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Limitations of Language Models in Deep Understanding of Human Language

May 6, 2025

محدودیت‌های مدل‌های زبانی در درک عمیق زبان انسانی

Introduction

Large Language Models (LLMs) such as GPT, BERT, and PaLM have made significant advances in natural language processing in recent years. These models are now capable of producing text that closely resembles human writing in structure and meaning. But the fundamental question remains: do these models genuinely understand human language, or are they merely imitating statistical and linguistic patterns? This article explores the limitations of language models in truly comprehending human language and explains why, despite their apparent capabilities, these technologies remain distant from real understanding.

1. The Difference Between Statistical Processing and Conceptual Understanding

Language models are built on statistical learning. By observing a massive volume of text, they learn the probabilistic distribution of words. This means that if you ask them to write a sentence, they generate the continuation based on the common patterns in the data.
But genuine understanding of language is not just statistical simulation. Humans, when they comprehend a sentence, relate it to prior knowledge, logic, lived experience, and mental context. In contrast, language models truly lack context in the real sense.

2. Inability to Perform High-Level Inference

One dimension of deep understanding is the ability to make logical and contextual inferences. Consider the following example:
“Ali came home from work. The lights were off. He sat in the dark.”
A human easily infers that “probably no one is home” or “Ali might be upset.” But most language models cannot correctly derive these kinds of interpretations because they lack human background knowledge and intuition.

3. Lack of Intention and Purpose

Language models lack awareness and intention. They do not know why they are saying a sentence or what their goal is in saying it. For this reason, they sometimes give responses that are semantically correct but contextually irrelevant, especially in sensitive conversations such as psychology or medicine, which can be dangerous.

4. Superficial Understanding of Metaphor and Humor

Human language is full of metaphor, ambiguity, irony, humor, and wordplay. Language models can mimic some of these aspects, but they are often unable to grasp the hidden meaning or situational humor.
For example, the sentence:
“He’s so smart that when the power goes out, he can find his way with the light of his intellect.”
A model like GPT might recognize this as an exaggerated compliment but often may give vague or even incorrect responses because “understanding” in these models is purely statistical reproduction.

5. Lack of Real-World Knowledge

Even models trained on massive datasets do not have a true grasp of the world. They do not “know” that water is wet or that the sun rises; they only know that the word “water” is frequently associated with “wet” in text.
This distinction between statistical knowledge and experiential understanding makes their output seem artificial or superficial in certain contexts.

6. Difficulty Maintaining Long-Term Coherence

Language models struggle with logical coherence in long texts. For instance, they might introduce a character as a doctor at the beginning and refer to them as a student later, or present contradictory positions within the same article. This shows they lack an understanding of overall structure and operate only at the sentence or paragraph level.

7. Absence of Persistent, Continuous Memory

By default, models like GPT have no persistent memory. If you teach them something in one conversation, they forget it in the next. Although some versions (e.g., GPT with active memory) have tried to mitigate this limitation, they are still far from human memory capabilities.

8. Challenges in Understanding Cultural and Social Context

Human language is intertwined with cultural, historical, and social contexts. To understand sentences like:
“He fought the enemy like Rostam did.”
a model needs familiarity with the Shahnameh and Iranian mythology, not just the words. Most models cannot properly grasp cultural context or respond appropriately.

9. Limitations in Learning Abstract Concepts

Abstract concepts like justice, freedom, love, ethics require understanding beyond text. Humans learn these through experience, reflection, upbringing, and observation. Language models can only analyze them based on frequency and co-occurrence in text.
Therefore, when you ask a model about “the meaning of justice,” it may provide eloquent answers, but these are not genuine understanding or a thoughtful stance, but rather reflections of common patterns in the training data.

10. Inability to Empathize or Feel Real Emotions

Language models can write empathetic sentences, for example:
“I’m sorry that you’re upset; this is a difficult time.”
But this empathy is not real. The model has no feelings. These sentences are merely a statistical response to emotional input, whereas human empathy arises from lived experience and genuine emotion.

11. High-Confidence Incorrect Responses (Hallucinations)

One of the main issues with language models is their generation of false information with high confidence. They may misstate dates, statistics, or people’s names and present them as fact with complete certainty. These errors stem from their lack of deep understanding and reliance on textual patterns.

12. Dependence on Training Data and Hidden Biases

Language models learn only from their training data. If that data contains bias, stereotypes, or misinformation, the model will reproduce them. This leads to biased responses on sensitive topics such as race, gender, or politics.

Summary

Language models excel at generating human-like text, but despite their apparent intelligence, they lack deep understanding of human language. They do not truly understand, feel, intend, or experience; they are merely mirrors reflecting the linguistic data they have seen. While this technology continues to advance, overcoming these fundamental limitations is essential to achieve genuine human-like comprehension.

Conclusion

Despite the capabilities and appeal of models like GPT and Gemini, they cannot replace human understanding. Intelligent use of these models requires awareness of their limitations and reliance on humans in domains demanding genuine comprehension, emotion, intention, and reasoning. The future may bring models closer to awareness, but for now, human language remains more than mere statistics and algorithms.