Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives
The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our paper contributes to providing a hol…
Licence
OPEN
CC-BY-4.0
Authors
Desta Haileselassie Hagos, Rick Battle, Danda B. Rawat
Our research has identified several key areas that require attention to ensure the ethical integration of Generative AI and LLMs. These areas include addressing issues such as bias and fairness in outputs, the necessity for models to provide explanations for their reasoning, and the challenges associated with adapting these models to diverse situations and domains. Furthermore, considerations regarding data privacy, security, and the potential for misuse in areas such as deepfakes require careful attention. Addressing these challenges through advancements in areas we have proposed, such as improved bias detection and the development of interpretable models, holds significant promise. Proactively tackling these issues is essential to ensuring that AI development is not only technically advanced but also beneficial to society. This includes developing clear metrics to assess model performance, enhancing their interpretability, and prioritizing user privacy and security. By incorporating ethical considerations into AI development, we pave the way for their responsible deployment across various domains, including healthcare, recruitment, and content creation. This will foster a future where AI serves as a positive force for societal good, promoting inclusivity and making a real impact.
Metadata record
One description, two standard projections
Built from what the sources declared and what the gates observed.
Nothing absent has been filled in here.
1 value read out of the text by the enrichment rules and 52 links to or from other resources — a lab's files, the pages it links, the works it cites stand under their elements, marked inferred, and are kept apart in the exports.
DCMI Metadata Terms. Dublin Core has no element that separates the original file from the text extracted out of it, and none for LOM's educational characterisation. Both survive here as provenance statements and in the record itself, not in the projection.
the standard ↗
The groups below are this library's, for reading. DCMI Terms itself has no categories; each term keeps its standard name.
The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our paper contributes to providing a holistic perspective on the technical foundations, practical applications, and emerging challenges within the evolving landscape of Generative AI and LLMs. We believe that understanding the generative capabilities of AI systems and the specific context of LLMs is crucial for researchers, practitioners, and policymakers to collaboratively shape the responsible and ethical integration of these technologies into various domains. Furthermore, we identify and address main research gaps, providing valuable insights to guide future research endeavors within the AI research community.
The abstract, and the depositor's additional notes after it when the source has a field for them.
Where the work was published, in the source's own words: a journal with its volume and pages, a conference, an imprint.
Relations and custody
1/9
Is part ofdcterms:isPartOf
none found — the source did not declare it
The repository, book or record this resource was found inside.
Has partdcterms:hasPart
none found — the source did not declare it
What this resource is made of, when the source lists its parts.
Is version ofdcterms:isVersionOf
none found — the source did not declare it
Has versiondcterms:hasVersion
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Referencesdcterms:references
inferred
read its reference list, line 385citation
arXiv:1910.13461
arXiv:2110.14168
arXiv:2107.03374
arXiv:2001.08361
arXiv:1312.6114
arXiv:2201.08239
arXiv:1511.06434
arXiv:1710.10196
arXiv:1701.06538
arXiv:2108.05036
arXiv:2306.01708
arXiv:2106.02834
and 40 more, every one of them in the export
Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.
Is referenced bydcterms:isReferencedBy
none found — the source did not declare it
What links to or cites this one, when a source declares it; inferred from the texts held otherwise, and set apart.
Requiresdcterms:requires
none found — the source did not declare it
What this resource needs in order to be used, when a source declares it. The files a lab works on are inferred, and stand under it apart.
Is required bydcterms:isRequiredBy
none found — the source did not declare it
What needs this resource, when a source declares it. The lab a component belongs to is inferred, and stands under it apart.
Provenancedcterms:provenance
this engine
source
conversion
latexml-html
Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it.
Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.
IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other.
the standard ↗
The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our paper contributes to providing a holistic perspective on the technical foundations, practical applications, and emerging challenges within the evolving landscape of Generative AI and LLMs. We believe that understanding the generative capabilities of AI systems and the specific context of LLMs is crucial for researchers, practitioners, and policymakers to collaboratively shape the responsible and ethical integration of these technologies into various domains. Furthermore, we identify and address main research gaps, providing valuable insights to guide future research endeavors within the AI research community.
The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.
Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.
3 Meta-metadata
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Identifier3.1
this engine
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URI: tag:aim-pro.eu,2026:oer/cf812c7c731a/record
The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
Contribute3.2
this engine
source
AIM-PRO WP3 OER harvester (arXiv) — creator
Who generated this record and when — a statement about the record, not about the resource.
Metadata schema3.3
LOMv1.0
aimpro-oer-profile/1
LOMv1.0, and the profile this was built against.
Language3.4
language gate
read the declared field
en
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conversion
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text/markdown
One value per form held: the original as the source published it, and the Markdown this engine extracted.
Size4.2
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latexml-html
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Where the source serves it.
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Software or hardware needed to use it. No source declares it.
Installation remarks4.5
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Interactivity type5.1
not collected — this library does not fill it
Active, expositive or mixed. A judgement about how the resource is used; no source declares it.
Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
The SPDX id, the licence URI and the conditions. LOM has no element for any of the three, so this is where they survive.
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Kind7.1
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Resource7.2
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read its reference list, line 385citation
references: arXiv:1910.13461
references: arXiv:2110.14168
references: arXiv:2107.03374
references: arXiv:2001.08361
references: arXiv:1312.6114
references: arXiv:2201.08239
references: arXiv:1511.06434
references: arXiv:1710.10196
references: arXiv:1701.06538
references: arXiv:2108.05036
references: arXiv:2306.01708
references: arXiv:2106.02834
and 40 more, every one of them in the export
The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.
8 Annotation
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Entity8.1
not collected — this library does not fill it
Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.
Date8.2
not collected — this library does not fill it
Description8.3
not collected — this library does not fill it
9 Classification
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Purpose9.1
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Taxon path9.2
not collected — this library does not fill it
Where the competency framework goes. Empty in the record for the reason above.
Description9.3
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Keyword9.4
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standard, restructured, or taken from the repository — and what was done
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the harvester missed.
Status
Field
Why
Not available
rights_holder
no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it
Not available
publisher
the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim
Not available
educational
the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them