Debunking LLM Intelligence: What's Really Happening Under the Hood?

Large language models (LLMs) possess an impressive ability to generate text, poetry, code, and even hold complex conversations. Yet, a fundamental question arises: do these systems truly understand what they are saying, or do they merely imitate a form of thought? Is it a simple illusion, an elaborate statistical performance, or are LLMs developing a form of understanding, or even reasoning?

This question is at the heart of current debates on artificial intelligence. On one hand, the achievements of LLMs are undeniable: they can translate languages, summarize articles, draft emails, and even answer complex questions with surprising accuracy. This ability to manipulate language with such ease could suggest genuine understanding. 

On the other hand, analysts emphasize that LLMs are first and foremost statistical machines, trained on enormous quantities of textual data. They learn to identify patterns and associations between words, but this does not necessarily mean they understand the deep meaning of what they produce. Don’t they simply reproduce patterns and structures they have already encountered, without true awareness of what they are saying? 

The question remains open and divides researchers. Some believe that LLMs are on the path to genuine understanding, while others think they will always remain sophisticated simulators, incapable of true thought. Regardless, the question of LLM comprehension raises philosophical, ethical, and practical issues that translate into how we can use them. 

Also, it appears more useful than ever today to demystify the human "thinking" capabilities sometimes wrongly attributed to them, due to excessive enthusiasm or simply a lack of knowledge about the underlying technology. This is the very point a team of researchers at Apple recently demonstrated in their study "The Illusion of Thinking."

They observed that despite LLMs' undeniable progress in performance, their fundamental limitations remained poorly understood. Critical questions persisted, particularly regarding their ability to generalize reasoning or handle increasingly complex problems. 

"This finding strengthens evidence that the limitation is not just in problem-solving and solution strategy discovery but also in consistent logical verification and step execution limitation throughout the generated reasoning chains" - Example of Prescribed Algorithm for Tower of Hanoi - “The Illusion of Thinking” - Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, Mehrdad Farajtabar - APPLE

To better get the essence of LLMs, let’s explore their internal workings and establish fundamental distinctions with human thought. To do this, let’s use the concrete example of this meme ("WHAT HAPPENED TO HIM? - P > 0.05") to illustrate both the technological prowess of LLMs and the fundamentally computational nature of their operation, which is essentially distinct from human consciousness.

The 'P > 0.05' Meme Explained Simply by an LLM

I asked an LLM to explain this meme to me simply, and here is its response:

The LLM Facing the Meme: A Demonstration of Power

If we look closely, for a human, understanding the humor of this meme requires knowledge of the Harry Potter saga, basic statistics, and the ability to get the irony of the funny juxtaposition. 

Now, when the LLM was confronted with this meme, it demonstrated an impressive ability to decipher it. It managed to identify the visual and textual elements, recognize the cultural context (the Harry Potter scene and the characters), understand an abstract scientific concept (the p-value in statistics and its meaning), and synthesize all this information to explain the meme's humor. 

Let's agree that the LLM's performance in doing the job was quite remarkable. It could, at first glance, suggest a deep "understanding," or even a form of intelligence similar to ours, capable of reasoning and interpreting the world.

The Mechanisms of 'Reasoning': A Computational Process

However, this performance does not result from 'reflection' in the human sense. The LLM does not 'think,' has no consciousness, no introspection, and even less subjective experience. What we perceive as reasoning is, in reality, a sophisticated analysis process, based on algorithms and a colossal amount of data.

The Scale of Training Data

An LLM like Gemini or ChatGPT is trained on massive volumes of data, reaching hundreds of terabytes, including billions of text documents (books, articles, web pages) and billions of multimodal elements (captioned images, videos, audio), containing billions of parameters. 

This knowledge base is comparable to a gigantic, digitized, and indexed library. It includes an encyclopedic knowledge of the world, entire segments of popular culture (like the Harry Potter saga), scientific articles, movie scripts, online discussions, and much more. It’s this massive and diverse exposure to information that allows it to recognize patterns, correlations, and contexts.

The Algorithms at Work

To analyze the meme, several types of algorithms come into play:

• Natural language processing (NLP): It’s the core of interaction with text. NLP allows the model to understand the semantics of phrases ('WHAT HAPPENED TO HIM?') and to process textual information.
• Visual recognition / OCR (Optical Character Recognition): For image-based memes, the system uses OCR algorithms to extract and 'read' the text present in the image ('P > 0.05'). Concurrently, visual recognition allows for the identification of graphic elements: the characters' faces, the specific scene from the movie, and even the creature's frail nature.
• Transformer neural networks: These are the main architectures of LLMs. They are particularly effective at identifying complex patterns and long-term relationships in data. They allow the model to link 'Harry Potter' to specific scenes and to understand that 'P > 0.05' is a statistical concept.

The Meme Analysis Process, Step-by-Step:

When faced with the meme, the LLM carries out a precise computational process:

1. Extraction and recognition: The system identifies keywords, faces, the scene, and technical text.
2. Activation of relevant knowledge: Based on these extracted elements, the model 'activates' and weighs the most relevant segments of its knowledge. It establishes connections with its data on Harry Potter (the 'limbo,' Voldemort's soul fragment), statistics (the definition of the p-value and the 0.05 threshold), and humor patterns related to juxtaposition.
3. Response synthesis: The model then generates a text that articulates the humorous contrast. It explains that the joke comes from Dumbledore's cold and statistical response to a very emotional and existential question. This highlights the absence of 'statistical significance' of the creature's state. This explanation is constructed by identifying the most probable and relevant semantic associations, learned during its training.

The Fundamental Difference: Statistics, Data, and Absence of Consciousness

This LLM's 'reasoning,' or rather, its mode of operation, therefore results from a series of complex statistical inferences based on correlations observed in massive quantities of data.

The model does not 'understand' the abstract meaning, emotional implications, or moral nuances of the Harry Potter scene. It just predicts the most probable sequence, the most relevant associations, based on the billions of parameters it has processed.

This fundamentally contrasts with human thought. Indeed, humans possess consciousness, lived experience, and emotions. It’s with these that we create new meaning rather than simply recombining existing knowledge. We apprehend causes and effects beyond simple statistical correlations. It’s this that allows us to understand Voldemort's state, the profound implications of the scene, and the symbolic meaning of the meme.

And above all, unlike LLMs, humans act with intentions, desires, and beliefs. LLMs merely execute a task based on a set of rules and probabilities.

While LLMs are very good at manipulating very large volumes of symbols and representations, they lack the understanding of the real world, common sense, and consciousness inherent in human intelligence, not to mention the biases, unexpected behaviors, or 'hallucinations' they can generate.

Conclusion

Language models are tools that possess huge computational power, capable of performing tasks that mimic human understanding in an impressive way. However, their operation relies on statistical analysis and pattern recognition within vast datasets, and not on consciousness, reflection, or an inherently human understanding of the world.

Understanding this distinction is important when the technological ecosystem exaggerates supposed reasoning capabilities. In this context, adopting a realistic view allows us to fully leverage the capabilities of these systems without attributing qualities to them that they don't possess.

Personally, I’m convinced that the future of AI lies in intelligent collaboration between humans and machines, where each brings its unique strengths: consciousness, creativity, and critical thinking on one side; computational power, speed of analysis, and access to information on the other.