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…
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OPEN
CC-BY-4.0
Authors
Desta Haileselassie Hagos, Rick Battle, Danda B. Rawat
This paper explores the transformative potential of Generative AI and LLMs, highlighting their advancements, technical foundations, and practical applications across diverse domains. We argue that understanding the full potential and limitations of Generative AI and LLMs is crucial for shaping the responsible integration of these technologies. By addressing critical research gaps in areas such as bias, interpretability, deepfakes, and human-AI collaboration, our work paves the way for an impactful, ethical, and inclusive future of NLP. We envision this research serving as a roadmap for the AI community, empowering diverse domains with transformative tools and establishing a clear path for the responsible evolution of AI.
In our future work, we aim to explore advanced techniques for identifying and mitigating bias in both training data and algorithms to enhance fairness in AI systems. Additionally, we plan to investigate explainable AI approaches and develop new strategies to improve the interpretability of AI models. Building upon our previous line of research on human-autonomy teaming, we will delve into the development of models that facilitate seamless collaboration and interaction between humans and AI. We hope this work encourages researchers across multiple disciplines of the AI community, from both academia and industry, to further explore the broader domain of Generative AI and LLMs.
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
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The repository, book or record this resource was found inside.
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What this resource is made of, when the source lists its parts.
Is version ofdcterms:isVersionOf
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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
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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
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What links to or cites this one, when a source declares it; inferred from the texts held otherwise, and set apart.
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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
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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.
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Who generated this record and when — a statement about the record, not about the resource.
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aimpro-oer-profile/1
LOMv1.0, and the profile this was built against.
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language gate
read the declared field
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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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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.
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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.
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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
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Description8.3
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Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.
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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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