Artificial intelligence
LLM (Large Language Model) definition
Publiée le September 24, 2025
Publiée le September 24, 2025
An LLM (Large Language Model ) is a type ofartificial intelligence based on very large neural networks trained on huge volumes of text. These models are capable of understanding, generating and manipulating natural language (text or sometimes computer code) in a fluid and coherent way.
In concrete terms, an LLM can write an article, summarize a document, translate a text, answer a question or generate code. Well-known examples include GPT (OpenAI), Claude (Anthropic), LLaMA (Meta), or Gemini (Google DeepMind).
Transformer architecture
LLMs use an architecture called Transformer (introduced in 2017 by Google). This approach enables language to be processed in parallel and contextual relationships between words to be modeled using a mechanism called attention.
Training on large corpora
An LLM is trained on massive data: books, articles, websites, technical documentation, source code.
The aim is to predict the next word in a sentence.
Repeated on a very large scale, this mechanism enables the model to learn the structure and subtleties of the language.
Very large-scale parameters
Recent models include billions, even trillions of parameters. These parameters represent the “weights” learned during training, and make it possible to capture very fine nuances of language.
LLMs can read, analyze and interpret complex texts in natural language. Thanks to their billions of parameters and Transformer architecture, they can :
Extract the overall meaning of a document (semantic analysis).
Summarize a long text in a few clear, coherent sentences.
Classify documents according to categories (legal, marketing, technical).
Identify key entities (names, dates, places, amounts).
💡 A concrete example: in the banking and insurance sectors, an LLM can analyze a contract of several dozen pages and highlight the important clauses (exclusions, guarantees, interest rates). This saves an advisor or customer precious time in reading and comparing documents.
LLMs excel at producing texts that are coherent, fluid and contextually appropriate. They can :
Write SEO-optimized blog posts.
Generate large-scale personalized emails for customer relations.
Propose social network posts adapted to a brand’s tone.
Create summary reports from raw data.
💡 Case in point: an insurer can use LLM to automatically generate customer reminder letters tailored to each profile, with a personalized tone, while respecting legal compliance.
A multilingual LLM is able to translate fluently and accurately, taking into account cultural context and style. Unlike conventional word-for-word translation tools, it understands the overall meaning and chooses the most natural formulations.
💡 A concrete example: an international bank can instantly translate its product brochures or general insurance conditions into several languages, without losing nuance or clarity.
LLMs enable the creation of chatbots and virtual assistants capable of natural dialogue.
They understand the intentions behind the questions.
They can manage multi-turn conversations (following a context over several exchanges).
They adopt an appropriate tone (formal, friendly, professional).
💡 Case in point: a customer asks his insurer a question via a chatbot (“Am I covered if I break my phone?”). The AI understands the question, fetches the contract clause (via RAG), and provides a clear and precise answer.
More and more LLMs are trained not only on natural text, but also on source code. They are able to :
Generate code in several languages (Python, JavaScript, SQL, etc.).
Help debugging by identifying errors in a program.
Automatically write unit tests or technical documentation.
💡 Real-life example: a bank can use LLM to quickly generate automation scripts (e.g. accounting data extraction) and assist its developers in modernizing its systems.
LLMs can follow simple logical steps:
Solve basic equations.
Structure a plan or list of actions.
Answer questions requiring elementary reasoning (comparison, sorting, simple calculation).
⚠️ However, their reasoning remains limited: they don’t manipulate complex abstract concepts as a human would, and may give incorrect answers if the problem requires genuine mathematical or scientific logic.
💡 Concrete example: an augmented advisor might ask the LLM: “List me the 3 best investment options for a 45-year-old client, cautious profile, with €50,000 available”. The model will be able to rank the solutions according to general criteria, but the advisor will have to validate the actual suitability according to current regulations and taxation.
—————————————————————————————————————————————————————————-
Hallucinations refer to cases where an LLM generates false but plausible information.
This is because an LLM doesn’t “reason”, but statistically predicts the most likely sequence of a sentence.
He can invent a non-existent legal reference, an erroneous date or a false figure.
💡 Case in point: an LLM used by an insurer could “hallucinate” a contract clause not legally provided for, or offer an insurance formula that doesn’t exist.
👉 Impact: legal risk, loss of customer confidence, even regulatory non-compliance.
👉 Solutions:
integrate a RAG (Retrieval-Augmented Generation) mechanism to limit responses to internally validated data only.
add human-in-the-loop monitoring of critical cases.
LLMs are trained on large masses of public data (websites, forums, articles, etc.), which inevitably contain social, cultural or historical biases.
This can lead to implicit discrimination (gender, age, origin).
Results may reflect stereotypes or favour certain languages/cultures.
💡 Concrete example: in a bank, a biased LLM could favor a socioeconomic profile in a credit simulation, or minimize certain risks related to specific geographic areas.
👉 Impact: indirect discrimination, regulatory sanctions (AI Act, RGPD).
👉 Solutions:
control and filter training datasets.
apply answer debiasing and auditing techniques.
use regular tests to verify the fairness of recommendations.
An LLM is frozen at the date of its last training session.
He is unaware of recent events (e.g. changes in interest rates, new legislation, market fluctuations).
Without connection to an external database or the Internet, its answers can quickly become obsolete.
💡 Case in point: an augmented advisor relying on an unconnected LLM might be unaware of the latest reform on retirement savings, or updated credit rates.
👉 Impact: erroneous advice, loss of credibility with the customer.
👉 Solutions:
coupling LLM with real-time data sources (financial APIs, regulatory databases).
set up a continuous fine-tuning system or plug-ins connected to business data.
LLMs are often regarded as black boxes.
It’s difficult to explain precisely why a model has produced a particular response.
Traceability of reasoning is not always available.
💡 Concrete example: an insurer asks the AI why it recommends one pension product over another. The model gives the final answer, but without making explicit the logical rules behind it.
👉 Impact: major challenge for regulatory compliance, as financial institutions must justify their decisions (principle ofexplicability imposed by the European AI Act).
👉 Solutions:
integrateXAI (Explainable AI) mechanisms that provide legible justifications.
keep an audit trail (history of sources and calculations used by the model).
combine LLM + explicit business rules for greater transparency.
Automated contract analysis with OCR and LLM.
Augmented agents for bank advisors (real-time recommendations).
Intelligent chatbots capable of responding to customers 24/7.
Internal documentation generation.
Intelligent search in knowledge bases (via RAG – Retrieval-Augmented Generation).
Legal assistance or regulatory compliance.
Conversational assistants like ChatGPT.
Automated authoring tools.
Translation and language learning.
An LLM (Large Language Model) is a major advance in modern AI, capable of transforming the way we produce, consume and analyze information. In 2025, its role will extend far beyond that of a simple chatbot: it will become a strategic technological foundation for businesses and the general public alike.
However, its use needs to be supervised: explicability, data governance, respect for bias and security are all necessary conditions for responsible adoption.