ISSN 2186-7437

NII Shonan Meeting Report

No. 166

Visualization for XAI (Explainable AI)
Seok-Hee Hong
Daniel Keim
Issei Fujishiro
February 6–9, 2023

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Visualization for XAI (Explainable AI)
Organizers:
Seok-Hee Hong (University of Sydney)
Daniel Keim (University of Konstanz)
Issei Fujishiro (Keio University)
February 6–9, 2023

Background and introduction
Summary
Explainable AI (XAI) refers to artificial intelligence (AI) techniques which canbe easily understood by humans which increases their trustworthiness. In contrast, many AI techniques generate ”black box” models where even their designers cannot explain why they arrive at specific decisions. Visualization ofdata or the generated models plays a significant role in making AI methods
understandable and accessible to the user.
The technical challenge of XAI is to make AI decisions understandable andinterpretable, leading to a higher degree of trust by the users of AI methods.
In general, AI methods learn useful rules from the test-set; however, they may
also learn incorrect rules, which fail to generalize outside the test set, or eveninappropriate rules, which lead to an undesired social, political, or racial profiling. XAI methods help humans understand the learned models as well as therelationship to the training and application data. The goal is to get an idea
how likely the AI decisions will generalize to future real-world data outside thetest-set and understand their economic, social, and political implications.
XAI is of interest to a wide variety of user groups, including• AI method developers who are interested in understanding and improving
their AI algorithms but they are in general application-agnostic• Application end users who are interested in understanding the AI decisions
since they are responsible for the consequences but they are in generalmodel-agnostic• Data scientists who are interested in understanding which AI methods
they should apply to the concrete data set at hand.
The role of visualization in XAI gained significant attention in recent years.
Visualizations play an important role since they allow users to quickly get an
overview of the learned models as well as their relationship to various data sets
involved (training data, test, data, and application data). With the growing
complexity of AI models, the critical need for understanding their inner-workings1has increased and the complex relationships between training, test, and application data and their effect on the usefulness and trustworthiness of the AI
decisions has to be carefully taken into account. Visualizations are powerful
techniques to fill such critical needs.
The main goal of this workshop is to discuss the role of visualization for XAI
methods. In particular, we aim at identifying research opportunities for using
visualization in XAI, focusing on the Asia- Pacific context. We believe the visualization community can leverage their expertise in creating visual narratives
to bring new insight into the often-obfuscated complexity of AI systems.

Overview of the meeting
Aims
This workshop aims at bringing world-renowned researchers in visualization and
AI together, and collaboratively develop innovative visualization approaches for
XAI with specific applications of large and complex networks to solve the scalability and complexity issues for analyzing big data arising from various application domains including social networks, business intelligence, and network
security.

Objectives
Our specific objectives are:
• We will identify research opportunities in XAI, focusing on the visualization perspectives in the Asia-Pacific context.
• We will form a broader research community with cross-disciplinary collaboration, including computer science and machine learning, with a particular focus on visualization and visual analytics for XAI.
• We will foster exchange between visualization researchers and practitioners, and draw more researchers in the Asia-Pacific region to enter this
rapidly growing area of research.
• We will assist emerging researchers to link to international researchers,
find industrial contacts, and apply for competitive research grants.

Significance and Innovation
XAI is one of the biggest fundamental challenges in IT research due to the wide
spread use of AI methods in research and industry. In many application contexts, the applicability of the results, however, suffers from the black-box natureof many AI methods which prevents understandability and interpretability, andultimately limits the trust into the results. Visualization and visual analytics
provide a great potential to overcome the current limits of AI methods and significantly increase understandability, interpretability, and trust in AI methods.
Innovative visualization- and visual analytics-based XAI methods may therefore
be the key enabler for researchers and end users in many application domains
and other disciplines. We believe that this workshop has the potential to set a
new research agenda for visualization and visual analytics based XAI research.
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Expected Outcomes• Innovative visualization and visual analytics techniques and solutions for
XAI, which will be used by domain experts and end users in various applications.
• Joint publications at top conferences and journals in visualization andvisual analytics, jointly authored by visualization and AI researchers.
• Joint funding applications for long-term research collaborations continuing
the research.

Impact• Academic impact: Research publications at top conferences and journals,
with high number of citations.
• Societal impact: Visualization and visual analytics techniques for XAI will
be in high- demand by researchers and practitioners in various applications
and disciplines to solve complex problems in their domains.

Overview of Talks
Visual, Interactive, and Explainable AI: Overview and Open
Research Challenges
Mennatallah El-Assady, ETH, Switzeralnd

Figure 1: XAI definition based on faithfulness and comprehensibility.
Interactive and explainable machine learning can be regarded as a process
encompassing three high-level stages: (1) Understanding machine learning models and data; (2) Diagnosing model limitations using explainable AI methods;
and (3) Refining and optimizing models interactively.

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In this talk, I review the current state-of-the-art of visualization and visual
analytics techniques by grouping them into these three stages. In addition, I
argue for expanding our approach to explainability by adapting concepts, suchas metaphorical narratives, verbalization, and gamification. I further introduce
the explAIner.ai framework for structuring the process of explainable artificial
intelligence (XAI) and interactive machine learning (IML), operationalizing it
through a TensoBoard plugin.
Lastly, to derive a robust XAI methodology, I present some first steps to extract XAI strategies and mediums by transferring knowledge and best practices
from other disciplines.

Visual Analytics for Explainability to understand High Dimensional Data
Kwan-Liu Ma, University of California at Davis

Figure 2: The results of applying ccPCA to the UCI wine dataset.
High dimensional data is commonly found in real-world applications. Dimensionality reduction methods are often used to study the intrinsic structureof such data. But the results from most of these methods, as 2D projections,
are not directly interpretable. That is, while these methods, such as t-SNE,
visually show us clusters, we don’t at a glance know what determine each cluster. In addition, even though the methods that we use, such as PCA which
preserves data variance and t-SNE which preserves local neighbors, can extract
information from one dataset, they are not suitable for comparative analysis ofmultiple datasets (or multiple groups in a dataset.)
ccPCA (Contrasting Clsuters in PCA), instead, not only can identify thesalient factors/attributes, but also can derive the level of contribution of each
attribute, which allows us to explain what attributes/dimensions dominate the4forming of a group/class. ccPCA has been applied to a real-world dataset collected from both colon cancer patients and healthy people for the study of gut
microbiota composition. Bacteria in a human gut highly influence the development of colorectal cancer. Understanding the impact of bacteria composition ischallenging as the colon environments are influenced by a chain of interactions
between multiple bacteria.
ccPCA can greatly help clinical researchers find such combinational factors.
First, ccPCA can visually show a clear separation between cancerous and normal
samples. More importantly, with ccPCA, we can also explain what bacteria
dominate the separation of the two groups of samples with the contribution
value computed for each type of bacteria.

Measuring and Explaining the Inter-Cluster Reliability ofMultidimensional Projections
Jinwook Seo, HCI Lab, Department of Computer Science and Engineering, Seoul
National University
Explainable AI (XAI) is concerned with the development of Artificial Intelligence systems that can provide a clear and interpretable explanation of theirdecision-making process. Visualizations can play a crucial role in making theprocess more transparent and understandable to humans by providing insight
into how AI systems arrive at their decisions. As visualizations are the key
frontend components of XAI, the graphical integrity of visualizations is an important factor in designing XAI systems.
In this talk, we are going to focus on the graphical integrity of low-dimensional
projections (i.e., 2D scatterplots) of high-dimensional data since most AI systems are built upon large high-dimensional data. We are presenting two novel
metrics, Steadiness and Cohesiveness, for determining the inter-cluster consistency of multidimensional projections (MDPs).
Our aim is to assess how well the relationships between clusters in the highdimensional space are maintained in the low-dimensional projection space. Thisconsistency is crucial for the accuracy of tasks that involve understanding therelationships between clusters, such as identifying cluster connections in theoriginal space from a projected view. However, existing metrics, like Trustworthiness and Continuity, do not provide an adequate measure of inter-cluster
consistency.
Our metrics evaluate two aspects of consistency: Steadiness measures thecorrelation between clusters in the projected space and those in the original
space, while Cohesiveness measures the opposite. We quantify the distortionof randomly selected clusters of different shapes and positions in one space anddetermine how much they are stretched or dispersed in the other space. Our
metrics also produce a reliability map, which visualizes the consistency of the
projection and provides useful information for choosing the proper projection
technique or hyperparameters.
We conclude by proposing a future research direction to design and development of measures of misrepresentations (i.e., visualization integrity measures)for common XAI visualization techniques.

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XAI for the Sciences – Recent Developments in XAI
Klaus-Robert Müller, Machine Learning Group, TU Berlin, Berlin, Germany;
Department of Artificial Intelligence, Korea University, Seoul, Korea; MPIII,
Saarbrücken, Germany; BIFOLD, TU Berlin, Berlin, Germany
In recent years, machine learning (ML) and artificial intelligence (AI) methods have begun to play a more and more enabling role in the sciences and inindustry. In particular, the advent of large and/or complex data corpora has
given rise to new technological challenges and possibilities.
In this talk, I will touch upon the topic of ML applications in the sciences,in particular in medicine and physics. I will also discuss possibilities for extracting information from machine learning models to further our understanding by
explaining AI (XAI) models.
In particular also recent XAI developments are discussed. E.g. Machine
Learning Models for Quantum Chemistry can, by applying XAI, contribute tofurthering chemical understanding. Finally, I will briefly outline perspectives
and limitations.

Steering Deep Neural Networks towards Interactive Data
Labeling, Classifier Debiasing, and Generative Tasks
Jaegul Choo, KAIST, Korea
Human intervention can play a crucial role in utilizing deep neural networks
in one’s own manner. In this talk, I will present my research on (1) leveraging
neural networks for efficient labeling of sentence classification via word-level
attention guidance, (2) debiasing classifiers using synthetic data augmentation
via image-to-image translation, and (3) interactive image generation and editing
via conditional generative adversarial networks.
Finally, I will conclude the talk by discussing challenges and opportunities
towards interpretable and interactive deep neural networks.

Visual and Linguistic Explanations in Semantic MachineIntelligence
Komei Sugiura, Keio University, Japan
Models that generate both visual and linguistic explanations have the potential to provide insight into previously unexplained phenomena and to offeruser-friendly interfaces for novice users. In this talk, our recent work on XAI
models that generate both visual and linguistic explanations is presented.
The focus of the first topic is on visual explanation through the use of lightweight transformers for solar flare prediction. A large-scale solar flare can resultin estimated damage of 160 billion, however its theoretical background is not
fully understood. In order to provide scientific insights to experts on solar
flares, our approach generates attention maps about salient regions based on
the incorporation of a branching network into light-weight transformers.
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Figure 3: Deep flare net.
The second topic of this talk will address future captioning methods forgenerating linguistic explanations. These future captioning models generate
descriptions of future scenes based on video clips or images. Their applications
to cooking videos and domestic service robots are discussed.

XAI - A User- and Domain-Centric PerspectiveJörn Kohlhammer, Fraunhofer IGD, Germany
Successful xAI approaches have to be designed for its target user groupand in most cases for a specific domain. Depending on the domain and userrole, the model, the learning process, and the results need to be explained in acertain way. In some domains, xAI also fulfils the need for trust building andovercoming skepticism.
My talk introduces the medical domain as one such domain, where there
are high-stakes decisions and where black-box algorithms are only acceptable
for small sections of diagnostic processes, e.g. for supporting the detection ofcancer cells in medical imagery. The context of use for medical experts or
patients, and the prior knowledge and stances of these user groups, influences
the level of detail that is required of explanatory visualizations.
My talk also briefly summarizes some results of two earlier Dagstuhl seminars on Visualization for Trustworthy AI. These included, most prominently, amanifesto of the participants of the first workshop, and a first model that distinguishes subjective and objective trust during human AI-supported decision
making.

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Analysis of CNNs and Its Applications
Yasushi Yamaguchi, University of Tokyo, Japan

Figure 4: Examples of unexpected region removal.
This talk introduces three studies aiming at analyzing the internal functionalities of convolutional neural networks for images and some applications basedon the analysis.
The first study is a technique for visualizing class-targeted features of classification networks. Even if we generate a feature visualization from the classtargeted neuron attribution, the result can be contaminated with the otherfeatures located in the different regions in the image. We proposed a region
conscious visualization method which generates a spatial mask as well as a feature visualization related to the target class simultaneously to filter out theunrelated features.
The second one is a pruning technique to obtain a light-weight single-classdetection network from the multi-classes detection network. We developed theway to calculate output target related to objects to be detected for the detection
network and examined the pruning effect in each layer.
The last study is an interactive tuning method to remove unexpected regions
from generated images with GANs. The method allows users to interactively
modify the pretrained generator network by ablating some channels related tothe artifacts. It is capable of modifying the network to avoid watermarks or
captions even if the network has learned with watermarks and captions fromthe training dataset.

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List of Participants• Jaegul Choo (KASIT, Korea)• Mennatallah El-Assady (ETH, Switzeralnd)• Issei Fujishiro (Keio University, Japan)• Seok-Hee Hong (University of Sydney, Australia)• Yun Jang (Sejong University, Korea)• Daniel Keim (University of Konstanz, Germany)• Jörn Kohlhammer (Fraunhofer IGD, Germany)• Bum Chul Kwon (IBM Research, USA)• Kwan-Liu Ma (UC Davis, USA)• Klaus-Robert Müller (TU Berlin, Germany)• Jinwook Seo (Seoul National University, Korea)• Komei Sugiura (Keio University, Japan)• Yasushi Yamaguchi (University of Tokyo, Japan)

Figure 5: Shonan meeting 166 on Vis for XAI

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Meeting Schedule
Day 0: February 5 (Sun)• 19-21 Welcome Banquet
Day 1: February 6 (Mon)• 9-9:30 Introduction• 9:30-10:30 Talk by Menna• 11-11:30 Talk by Kwan-Liu• 11:30-12 Talk by Jinwook• Group Photo Shooting• 13:30-14:00 Talk by Klaus• 14-14:30 Talk by Jaegul• 14:30-15:00 Talk by Komei• 15:30-16 Talk by Joern• 16-17 Open problem session and Group formation 1
Day 2: February 7 (Tue)• 9-9:30 Talk by Yasushi• 9:30-10:30 Open problem session and Group formation 2• 11-12 Group Discussion 1• 13:30-15 Group Discussion 2• 15:30-16:30 Group Discussion 3• 16:30-17 Group Report 1
Day 3: February 8 (Wed)• 9-10 Group Discussion 4• 10:30-11:30 Group Discussion 5• 11:30-12 Group Report 2• 13:30-21 Excursion and Main Banquet
Day 4: February 9 (Thu)• 9-10 Group Discussion 6• 10:30-11:30 Group Discussion 7• 11:30-12 Group Report 3 and Wrap up
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Group 1 Report
Visualization and Machine Learning: Bridging Gaps towards Insight

Summary of discussions
We discuss the need for integrating visualization and machine learning beyond
the current research in XAI, such that visualizations also help to understandthe data and the problem, and allow the domain experts to steer the modeling process and incorporate their knowledge. To solve complex application
problems, domain experts, modeling experts, and visualization experts need towork together by incorporating their complementary strengths to generate new
insights. For example, the domain experts can contribute their data and problem understanding, the modeling experts can contribute their knowledge of themodeling process, and the visualization experts can contribute their expertisein creating visual narratives to bring new insight to enable more effective usageof the AI systems.
We discuss more specific details of the three phases, namely the data andproblem, modeling, and insight generation, where the three expert groups work
together to successfully address the complex application problems. Finally, we
conclude with challenges and future research directions that require a close
collaboration of ML and VIS researchers to set the joint research agenda.

Summary of new findings
In general, the ML researchers can benefit from a better understanding of thetargeted perceptual and interactive tasks facilitated by the visualization approach, which needs to be tightly integrated with the modeling approach. The
VIS researchers can benefit from a more targeted development and use of an
ideal model and parameterization for the automated part of the solution. Therefore, enabling the explanation and integration of automated results with aninteractive visualization approach can enable domain users a truly resonate solution with the combined expertise of both ML and VIS communities.
• Data and Problem Specification: Collaboration between the domain experts, ML experts, and VIS experts is essential for this stage. Specifically,
data specification is crucial for assessing the data quality, such as data
correctness, data completeness, as well as potential biases. For problem
specification, domain experts need to work with the ML experts to define the problem to be solved and select an appropriate model and thenecessary inputs.
Moreover, the data and problem need to be specified using more generalterms than the domain-specific terms so that the ML experts and VIS
experts can focus on the gist of the problem. Therefore, close communication between the three expert groups is crucial for the data and problem
specification to be effective.
• Modeling, Explanation, and Representation: For this process, ML experts
need to work with domain experts to select the best ML models andprovide guidance on how to set important hyperparameters.

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Figure 6: Collaboration between domain experts, ML experts and VIS experts.
XAI techniques can be deployed to enable better understanding of theresults. Namely, VIS experts can help to shortcut the model selection,
training, and optimization process. With the help of VIS experts, themodels and results can be reviewed by the ML experts, with the feedback from the domain experts. Note that an iterative loop between themodeling and insight generation is necessary to develop the appropriate
solutions and to improve the results.
• Insight Generation: Domain experts use the ML and XAI systems to solve
their application problems or to generate new insights. Therefore, effective
interaction with the ML and XAI systems plays an important role in thisprocess.
An appropriate visual analytic interface need be implemented to support
effective interaction between the ML and XAI models, minimizing perceptual biases. However, designing effective Human-AI interfaces posessignificant challenges due to their complexity.

Identified issues and future directions
We now discuss challenges and future research directions, which require a close
collaboration between the domain experts, ML researchers and VIS researchers.
• Interdisciplinary Design Process: It is highly challenging to collaborate
effectively due to the difference in the languages used in different domains.
Therefore, the team of diverse experts needs to actively learn the languageof others to collaboratively identify problems and brainstorm solutions
together. More specifically, the team must discuss initial development,
critical feedback, and refinement together with the domain experts.
Moreover, it is equally important to build a trust between the experts
in different domains, to facilitate the communication. Trust building canstart by better understanding each other’s expertise.

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Figure 7: Research challenges.
• Bias and Uncertainty Amplification: When building a visual, interactive
and explainable ML systems, it is highly challenging to analyze the confound effects of the multi-stage pipelines developed in the system. In
general, the underlying pipelines consist of one or more data processing
and machine learning step, as well as various visualization and interaction steps. Specifically, each of these steps involves a transformation ofsome input (i.e., data) to some output (i.e, representation), which may
introduce some artifacts.
Therefore, a significant challenge for both ML and VIS researchers is toeffectively measure the amplification effects in such multi-stage pipelines.
For example, a possible future research direction is to develop systematicand automatic tools to explore the limits of combining machine learningand visualization.
• Unified Evaluation: The existing evaluation methods for explainable ML
systems use either algorithm-centric evaluation methods (such as quality
metrics and benchmarks) or human-centric evaluation methods (such as
user studies).
A significant challenge is to design unified evaluation techniques that integrates both the fidelity of the ML models as well as their comprehensibility
to the targeted user group. Therefore, a possible future research direction
for ML and VIS researchers is to collaboratively design unified evaluation
schemes that consists of both the algorithm-centric and the human-centricevaluation methodologies.

Group 2 Report
Visual Analytics for Evaluating Large-scale Pretrained Models

Summary of discussions
The use of large-scale pretrained models, such as GPT-3 and ChatGPT, hasbeen beneficial in various AI applications, however there may be a domain
gap between the pretraining data and new tasks, making transfer learning necessary. Finetuning can lead to overfitting, but techniques such as adding a13

fully-connected layer have been proposed to mitigate this issue. In a zero-shotsetting, prompt tuning is used to optimize natural language textual templates
for the model to perform new tasks. However, this approach can be ad-hoc andheuristic, requiring a visual analytic system to analyze the resulting outputs
and form various textual query templates. This system must rely on a blackbox approach for interpreting the model behavior, as internal states may beinaccessible. Pretrained models may also have limitations, requiring additional
information on model uncertainty.
In response, visual analytic approaches can help to address these challenges
in leveraging pretrained models for new tasks, and various potential approaches
and research directions have been discussed.

Summary of new findings
Large-scale pre-trained models are making a significant impact on various applications of artificial intelligence. For example, in natural language processing
domains, GPT-3 [1] and ChatGPT [2,3] are making a huge impact by working as a general AI agent that can answer various questions describing one’s
own tasks. Consequently, information retrieval tasks such as search engines are
now being replaced by a more intelligent agent, such as ChatGPT, that canunderstand various natural language queries whether it is asking the document
summarization, machine translation, document classification, beyond just a simple keyword-based information retrieval of web documents. Another example of
an impactful large-scale pretrained model is Stable Diffusion [4], which can synthesize high-quality images reflecting the users’ own textual description given
as input.
Given these powerful, general-purpose AI models, our task is often domainspecific, and it may involve narrow tasks. For example, in the medical domain,
we may want to automatically detect some phrases and sentences from free-text
medical notes. We may also want to identify the area of nodules from lung
CT images. In these cases, there may exist a significant domain gap between
the training data used during the pretraining stage and the data for new tasks,
limiting the effectiveness of utilizing these preptrained models.
To properly leverage the power of the above-described pretrained models for
new target tasks, we often adopt the transfer learning approach that fine-tunes
the given pre-trained models by using our own, possibly small-scale training
data. Alternatively, we can even ask directly the model to solve our task by describing our tasks in a natural language form, without finetuning the pretrainedmodel at all. For example, we can directly ask the ChatGPT model, by saying “find out the treatment sentences from the following sentences.” Here, theformer approach that uses finetuning can be referred to as a few-shot approach
while the latter as a zero-shot approach.
In each of these two scenarios of leveraging the pretrained models for our own
tasks, new challenges emerge. In the former case, the main issue is potential
overfitting. The training data for finetuning is relatively small compared to
the large-scale data used during pretraining. Thus, it can cause finetuning,or so-called catastrophic forgetting of the general knowledge learned from thepretraining stage. This does not only harm the generalization capability of thedownstream tasks but also prevents the finetuned model from being recycled
for various future use, e.g., adapting to domain-shifted data.
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Researchers recently proposed various techniques to avoid it. For example, we can add a simple fully-connected layer between some adjacent layers of
the pretrained model and train only these newly added layers, while keeping
the original pretrained model parameters intact. Also, one can restrict thosetrainable regions to be a certain layer of the pretrained model. Moreover, researchers have recently proposed the technique to finetune only the existing
fully-connected layer playing a role of transforming an input vector to a query,a key, and a value vector in a self-attention block [5].
However, the current approaches in this process are mostly designed in an
ad-hoc manner, without conducting a systematic, in-depth analysis. Therefore,
a visual analytic approach can help this stage. For example, when finetuning thepretrained model, one can summarize and visualize which layers has changed
most significantly. Furthermore, one can apply an interactive what-if scenario
approach of analyzing the effect of a certain layer by excluding the rest of the
network from the trainable parameters during the finetuning stage.
Additionally, from the perspective of data-item level analysis and interpretability, one can highlight which training or test data items are mainly affected as to whether they are correctly predicted or not, in terms of the original
pretraining tasks and/or the finetuning tasks. Here, the reason why we need tocare about the accuracy of the original pretraining tasks is because our finetuning tasks can change over time or the characteristics of the data for finetuning
can evolve over time. In this manner, visual analytic approaches can help toanalyze how the network behavior changes due to different finetuning strategies.

Identified issues and future directions
In this entire process, the main challenge is that the number of parameters of
the pretrained model is huge, easily going beyond billions of parameters for asingle pretrained model. How we can properly summarize and visualize such alarge-scale set of parameters in the network would be a significant challenge.
On the other hand, the latter case of using the pretrained model in a zeroshot setting poses another important challenge. For example, ChatGPT has
shown surprisingly great potentials across a wide variety of tasks that are described in a natural-language-form input. In this case, to improve the accuracyof the target task, people often perform prompt tuning, which identifies the
optimal natural language textual template to ask the model to perform ourown tasks. However, this problem is inherently ill-defined since the variables tooptimize are those words in a particular order, which are basically categoricalvariables.
Tackling this problem is often performed in an ad-hoc and heuristic manner,
therefore a visual analytic system that can greatly help users form various textual
query templates and analyze the resulting outputs from the pretrained model
would be an interesting research direction. In this process, the visual analytic
system can facilitate the comparisons between different textual templates andallows users to find an optimal template for their own tasks. Moreover, suchhuman-in-the-loop analysis in a visual analytic environment can enlighten users
with a crucial hypothesis towards an optimal prompt template.
However, a caveat in this process is that we may not be able to accessthe internal state of the pretrained model, which is currently the case of ChatGPT. In this case, our visual analytic approach should mainly rely on black-box
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approach for interpreting and understanding the model behavior, which poses
another important challenge in leveraging the pretrained model.
In addition, the strategy of using the pretrained model in a zero-shot setting
bears the limitations of the pretrained model, where it may not perform particularly poorly in certain situations. For example, when performing a sentimentclassification of a product review, the pretrained ChatGPT may give incorrect
answers more frequently for longer sentences than short ones. In that case, one
may have no other choice but to accept such limitations in a zero-shot setting.
Therefore, when deploying the model to end users, it would be desirable to provide additional information about the model uncertainty as to how much thepredicted answer by the pretrained model is trustworthy.
In conclusion, the capability of a large-scale pretrained model is significantly
improving in terms of its wide applicability, towards getting a general artificial
intelligence system. However, as we rely more on this pretrained model, thetechniques and strategies on how we properly utilize these models for our own
tasks and purposes are also fast evolving, having to adapt to various characteristics of these pretrained models. These techniques and strategies are heuristic,
not often based on a standard practice of setting up a clear problem definitionand optimization via a properly designed loss function and its associated training data. Therefore, human-in-the-loop visual analytic approaches can be aneffective alternative.

References[1] Brown, T. et al. Language models are few-shot learners. Advances in neural
information processing systems 33 (2020): 1877-1901.
[2] https://openai.com/blog/chatgpt/[3] Ouyang, L. et al. Training language models to follow instructions with human
feedback. in NeurIPS 2022.
[4] Rombach, R. et al. High-resolution image synthesis with latent diffusion
models. Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition. 2022.
[5] Tumanyan, N. et al. Plug-and-Play Diffusion Features for Text-Driven
Image-to-Image Translation. in arXiv 2022.

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