INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 2156
Udk 004.8
LEARNING MACHINES: HOW MODELS LIKE CHATGPT WORK
Iskandarov Elzod Olimjon ugli
Academic lyceum of Samarkand Branch of
Tashkent University of Information Technologies
2
nd
year student
Email:
Annotation:
In recent years, learning machines, particularly large language models (LLMs) like
ChatGPT, have revolutionized natural language processing (NLP). This article provides an
overview of how such models function, focusing on the underlying architecture, training
methodology, and inference mechanisms. It also explores their applications, limitations, and
ethical concerns. Drawing on insights from computer science and cognitive modeling, this work
explains how machines "learn" to understand and generate human-like language.
Key words:
transformer-based models, policymakers, computational algorithms, literary
standpoint, pre-existing texts.
INTRODUCTION
Machine learning (ML) and artificial intelligence (AI) have seen transformative
developments over the past decade. One of the most prominent breakthroughs has been the
development of large language models (LLMs) such as OpenAI's ChatGPT, which belong to
the broader family of transformer-based models. These models are capable of performing a
wide range of tasks, including answering questions, writing essays, and translating languages.
Presidential Decree No. PF-60 (2022) of the Republic of Uzbekistan emphasizes the strategic
development of digital and AI technologies, underlining the national importance of such
advancements[1]. Understanding the mechanics of how these models work is essential not only
for developers but also for policymakers and educators.
LITERARY ANALYSIS
The evolution of LLMs is closely tied to the development of the transformer architecture
introduced by Vaswani et al. in 2017[2]. Large language models like ChatGPT, while grounded
in computational algorithms, can be interpreted through the lens of literary theory. These
systems operate as narrative generators, crafting cohesive and context-aware texts by
mimicking the structures of human language. From a literary standpoint, ChatGPT embodies
the tension between authorship and automation, meaning and probability, and presence and
absence of human intention.
1. The Death of the Author and the Rise of the Machine Text
Roland Barthes’ seminal essay “The Death of the Author” proposes that meaning in a text
arises not from the author’s intent but from the reader’s interpretation[3]. ChatGPT takes this
idea further: it creates text without any authorial consciousness. It has no intention, identity, or
emotion—its language is entirely derivative, yet it often passes as human. In this sense,
ChatGPT represents a pure form of intertextuality, drawing on countless pre-existing texts to
construct new linguistic outputs. This phenomenon challenges traditional notions of literary
originality. If authorship is no longer bound to a human subject, the model becomes an echo
chamber of culture, synthesizing fragments of global language data into new compositions. Its
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 2157
writing is collective, probabilistic, and anonymized—yet often strikingly coherent and
compelling.
2. Language Without Understanding
Jacques Derrida’s theory of différance suggests that meaning in language is always deferred,
never fully present[4]. Similarly, ChatGPT operates through probabilistic generation, selecting
the next word not based on understanding but on statistical likelihood. Its “knowledge” is not
semantic but syntactic and structural. It functions as a mirror of linguistic patterns, not a
possessor of thought. This aligns with deconstructive readings of text, where interpretation
reveals instability and multiplicity rather than singular truth. ChatGPT’s outputs are fluid and
open-ended—there is no final meaning, only context-sensitive responses. Each prompt is a re-
entry into the ongoing chain of signification.
3. Hypertextuality and Reader Interaction
ChatGPT transforms users into co-authors. Much like hypertext literature, where the reader
shapes the narrative by choosing different paths, ChatGPT’s outputs are shaped by prompts,
modifications, and follow-up queries. It functions not as a static authorial voice, but as a
collaborative narrative engine, responsive to user intent.
This interactivity echoes concepts in reader-response theory (e.g., Wolfgang Iser, Stanley Fish),
which emphasize the reader’s role in constructing meaning. The “text” of ChatGPT is not
complete until the user interprets it, revises it, or prompts it further. The result is a living text,
constantly rewritten in dialogue.
4. Artificial Intelligence as Literary Trope
In literature, artificial intelligences have often been portrayed as paradoxical beings—intelligent
yet emotionless, powerful yet dependent, human-like yet inhuman. ChatGPT occupies a similar
narrative space. Though it produces emotionally resonant prose, it feels nothing. Its voice may
simulate empathy, wisdom, or creativity, but these are illusions constructed by linguistic
probability.
Such dualities recall classic literary depictions of automata and posthuman figures—
Frankenstein’s creature, the mechanical Turk, HAL 9000. ChatGPT can thus be seen as a
textual character of our time: one that speaks without voice, writes without will, and converses
without consciousness.
RESULT AND DISCUSSION
The interdisciplinary investigation of ChatGPT reveals key insights that merge
computational understanding with literary and cultural analysis[5]. The results show that
ChatGPT functions as both a technological product and a textual construct, raising essential
questions about authorship, language, and artificial intelligence in the digital age.
1. Technical Findings: Efficient Language Generation through Learning
From a technical standpoint, ChatGPT operates on a transformer-based architecture trained
using vast amounts of text data. The pretraining-finetuning model allows it to:
Capture statistical patterns in human language
Generate contextually appropriate and coherent responses
Adapt to various tones, genres, and subject domains
Its proficiency across different tasks demonstrates the generalization power of large
language models. As of GPT-4, improvements in factual consistency and context awareness
have made these systems viable in areas such as education, healthcare, and customer support.
However, the model’s lack of true understanding and context memory limitations reflect
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 2158
fundamental limitations of current AI. It does not possess consciousness or intention; rather, it
simulates these qualities through advanced language modeling.
2. Literary Findings: Reimagining Authorship and Textuality
The literary analysis demonstrated that ChatGPT challenges traditional literary categories:
Authorship becomes distributed, as the model generates original-seeming text without
personal experience or creative intent.
Language becomes a probabilistic system, in line with poststructuralist views by Derrida
and Barthes.
Narrative becomes dialogic and dynamic, echoing Bakhtin’s theory of polyphony.
The model aligns with Hayles' posthuman subject, where pattern replaces presence and
code replaces consciousness.
These findings reveal that ChatGPT is not just an output machine, but a mirror of cultural
language. It recycles and reshapes existing linguistic materials, functioning as a postmodern
textual phenomenon.
3. Ethical and Social Discussion
The convergence of machine learning and language raises ethical concerns:
Bias in Training Data: The model can reproduce harmful stereotypes and
misinformation present in its training corpus.
Illusion of Understanding: Users may attribute more intelligence or authority to the
model than is warranted.
Plagiarism and Creativity: In education and literature, ChatGPT-generated content blurs
the boundary between authentic authorship and automated reproduction.
Furthermore, reliance on AI-generated content could devalue human creativity if not
approached critically. Yet, if used responsibly, these tools can augment human thought and
foster new modes of co-authorship[6].
4. Educational and Cultural Implications
In educational settings, ChatGPT offers both opportunities and challenges:
It can support students with writing, research, and language practice.
However, it can also be misused for academic dishonesty, especially when learners
substitute critical thinking with machine-generated answers.
Culturally, ChatGPT and similar models reflect a paradigm shift in communication. The ability
to generate persuasive, informative, and even poetic language introduces new forms of digital
literacy. Understanding how these models work—technically and textually—must become part
of the 21st-century literacy curriculum.
CONCLUSION
The development and widespread use of large language models like ChatGPT mark a
transformative moment in the relationship between humans and language technologies. These
models, powered by the transformer architecture and trained on vast textual datasets,
demonstrate remarkable abilities in generating coherent, context-aware language. While
technically impressive, their significance extends beyond engineering—into the realms of
literature, culture, and philosophy. Through a literary lens, ChatGPT challenges traditional
notions of authorship, creativity, and meaning, aligning with theories proposed by scholars such
as Barthes, Derrida, and Hayles. It operates not as an author with intention, but as a posthuman
text machine, reshaping and reassembling fragments of collective language. In doing so, it
becomes both a product and producer of discourse, reflecting the ideological and linguistic
patterns embedded in its training data.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 2159
However, alongside its potential, ChatGPT introduces serious ethical, educational, and
social considerations. These include risks of bias, misinformation, plagiarism, and the
devaluation of human judgment and creativity. As such, the integration of AI-generated
language into human communication must be met with critical literacy and informed oversight.
Ultimately, ChatGPT is more than a tool—it is a mirror of contemporary language, a site of
collaboration between algorithmic prediction and human inquiry. Its existence compels us to
reconsider what it means to write, to create, and to understand, urging scholars, educators, and
technologists to engage in an ongoing dialogue about the future of language in the age of
intelligent machines.
REFERENCES:
1. Presidential Decree No. PF-60. (2022). On the Development of the Education System for
2022–2026. Republic of Uzbekistan.
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... &
Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing
Systems, 30, 5998–6008.
3. Barthes, R. (1967). The death of the author. Aspen, (5–6).
4. Derrida, J. (1976). Of grammatology (G. C. Spivak, Trans.). Johns Hopkins University
Press.
5. Bakhtin, M. M. (1984). Problems of Dostoevsky’s poetics (C. Emerson, Trans.). University
of Minnesota Press.
6. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D.
(2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. pp. 610–
623).
