 2698/2-1 and KL 2698/5-1, and the BMBF awards 03|B0770E and
01|S21010C. Part of this work was conducted within the DFG research unit FOR
5359 (BU 4042/2-1, KL 2698/6-1, and KL 2698/7-1). RR was partly supported by
the NIH R01HL148248, NSF Award 1922658, NSF Award 2145542, ONR
N00014-23-1-2634, and Optum. YW is supported in part by the Office of Naval
Research under grant number N00014-23-1-2590 and the National Science
Foundation under Grant No. 2231174 and No. 2310831. VF was supported by a
Branco Weiss Fellowship.

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