From ChatGPT to CatGPT: The Implications of Artificial Intelligence on Library Cataloging

Abstract

This paper explores the potential of language models such as ChatGPT to transform library cataloging. Through experiments with ChatGPT, the author demonstrates its ability to generate accurate MARC records using RDA and other standards such as the Dublin Core Metadata Element Set. These results demonstrate the potential of ChatGPT as a tool for streamlining the record creation process and improving efficiency in library settings. The use of AI-generated records, however, also raises important questions related to intellectual property rights and bias. The paper reviews recent studies on AI in libraries and concludes that further research and development of this innovative technology is necessary to ensure its responsible implementation in the field of library cataloging.

Full text

ARTICLE
From ChatGPT to CatGPT
The Implications of Artificial Intelligence on Library Cataloging
Richard Brzustowicz
INFORMATION TECHNOLOGY AND LIBRARIES | SEPTEMBER 2023
https://doi.org/10.5860/ital.v42i3.16295
Richard Brzustowicz (rrbrzustowicz@carlow.edu) is Instruction and Outreach Librarian,
Carlow University. © 2023.
ABSTRACT
This paper explores the potential of language models such as ChatGPT to transform library
cataloging. Through experiments with ChatGPT, the author demonstrates its ability to generate
accurate MARC records using RDA and other standards such as the Dublin Core Metadata Element
Set. These results demonstrate the potential of ChatGPT as a tool for streamlining the record creation
process and improving efficiency in library settings. The use of AI-generated records, however, also
raises important questions related to intellectual property rights and bias. The paper reviews recent
studies on AI in libraries and concludes that further research and development of this innovative
technology is necessary to ensure its responsible implementation in the field of library cataloging.
INTRODUCTION
As librarianship continues to evolve in the digital age, the importance of cataloging as a tool for
accessing vast amounts of information cannot be overstated. Unfortunately, this crucial process
can be both labor-intensive and time-consuming, often requiring significant resources. In recent
years, automation and artificial intelligence (AI) technologies have emerged as potential solutions
for streamlining workflows. OpenAI’s language model ChatGPT
1
is one such technology, offering
the potential to automate various tasks, including text generation and even creating working
code.
2
This paper explores the potential applications of ChatGPT in library cataloging, examining
the results of my own experiments using this innovative technology.
LITERATURE REVIEW
Large language models (LLMs) applications have been explored in a range of contexts.
Taecharungroj explores reactions to ChatGPT, noting wide public interest expressed via social
media posts. Due to ChatGPT’s ability to generate accurate information in a conversational tone, it
provides an accessible medium for interacting with an AI resource. These technologies will usher
in substantial changes to how we do our work: “The next evolution of jobs will likely be impacted
by ChatGPT and other innovative AI technologies.”
3
At the same time, the author cautions that
while ChatGPT performs admirably in generating semantically and syntactically correct
information, it does not always provide accurate information. Therefore, this presents an opening
for targeted professional development opportunities in fields that may be affected by LLMs.
Kasneci et al. argue that AI and language models, if used effectively, are an opportunity for better,
more learner-centered education. They caution, however, that LLMs’ potential for bias may
necessitate careful training and review of records by professionals: “[I]f a model is trained on data
that is biased towards certain groups of people, it may produce results that are unfair or
discriminatory towards those groups (e.g., local knowledge about minorities such as small ethnic
groups or cultures can fade into the background).”
4
To counter the potential for bias and abuse,
the authors advise that individuals using these resources should ensure that the data training the

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AI systems are diverse and inclusive. To that end, they recommend making the data open and
available to users. Likewise, the authors recommend regular reviews and continuous updates of AI
models. This will ensure not only that the AI (e.g., ChatGPT) generates accurate information, but it
will also provide a model for continuous improvement of the machine learning algorithm.
5
Recent research has examined the potential of ChatGPT in the library context. Lund and Wang
discuss the implications of ChatGPT for libraries and the academy. According to their findings,
ChatGPT can enhance library search systems’ accuracy and effectiveness by automatically
generating cataloging and metadata for library resources, such as book titles and descriptions.
6
However, they also note the potential ethical and legal implications of using ChatGPT. As ChatGPT
draws on publicly available material, it may include copyrighted content, necessitating careful
evaluation of AI-generated content and coordination with the copyright holder.
According to Atlas, the initial impact of AI in higher education is still not fully understood, but it
does present an opportunity for professionals to incorporate ChatGPT into their workflow to
increase efficiency and streamline tasks. Atlas advises that a well-thought-out plan with clear
goals in mind is crucial when utilizing ChatGPT.
7
These findings suggest that AI resources such as
ChatGPT have the potential to improve library cataloging by automating labor-intensive and time-
consuming tasks. Moreover, they offer opportunities for professional development resources to
ensure that libraries remain up to date with current technologies.
METHODOLOGY
To evaluate ChatGPT’s potential for library cataloging, I used the ChatGPT Free Research Preview
Version (released February 13, 2023) to generate MARC records in RDA for various items. When
asked about its training data, ChatGPT replied:
I was trained on a vast amount of data, including examples of MARC and RDA records, as
well as the rules and guidelines for creating these records. My training data includes a wide
variety of cataloging resources, such as online guides and manuals, training modules, and
real-world examples of MARC and RDA records. This training allows me to generate
accurate and standardized MARC and RDA records based on the information provided
(ChatGPT, personal communication, February 23, 2023).
While this response was interesting, I wanted to test ChatGPT’s claims. I asked it to generate
records for six items, including one with no corresponding WorldCat entry, and compared the
results to records created by human catalogers. Five items had existing MARC records, while the
sixth item was specifically chosen to test ChatGPT’s ability to generate an original record. I
evaluated ChatGPT’s accuracy, efficiency, and ability to handle different types of materials and
cataloging/metadata standards, given that ChatGPT is known to produce both accurate and
inaccurate or misleading information.
8
To test ChatGPT’s ability to create MARC records, I conducted an initial experiment using readily
available materials. I asked ChatGPT to generate a MARC record for the 1996 edition of Anne
Rice’s Interview with the Vampire
9
using RDA (ChatGPT, personal communication, February 23,
2023). The resulting record is shown in table 1, and I compared it to a record in OCLC’s WorldCat,
which is illustrated in table 2. The results of this test indicate that ChatGPT can produce an
accurate and effective record for Interview with the Vampire.

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After this first success, I attempted to generate a MARC record for the 2018 vinyl reissue of David
Bowie’s 1977 album Low
10
using ChatGPT and the RDA standard (ChatGPT, personal
communication, February 23, 2023). The resulting MARC record is presented in table 3, which was
then compared to professional catalogers’ records. Table 4 shows an existing MARC record for
Low in OCLC’s WorldCat. Notable differences were observed between the human-generated and
ChatGPT-generated MARC records, with the ChatGPT record lacking foreign-language headings
and subject headings in certain fields (6xx). This is not surprising, as such tasks require a degree
of personal discernment on the part of the cataloger.
These discrepancies spurred me to investigate the applications further. I refined the question to
test ChatGPT’s ability to generate appropriate Library of Congress call numbers. For this example,
I requested: “Generate a MARC record using RDA that includes Library of Congress call number for
the 1971 German edition of Pedagogy of the Oppressed”
11
(ChatGPT, personal communication,
February 24, 2023). Tables 5 and 6 demonstrate that while ChatGPT may not always “choose” the
same subject access points or consistently format all relevant fields as effectively as a human
cataloger, given proper training and oversight it can be used as an effective supplement to human
cataloging. The accurate formatting of field 050 and appropriate “dummy” call number
(LB875.P442) further demonstrate this technology’s potential for streamlining cataloging and
resource description, given proper training. In this instance, the AI noted that multiple fields
would need to be edited: “Please note that the control number (001) and the date (005) in the
above record are placeholders and should be replaced with actual values when creating the
record” (ChatGPT, personal communication, February 24, 2023).
To further put ChatGPT’s abilities to the test, I asked it to generate a citation for the 2018 Russian
print edition of Cixin Liu’s The Three Body Problem
12
(ChatGPT, personal communication, March 2,
2023). This was a more complex request than the previous ones; it required ChatGPT to extract
and incorporate metadata from a non-Latin character set (Cyrillic) and in a foreign language.
Table 7 shows the MARC record generated by ChatGPT, while table 8 displays the existing MARC
record for the Russian translation of this work found in WorldCat. Although there were differences
between the two records, ChatGPT’s output was comparable to the professional catalogers’ work.
The discrepancies between the records, however, suggested that ChatGPT was not merely
reproducing existing records but creating original MARC records, as it claimed. The results of this
test further demonstrate ChatGPT’s potential as a powerful tool for automating the generation of
accurate metadata records.
During my testing, I discovered that the limited vinyl pressing of alternative rock band Mood
Rings’ 2013 single “Pathos Y Lagrimas”
13
had no WorldCat entry. To see if ChatGPT could generate
an original MARC record for this item, I asked it, “Can you generate a MARC record using RDA for
Mood Rings’ 2013 single ‘Pathos Y Lagrimas’” (ChatGPT, personal communication, March 8, 2023).
Despite the absence of an equivalent WorldCat record, ChatGPT was able to provide a sample
MARC record, which I have included in table 9. This record, complete with sample text for the
Leader and Control Fields (00X), serves as evidence of two important capabilities of ChatGPT: its
ability to generate original cataloging records, and its incorporation of placeholder content in
fields that are collection specific.
ChatGPT’s ability to generate accurate MARC records using both RDA and ersatz “original”
cataloging demonstrates its potential as a cataloging and item description resource. Additionally,
ChatGPT’s versatility is further highlighted by its ability to produce original content in other
metadata formats. When asked if it could generate records using the Dublin Core Metadata

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Element Set, ChatGPT not only confirmed its ability but also provided a sample entry for “Pathos Y
Lagrimas” as seen in table 10. While some modifications may be necessary to cater to collection-
specific demands, this showcases ChatGPT’s potential as a time-saving tool for automating record
generation in multiple formats.
In addition to its ability to generate accurate records adhering to multiple metadata standards, the
results of this study also highlight the potential versatility of ChatGPT as a cataloging and item
description resource. The model’s ability to generate records for different media and in different
languages could prove particularly useful for librarians and other information professionals who
manage diverse collections. Moreover, while catalogers may need to modify the pregenerated
records to suit their specific collections’ requirements, ChatGPT’s user-friendly interface and
accurate record generation suggest that it could be a valuable tool for improving cataloging
workflows and increasing efficiency. With further development and refinement, ChatGPT has the
potential to significantly enhance the capabilities of information professionals and improve the
discoverability of library collections.
RESULTS
This study provides evidence that ChatGPT can generate accurate records that conform to
multiple metadata standards. The model can extract essential metadata, including title, author,
publisher, publication date, subject headings, and other descriptive elements, with precision.
Additionally, my research reveals that ChatGPT’s ability to generate MARC records is not limited
to specific formats or languages, as it successfully created MARC records for various media and
materials in different languages, such as English, German, and Russian. ChatGPT was able to
generate both accurate existing authority records and entirely original ones, and it could generate
records using both RDA and Dublin Core standards.
According to ChatGPT, it has been trained on data from various catalogs, including OCLC’s
WorldCat, the Library of Congress, the National Library of Medicine, the British Library, COPAC
(UK Academic and National Library Catalog), Europeana, and the HathiTrust Digital Library
(ChatGPT, personal communication, March 9, 2023). This poses a unique challenge, as these
catalogs may have different policies on access and reuse of their data. For example, OCLC’s
CatExpress is a subscription-based automated cataloging system. If ChatGPT or a future “CatGPT”
draws on OCLC’s data and makes it available for free, it may raise questions about OCLC’s
copyright holdings. Additionally, while ChatGPT may generate records for materials available on
the public internet, such as “Pathos Y Lagrimas,” questions remain regarding how to credit the
intellectual labor necessary for creating these records.
My comparison of ChatGPT-generated MARC records against manually created records by
professional catalogers had positive results. While the accuracy of the ChatGPT-generated records
was comparable to those of the manually created records, notable differences existed in how
subject access points were assigned. This suggests that ChatGPT has the potential to provide new
methods for growing the discipline of library cataloging by automating the more rote, labor-
intensive and time-consuming tasks (for example, copy cataloging). In future studies, it may be of
interest to the discipline to further test the applications of AI-generated MARC records on a
catalog-wide scale.
While ChatGPT has the potential to streamline aspects of the cataloging process, it is not a
complete replacement for human catalogers. The records generated by ChatGPT can serve as
effective starting points, but they often contain discrepancies when compared to professional

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catalogers’ records. For example, while the placeholder text in fields 001 and 005 can be useful, it
may not match the formatting standards used by specific library collections. Nonetheless,
ChatGPT-generated records can be accurate and effective in classifying information that is not
specific to any collection, such as call numbers.
BIAS
While ChatGPT shows promise as a tool for generating MARC and Dublin Core-style records, it is
also limited by its training data. At present, ChatGPT searches public records (e.g., WorldCat). As a
result, any records it generates will draw on existing professional catalogers’ records. If a record is
incomplete or contains bias—even via omission—then ChatGPT will reflect those biases in its
output. This will necessitate close monitoring of both original records and those which ChatGPT
has created through virtual copy cataloging.
ChatGPT’s ability to copy and generate records is rooted in its machine learning-based
understanding of cataloging and metadata standards. This AI system uses training data from
OCLC’s WorldCat to generate records, which means that the quality of the generated records is
dependent on the quality and comprehensiveness of the training data.
14
Biases or limitations in
the training data can result in biased or incomplete records. For example, if the training data is
restricted to certain regions, languages, or publishers, the generated records may not reflect the
full diversity of a library’s collections. Similarly, biases in subject headings, descriptors, or other
fields in the training data may also manifest in the generated records. While ChatGPT itself has no
biases, it is possible for biases to be introduced through the training data, which makes it essential
for librarians and other information professionals to curate and update the data regularly.
To address these potential biases, information professionals training a large language model
should curate the training data carefully and periodically review and update it to ensure it is
comprehensive, representative, and unbiased. They may also need to manually review and edit
generated records to correct any biases or inaccuracies identified. This approach would provide
new opportunities for the profession to highlight diversity, equity, and inclusion in the
development and use of AI. While an AI may not have biases, biases of the people involved in
training and applying the AI could affect the generated content.
Like other machine learning models, ChatGPT acquires its biases from external sources as it can
only respond to the data it has been trained on, which may reflect human errors or intentions.
Therefore, while ChatGPT could streamline and improve the record generation process,
information professionals should approach its use with awareness of its limitations and potential
biases. To ensure the accuracy, comprehensiveness, and fairness of the generated records,
information professionals should take proactive measures to mitigate any biases and errors.
DISCUSSION
The results of this study have significant implications for library cataloging. The ability to
accurately create descriptive records using ChatGPT could significantly reduce the time and
resources required for copy cataloging; this could free up library workers to focus on other
important tasks, such as collection development, user services, and metadata management.
Moreover, ChatGPT could improve the accuracy and consistency of records in library catalogs. As
ChatGPT follows established cataloging rules, records created by the model are less likely to
contain errors or inconsistencies; this could lead to improved search and discovery experiences
for library users, as well as better interoperability between library catalogs and other systems.

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The intellectual property concerns surrounding ChatGPT’s ability to generate content are
multifaceted. One concern is the potential for copyright infringement, as ChatGPT’s detailed
descriptions of original works may be too like the originals, leading to legal issues for those who
use the generated content without proper attribution or permission. This concern is particularly
heightened for copyrighted works like books or music, where even small portions of the work can
be protected. Therefore, it is crucial for ChatGPT’s output to be thoroughly reviewed and vetted
before being used in any public-facing materials.
Another concern is the possibility of misattribution of authorship. ChatGPT’s use of Dublin Core to
describe original works could lead to disputes over ownership and potentially even legal action if
it generates a description that attributes authorship to the wrong person or entity. To prevent
such conflicts, information professionals should ensure that the metadata generated by ChatGPT
accurately reflects the authorship and ownership of the original work. This can be done by
reviewing and editing ChatGPT’s output to ensure that the metadata is correct before it is shared
publicly.
The ownership of the generated content is also a concern, as it is not clear who owns the content
created by ChatGPT. As a machine learning model, ChatGPT generates content based on the data it
has been trained on, raising questions about the ownership of the content it produces.
Establishing clear guidelines for ownership and use of the generated content can help avoid any
potential disputes over ownership and ensure that appropriate attribution and permissions are
obtained; this is particularly important given the potential commercial value of the content that
ChatGPT can produce. Furthermore, it is essential to consider ethical and legal implications of the
generated content, such as data privacy and protection, and to ensure that these concerns are
addressed when designing guidelines for ownership and use.
Finally, there is the potential for unintentional disclosure of sensitive or confidential information.
ChatGPT’s ability to generate detailed descriptions of original works may inadvertently disclose
unpublished findings or proprietary information, potentially causing harm to the author or
institution. To mitigate this risk ChatGPT’s output must be carefully reviewed and edited to ensure
that it does not inadvertently disclose sensitive information. Implementing appropriate data
security measures and access controls may help prevent unauthorized access to sensitive
information.
CONCLUSION
The study demonstrates that ChatGPT has the potential to significantly streamline the cataloging
process in libraries by generating accurate and consistent records for a diverse range of materials.
However, it should be used as an auxiliary tool in conjunction with human cataloging efforts to
ensure the highest level of accuracy and impartiality. Regular monitoring and evaluation of the
model are necessary to detect any potential biases or limitations in the training data. By applying a
careful and considered approach to its use, librarians and other information professionals can
leverage ChatGPT to enhance the efficiency and effectiveness of cataloging processes, ultimately
benefiting library and information center patrons.
The accurate and comprehensive MARC records produced by ChatGPT highlight its potential to
enhance the effectiveness of library cataloging systems. By extracting metadata information such
as author, publisher, subject headings, title, and other descriptive components with high precision,
the technology can improve the search and discovery experience for library users. As with any
machine learning model, though, there is a risk of bias that needs to be considered when utilizing

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ChatGPT. Therefore, it is crucial to monitor and evaluate the training data used to build the model,
to ensure that it is extensive, impartial, and representative.
The use of Dublin Core to describe original works is another factor that should be taken into
consideration when using ChatGPT for cataloging. The model’s potential to generate accurate
metadata records is reliant on the quality of the input data, including the use of standardized
vocabularies like Dublin Core. The use of any metadata standard, such as Dublin Core, may
introduce biases and errors that could affect quality of ChatGPT-generated records. Therefore,
information professionals should ensure that the metadata standards used by ChatGPT are
appropriate for the materials being cataloged and that the resulting records are regularly
reviewed and evaluated for accuracy and impartiality.
ChatGPT’s accurate and comprehensive metadata records have the potential to enhance the
effectiveness of library cataloging systems and improve the search and discovery experience for
library users. However, as with any machine learning model, there is a risk of bias that needs to be
considered when utilizing ChatGPT. To ensure error-free results, librarians and other information
professionals should approach ChatGPT’s application systematically, by monitoring and evaluating
the training data used to develop the model’s capabilities and by regularly curating and updating
those data. Additionally, periodic inspection and amendment of the generated records may be
necessary to avoid inaccuracies and discrepancies arising from biases in the training data. By
taking a careful and deliberate approach to ChatGPT’s use, we can ensure that the resulting
metadata records are as accurate, comprehensive, and unbiased as possible, while also being
mindful of the intellectual property considerations inherent in using a machine learning model to
generate content.

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TABLES
Table 1. ChatGPT-generated MARC record for the 1996 reprint of
Interview with the Vampire by Anne Rice
Field
Data
LDR
01110cam a2200325 a 4500
001
2740930
005
20040429112254.0
008
960304s1996 nyu 000 1 eng d
010
96011406
020
0345337662 (pbk.)
040
DLC $c DLC $d DLC
100 1
Rice, Anne.
245 1 0
Interview with the vampire / $c by Anne Rice.
260
New York : $b Ballantine Books, $c 1996.
300
372 p. ; $c 18 cm.
336
text $b txt $2 rdacontent
337
unmediated $b n $2 rdamedia
338
volume $b nc $2 rdacarrier
650 0
Vampires $v Fiction.
650 0
Horror tales.
650 0
Vampires $v Fiction.
650 0
Horror tales.

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Table 2. WorldCat MARC record for the 1996 reprint of Interview with the Vampire by Anne Rice
Field
Data
LDR
c a22 0 4500
001
1052676753
005
20220606044740.3
008
960903r19761996nyua b 001 0 eng
010
$z 75036792
020
9780394498218 (hardcover) ; 0394498216 (hardcover)
040
NTG $b eng $c NTG $d OCLCO $d OCLCF $d OCLCQ $d OCLCO $d OCLCA
049
CRCB
050 4
PS3568.I265 $b I5 1996
082 1 4
[E]
082 0 4
FIC RICE
090
$b
100 1
Rice, Anne, $d 1941-2021.
245 1 0
Interview with the vampire / $c by Anne Rice.
250
Anniversary ed.
260
New York : $b Knopf, $c 1996.
300
340 pages ; $c 22 cm
336
text $b txt $2 rdacontent
337
unmediated $b n $2 rdamedia
338
volume $b nc $2 rdacarrier
500
“Published May 5, 1976 ... reprinted eleven times”--Title...
520 0
In a remote room in a large city, a young reporter sits fa...
600 0
Lestat
$c (Vampire), $d 1760- $v Fiction.
650 0
Vampires $v Fiction.
650 0
Imaginary interviews.
650 6
Vampires $v Romans, nouvelles, etc.
650 6
Entretiens imaginaires.
600 0
Lestat
$c (Vampire), $d 1760- $2 fast $0 (OCoLC)fst00996665
650 7
Imaginary interviews. $2 fast $0 (OCoLC)fst00967566
650 7
Vampires. $2 fast $0 (OCoLC)fst01163968
655 7
Fiction. $2 fast $0 (OCoLC)fst01423787
655 7
Horror fiction. $2 fast $0 (OCoLC)fst01921684
655 7
Horror fiction. $2 lcgft

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Table 3. ChatGPT-generated MARC record for Low by David Bowie (vinyl)
Field
Description
LDR
01523njm a2200373 i 4500
001
ocn1029184931
005
20220130152943.0
007
sd fsngnn|||eu
008
180430s2018 xx umm n eng d
024 1
00602557675302
028 2
LCRHLP14
028 3
LCRHCD14
040
##$aDLC$beng$cDLC$dOCLCO$dOCoLC
049
##$aOCOZ
100 1
Bowie, David,$eperformer.
245 10
Low$h[sound recording] /$cDavid Bowie.
264 31
[Place of publication not identified] :$b[Label not identified],$c[2018]
300
##$a1 sound disc :$banalog, 33 1/3 rpm ;$c12 in.
336
##$anotated music$bntm$2rdacontent
337
##$audio$sanc$2rdamedia
338
##$aaudio disc$bac$2rdacarrier
500
##$aOriginally released in 1977.
500
##$aTitle from container.
500
##$aLyrics and full credits on container insert.
511 0
David Bowie, vocals, saxophones, guitars, piano, and Chamberlin ; Brian Eno,
synthesizer, guitar treatments, and piano ; Carlos Alomar, rhythm guitar ; George
Murray, bass guitar ; Dennis Davis, drums and percussion.
518
##$aRecorded August-September 1976 at the Château d’Hérouville, Hérouville,
France.
650 _0
$aRock music$y1971-1980.
650 _0
$aRock music$y2011-2020.
650 _0
$aRock music$zEngland.
650 _0
$aVinyl LPs.
700 1
Eno, Brian,$eperformer.
700 1
Alomar, Carlos,$eperformer.
700 1
Murray, George,$eperformer.
700 1
Davis, Dennis,$eperformer.
730 0
Low.
902
##$aMusic$new.

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Table 4. WorldCat MARC record for Low by David Bowie (vinyl)
Field
Data
LDR
02213cjm a2200529 i 4500
005
20220317111101.1
008
180321t20182017enkrcna zxx d
040
## $aSFR $beng $erda $cSFR $dOCLCF $dOCLCA $dOCLCQ $dOCLCO
024 3
$a0190295842918
028 02
$a0190295842918 $bParlophone
049
## $aCRCB
100 1
$aBowie, David, $ecomposer, performer, producer.
245 10
$aLow / $cDavid Bowie.
264 #1
$a[London] : $bParlophone Records, $c[2018].
264 #4
$c℗2017
300
## $a1 audio disc ; $c12 in.
336
## $aperformed music $bprm $2rdacontent
337
## $aaudio $bs $2rdamedia
338
## $aaudio disc $bsd $2rdacarrier
340
## $avinyl
344
## $aanalog $2rdatr
344
## $c33 1/3 rpm
344
## $dmicrogroove
344
## $gstereo $2rdacpc
500
## $aReissue, remastered, originally released in 1977.
508
## $aProduced by David Bowie and Tony Visconti.
511 0
$aDavid Bowie ; with accompanying musicians.
505 0
$tSpeed of life -- $tBreaking glass -- $tWhat in the world -- $tSound and vision --
$tAlways crashing in the same car -- $tBe my wife -- $tA new career in a new town -
- $tWarszawa -- $tArt decade -- $tWeeping wall -- $tSubterraneans.
538
## $aVinyl disc, double-sided; turntable required.
650 0
$aRock music $y1971-1980.
650 0
$aPopular music $y1971-1980.
650 0
$aProgressive rock music.
650 6
$aRock (Musique) $y1971-1980.
650 6
$aMusique populaire $y1971-1980.
650 6
$aRock progressif.
650 7
$aPopular music. $2fast $0(OCoLC)fst01071422

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Field
Data
650 7
$aProgressive rock music. $2fast $0(OCoLC)fst01078743
650 7
$aRock music. $2fast $0(OCoLC)fst01099204
648 7
$a1971-1980 $2fast
655 7
$aPopular music. $2fast $0(OCoLC)fst01726706
655 7
$aProgressive rock (Music) $2fast $0(OCoLC)fst01726709
655 7
$aPopular music. $2lcgft
655 7
$aProgressive rock (Music) $2lcgft
655 7
$aMusique populaire. $2rvmgf
655 7
$aRock progressif. $2rvmgf
700 1
$aVisconti, Tony, $eproducer.

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 13
BRZUSTOWICZ
Table 5. ChatGPT-generated MARC record for the 1971 German edition of
Pedagogy of the Oppressed by Paolo Freire
Field
Description
LDR
00000nam a2200000 4500
001
ocm04045077
003
OCoLC
005
2023-02-23
008
711215s1971 gw 000 0 ger
020
$a9783801200059
020
$a3801200054
035
$a(OCoLC)40053172
040
$aDLC$cDLC$dOCoLC$dOCLCF
041 1
$ager$hpor
042
$apcc
043
$ae-gx---
050 00
$aLB875.P442
100
1$aFreire, Paulo.
240 10
$aPedagogy of the oppressed.$lGerman
245 10
$aPädagogik der Unterdrückten /$cPaulo Freire ; aus dem Portugiesischen von
Walter Jens.
260
$aStuttgart :$bKlett-Cotta Verlag,$c1971.
300
$a218 pages ;$c21 cm
336
$atext$btxt$2rdacontent
337
$aunmediated$bn$2rdamedia
338
$avolume$bnc$2rdacarrier
546
$aIn German.
650 0
$aEducation$xPhilosophy.
650 0
$aPopular education$zBrazil.
650 0
$aSocial change.
651 0
$aBrazil$xSocial conditions.
700 1
$aJens, Walter,$d1923-2013,$etranslator.

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 14
BRZUSTOWICZ
Table 6. WorldCat MARC record for the 1971 German edition of
Pedagogy of the Oppressed by Paolo Freire
Field
Description
LDR
cam Mi
001
63439859
005
20220106164408.5
008
830119s1971 gw 000 0 ger d
040
NLGGC $b dut $e fobidrtb $c NLGGC $d HEBIS $d DEBBG $d OCLCQ $d
OCLCO
015
72,A06,0123 $2 dnb
016 7
720015707 $2 DE-101
020
3783103746
020
9783783103748
041 1
1 ger $h por
084
5,3 $2 ssgn
084
BU 1410 $2 rvk
084
DD 6455 $2 rvk
084
DT 1000 $2 rvk
090
$b
049
CRCB
100 1
Freire, Paulo, $d 1921-
245 10
Pa dagogik der Unterdru ckten / $c Paulo Freire ; mit einer Einf. von Ernst
Lange ; [autor. U bertr. aus dem Portugesischen].
260
Stuttgart : $b Kreuz-Verlag, $c 1971.
300
218 p. ; $c 21 cm
650 04
Onderdrukking.
650 04
Pedagogiek.
650 7
Bildungspolitik. $2 fes
650 7
Bildungsziel. $2 fes
650 7
Erziehung. $2 fes
650 7
Pa dagogik $2 gnd
650 7
Unterdrückung $2 gnd
650 7
Alphabetisierung $2 gnd
650 7
Politische Bildungsarbeit $2 gnd
650 7
Emanzipatorische Erziehung $2 gnd
651 7
Brasilien $2 gnd
650 17
Pedagogiek. $2 gtt

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 15
BRZUSTOWICZ
Field
Description
650 17
Onderdrukking. $2 gtt
655 4
Unterdrückte.
655 7
Unterdrückte. $2 swd
029 0
NLGGC $b 830230688
029 1
DEBBG $b BV005751307
029 1
DEBSZ $b 003425525
029 1
HEBIS $b 045711100

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 16
BRZUSTOWICZ
Table 7. ChatGPT-generated for the 2018 print edition in Russian of
The Three Body Problem by Cixin Liu
Field
Data
LDR
cam i
001
1037948853
005
20220402070218.7
008
180529s2018 ru 000 1 rus d
040
CLE $b eng $e rda $c CLE $d OCLCF $d OCL $d OCLCQ $d OCLCO
066
$c (N
020
9785040891122
020
5040891121
041 1
$h chi $a rus
043
a-cc---
090
$b
049
CRCB
100 1
$a Liu, Cixin, $e author.
240 10
$a San ti. $l Russian
245 10
$a Задача трех тел / $c Лю Цысинь.
245 10
$a Zadacha trekh tel / $c Liu TSysinʹ.
264 1
$a Москва : $b Э, $c 2018.
264 1
$a Moskva : $b E , $c 2018.
300
$a 462 pages ; $c 22 cm
336
$a text $b txt $2 rdacontent
337
$a unmediated $b n $2 rdamedia
338
$a volume $b nc $2 rdacarrier
490 0
$a Sci-Fi Universe
650 0
Human-alien encounters, fiction
650 0
Imaginary wars and battles, fiction
651 0
China -- History -- Cultural Revolution, 1966-1976 -- Fiction
650 6
Rencontres avec les extraterrestres, romans, nouvelles, etc.
650 6
Guerres et batailles imaginaires, romans, nouvelles, etc.
651 6
Chine -- Histoire -- 1966-1976 (Révolution culturelle) -- Romans, nouvelles, etc.
650 7
Human-alien encounters. $2 fast $0 (OCoLC)fst00963475
650 7
Imaginary wars and battles. $2 fast $0 (OCoLC)fst00967580
651 7
China. $2 fast $0 (OCoLC)fst01206073
647 7
Cultural Revolution (China : 1966-1976) $2 fast $0 (OCoLC)fst01352662

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 17
BRZUSTOWICZ
Field
Data
648 7
1966-1976 $2 fast
655 7
Fiction. $2 fast $0 (OCoLC)fst01423787
655 7
History. $2 fast $0 (OCoLC)fst01411628
655 7
Science fiction. $2 fast $0 (OCoLC)fst01726489
655 7
Science fiction. $2 lcgft
700 1
Translation of: Liu, Cixin. San ti.

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 18
BRZUSTOWICZ
Table 8. WorldCat record for the 2018 print edition in Russian of
The Three Body Problem by Cixin Liu
Field
Data
LDR
cam i
001
1037948853
005
20220402070218.7
008
180529s2018 ru 000 1 rus d
040
CLE $b eng $e rda $c CLE $d OCLCF $d OCL $d OCLCQ $d OCLCO
066
$c (N
020
9785040891122
020
5040891121
041 1
$h chi $a rus
043
a-cc---
090
$b
049
CRCB
100 1
$a Liu, Cixin, $e author.
240 10
$a San ti. $l Russian
245 10
$a Задача трех тел / $c Лю Цысинь.
245 10
$a Zadacha trekh tel / $c Liu TSysinʹ.
264 1
$a Москва : $b Э, $c 2018.
264 1
$a Moskva : $b E , $c 2018.
300
$a 462 pages ; $c 22 cm
336
$a text $b txt $2 rdacontent
337
$a unmediated $b n $2 rdamedia
338
$a volume $b nc $2 rdacarrier
490 0
$a Sci-Fi Universe
650 0
Human-alien encounters, fiction
650 0
Imaginary wars and battles, fiction
651 0
China -- History -- Cultural Revolution, 1966-1976 -- Fiction
650 6
Rencontres avec les extraterrestres, romans, nouvelles, etc.
650 6
Guerres et batailles imaginaires, romans, nouvelles, etc.
651 6
Chine -- Histoire -- 1966-1976 (Révolution culturelle) -- Romans, nouvelles, etc.
650 7
Human-alien encounters. $2 fast $0 (OCoLC)fst00963475
650 7
Imaginary wars and battles. $2 fast $0 (OCoLC)fst00967580
651 7
China. $2 fast $0 (OCoLC)fst01206073
647 7
Cultural Revolution (China : 1966-1976) $2 fast $0 (OCoLC)fst01352662

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 19
BRZUSTOWICZ
Field
Data
648 7
1966-1976 $2 fast
655 7
Fiction. $2 fast $0 (OCoLC)fst01423787
655 7
History. $2 fast $0 (OCoLC)fst01411628
655 7
Science fiction. $2 fast $0 (OCoLC)fst01726489
655 7
Science fiction. $2 lcgft
700 1
Translation of: Liu, Cixin. San ti.

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 20
BRZUSTOWICZ
Table 9. ChatGPT’s original MARC record for “Pathos Y Lagrimas” by Mood Rings
Field
Data
LDR
02461cjm a2200517 a 4500
001
123456789
005
20181025171247.0
007
sd fungnnmmned
008
181025s2013 nyurcn n zxx d
024 8
767870622805
035
(OCoLC)851415773
040
DLC $b eng $c DLC $e rda $d DLC
041 0
$g spa
043
n-mx---
047
vd $a pp
048
ka01
049
DLC
050 00
M1630.18 $b .M66 2013
082 00
782.421660972
100 1
Mood Rings $4 prf
245 10
Pathos Y Lagrimas / $c Mood Rings.
246 3
333
264 1
[Brooklyn, NY] : $b Mexican Summer, $c [2013]
300
1 audio disc : $b analog, 33 1/3 rpm ; $c 7 in.
336
performed music $b prm $2 rdacontent
337
audio $b s $2 rdamedia
338
audio disc $b sd $2 rdacarrier
500
Title from label.
500
B-side label has number “333.”
511 0
Mood Rings.
500
“Limited edition” “500 copies.”
650 0
Rock music $z Mexico $y 2011-2020.
655 7
Rock music. $2 lcgft
655 7
Popular music. $2 lcgft
700 1
Mood Rings. $4 prf
710 2
Mexican Summer (Firm) $4 pbl.

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 21
BRZUSTOWICZ
Table 10. ChatGPT’s original record using the Dublin Core for “Pathos Y Lagrimas” by Mood Rings
Field
Data
Title
Pathos Y Lagrimas / Mood Rings
Creator
Mood Rings
Contributor
Mexican Summer
Date
2013
Type
Sound
Format
audio/vinyl
Identifier
333
Language
eng
Relation
Mexican Summer
Source
Mexican Summer
Coverage
Atlanta, GA
Rights
All rights reserved

INFORMATION TECHNOLOGY AND LIBRARIES SEPTEMBER 2023
FROM CHATGPT TO CATGPT 22
BRZUSTOWICZ
ENDNOTES
1
Viriya Taecharungroj, “What Can ChatGPT Do? Analyzing Early Reactions to the Innovative AI
Chatbot on Twitter,” Big Data and Cognitive Computing 7, no. 1: 35,
https://doi.org/10.3390/bdcc7010035. GPT (generative pretrained transformer) technologies
are structured in a way that involves training language models on a large body of data.
Subsequently, fine-tuning processes are applied to enhance the model’s performance on
specific tasks and domains. Fine-tuning processes are then applied to enhance the model’s
performance on specific tasks and domains.
2
Roberto Gozalo-Brizuela and Eduardo C. Garrido-Merchan, “ChatGPT Is Not All You Need. A State
of the Art Review of Large Generative AI Models,” arXiv:2301.04655v1 [cs.LG]: 15,
https://doi.org/10.48550/arXiv.2301.04655.
3
Taecharungroj, “What Can ChatGPT Do?”
4
Enkelejda Kasneci et al., “ChatGPT for Good? On Opportunities and Challenges of Large Language
Models for Education,” EdArXiv (January 30, 2023), https://doi.org/10.35542/osf.io/5er8f.
5
Kasneci et al., “ChatGPT for Good?”
6
Brady D. Lund and Ting Wang, “Chatting about ChatGPT: How May AI and GPT Impact Academia
and Libraries?” Library Hi Tech News 40 (2023), no. 3: 26–29, https://doi.org/10.1108/LHTN-
01-2023-0009.
7
Stephen Atlas, “ChatGPT for Higher Education and Professional Development: A Guide to
Conversational AI,” (2023): 106–7, https://digitalcommons.uri.edu/cba_facpubs/548/.
8
Ali Borji, “A Categorical Archive of ChatGPT Failures,” arXiv.2302.03494 [cs.CL]: 11,
https://doi.org/10.48550/arXiv.2302.03494.
9
Anne Rice, Interview with the Vampire (New York: Alfred K. Knopf, 1996).
10
David Bowie, Low, recorded September–October 1976, RCA Victor, 1977, vinyl LP.
11
Paolo Freire, Pdagogik Der Unterdrckten (Stuttgart: Kreuz-Verlag, 1971).
12
Cixin Liu, Задача Трех Тел (Moscow: Sci-Fi Universe, 2018).
13
Mood Rings, “Pathos Y Lagrimas,” recorded ca. 2013, Mexican Summer, 2013, vinyl single.
14
Tom B. Brown et al., “Language Models Are Few-Shot Learners,” arXiv.2005.14165v4 [cs.CL]:
8–9, https://doi.org/10.48550/arXiv.2005.14165.