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Limitations of Language Models in Deep Understanding of Human Language

محدودیت‌های مدل‌های زبانی در درک عمیق زبان انسانی

Introduction

Large language models such as GPT, Claude, Gemini, and PaLM have made remarkable progress in natural language processing in recent years. These models can generate texts that are structurally and semantically similar to human writing, answer questions, write code, and even participate in complex conversations. But one fundamental question remains unanswered: Do these models truly understand human language, or are they merely mimicking statistical patterns?
This article provides an in-depth examination of the fundamental limitations of language models in truly understanding human language and demonstrates why these technologies, despite their impressive apparent capabilities, are still far from genuine understanding.

1. The Gap Between Statistical Processing and Conceptual Understanding

How Do Language Models Work?

Language models are built on statistical learning and complex neural network architectures. These systems learn the probabilistic distribution of words by observing billions of words from internet texts, books, articles, and conversations. Simply put, they learn what word is most likely to come after a sequence of words.
Modern architectures like Transformers and attention mechanisms allow these models to identify complex relationships between words in long texts. However, this process is fundamentally statistical and based on pattern recognition, not genuine semantic understanding.

The Difference in Human Understanding

When humans understand a sentence, they connect it to prior knowledge, logic, lived experience, emotions, and mental context. For example, when you read:
"Sarah picked up an umbrella and left the house."
You immediately infer that it's probably raining or the sky is cloudy. This inference comes not from the sentence itself, but from your real-world knowledge about umbrella usage. Language models may also make this inference, but not due to actual understanding—rather because they've seen this pattern in training data.
Key difference: A human "understands" why Sarah picked up the umbrella, while a language model "knows" that these words typically appear together.

2. Inability to Perform High-Level Inference and Reasoning

The Challenge of Contextual Inference

One fundamental aspect of deep language understanding is the ability to make logical inferences and reason based on context. While new reasoning models like o1 and o3-mini have made significant progress in this area, fundamental limitations still exist.
Consider this example:
"Ali came home from work. The lights were off. He sat in the darkness and stared at the wall."
A human reading these sentences makes multiple inferences:
  • Probably no one is home
  • Ali might be sad, tired, or depressed
  • Perhaps something unpleasant has happened
  • The power might be out
These inferences require understanding causal relationships, emotional states, and social context. Language models can simulate some of these based on patterns they've seen, but they lack genuine human intuition and understanding.

Limitations in Causal Reasoning

Language models often struggle with understanding causal relationships. They might know "rain causes the ground to get wet" because they've seen this phrase many times, but they cannot independently infer that "if the ground is dry, it probably hasn't rained"—unless this too exists in their training data.

3. Lack of Intent, Purpose, and Awareness

The Problem of Artificial Consciousness

One of the most fundamental differences between humans and language models is the issue of consciousness and intent. Humans speak with purpose and goals. We know why we say something, what effect we want to create, and what message we want to convey.
Language models lack consciousness. They don't know why they're saying something or what their purpose is in saying it. When ChatGPT or Claude responds to you, that response is the result of statistical calculations, not conscious decision-making.

Dangers in Sensitive Applications

This limitation can be dangerous in sensitive areas like psychological counseling, medical decision-making, or legal advice. Patients or clients expect the other party to truly understand and respond with awareness and intent, not merely act based on statistical patterns.
For example, when someone tells a language model "I feel worthless," the model might give an empathetic response, but that response comes from probabilistic calculation, not genuine understanding of human pain.

4. Challenges in Understanding Metaphor, Humor, and Ambiguity

The Complexity of Human Language

Human language goes beyond the literal meaning of words. We use metaphors, double meanings, irony, humor, and wordplay, all of which require deep understanding of cultural, social, and situational context.
For instance, the sentence:
"He's so smart that when the power goes out, he can find his way with the light of his intellect."
This is a humorous exaggeration that refers hyperbolically to someone's intelligence, or might even be subtle sarcasm. Language models may recognize this as praise or humor, but they often fail to understand the layers of meaning and speaker's intent.

Situational Humor

Situational humor that depends on specific conversation context or social situations is extremely difficult for language models. For example:
Person A: "I'm going to the movies tonight." Person B: "Yeah, sure!"
Tone and context determine whether Person B is agreeing or sarcastically doubting. Without access to vocal tone, body language, or relationship history, language models cannot accurately make this distinction.

5. The Real-World Knowledge Gap

The Difference Between Knowing and Experiencing

Language models, even those trained on trillions of words, have no sensory experience of the real world. They "know" that water is wet, but have never touched water. They know the sun rises, but have never felt morning light.
This difference between statistical knowing and experiential understanding makes their output seem artificial or superficial in some areas. For example:
"Describe the difference between hot tea and cold tea."
The model can provide precise explanations based on temperature, taste, and physical properties, but has never tasted it. Human understanding of this difference is tied to sensory memory and lived experience.

Limitations in Experience-Based Knowledge

Many human concepts can only be understood through experience:
  • Physical pain
  • The feeling of fear in dangerous situations
  • The pleasure of delicious food
  • Fatigue after a long workday
Language models can describe these things but have never experienced them. This limitation affects deep understanding of many aspects of human language.

6. The Problem of Maintaining Coherence in Long Texts

Memory and Continuity Challenges

One practical problem with language models is their inability to maintain logical coherence in long texts. Although new architectures like Transformers and optimization techniques like Flash Attention have reduced this problem, fundamental limitations remain.
For example, in a long story or article:
  • Character names might change
  • Previous details may be forgotten
  • Contradictory positions may be stated
  • Event timing may become confused

Practical Example

Paragraph 1: "Dr. Ahmadi is a professor at Tehran University." Paragraph 15: "Ahmadi, who has just started his doctoral degree..."
This contradiction shows that throughout the text, the model loses overall structure and logical coherence. Humans, having a mental model of the story or topic, easily detect these contradictions.

7. Memory Limitations and Continuous Learning

Lack of True Persistent Memory

Language models in their default state have no persistent memory. Each conversation is independent of previous ones. Although some systems like ChatGPT with Memory feature or RAG-based systems have tried to reduce this limitation, they're still very far from human memory.

Difference from Human Learning

Humans learn continuously and store their experiences in long-term memory. We can:
  • Learn from past mistakes
  • Integrate new knowledge with prior knowledge
  • Recall our memories with context and emotion
Language models have no continuous learning. Their weights are set during training and remain fixed afterward. Research in Continual Learning and federated learning is attempting to bridge this gap.

8. The Challenge of Understanding Cultural and Social Context

Cultural Context Complexity

Human language is deeply intertwined with cultural, historical, and social context. To understand sentences like:
"He fought the enemy like Rostam."
The model needs deep knowledge of Shahnameh, Iranian mythology, and cultural symbols. While most models can recognize these references, they don't truly understand the semantic depth and emotional weight.

Cultural Differences

The same sentence can have different meanings in different cultures:
  • In Iranian culture, "fighting like Rostam" symbolizes bravery and heroism
  • In other cultures unfamiliar with Shahnameh, this reference has no meaning
Language models often fail at cultural adaptation and understanding relative meanings. They might provide factual information about Rostam, but don't understand the feeling and symbolic meaning.

9. Inability to Understand Deep Abstract Concepts

The Nature of Abstract Concepts

Abstract concepts like justice, freedom, love, ethics, and beauty require understanding beyond text. These concepts are shaped through lived experience, philosophical thinking, social upbringing, and human observation.
Language models can analyze these concepts based on:
  • Frequency of repetition in text
  • Co-occurrence with other words
  • Dictionary definitions
  • Common usage
But they have never felt them.

Example: The Meaning of Justice

When you ask a language model "What is justice?", it might give beautiful philosophical answers:
"Justice means equality in rights, fairness in resource distribution, and respect for human dignity."
But this answer is a reflection of common patterns in philosophical and legal data, not an intellectual position or personal understanding. The model cannot say "I believe" because it has no beliefs.

10. Absence of Empathy and Real Emotions

Simulating Empathy

Language models can write empathetic sentences:
"I'm sorry you're feeling down these days. I know getting through this period is difficult."
But this is not real empathy. These sentences are the result of probabilistic calculations that determine what kind of sentences are appropriate in response to such inputs.

Difference from Human Empathy

Human empathy comes from shared experience, internal feeling, and the ability to put oneself in another's place. When a friend empathizes with you:
  • They genuinely feel
  • They use their own similar experiences
  • Their goal is to help you, not just generate a response
Language models have no feelings. They neither know what sadness is nor can they truly understand what troubles you.

11. The Problem of Hallucination and Misinformation

The Hallucination Phenomenon

One serious problem with language models is generating incorrect information with high confidence, called AI Hallucination. Models might:
  • Provide wrong dates
  • Generate fake statistics
  • Misstate names of people or places
  • Cite non-existent sources
And state all of this with a completely confident tone.

Why Does This Happen?

These errors result from lack of deep understanding and complete reliance on statistical patterns. The model doesn't "know" what's real; it only calculates what seems likely.
For example, if the model sees a sentence like "University X was founded in year...", based on similar patterns, it generates a likely date, even if that date is wrong.

Existing Solutions

To reduce this problem, approaches like:
Have been developed, but the fundamental problem remains.

12. Hidden Biases and Dependence on Training Data

Source of Biases

Language models learn what they know from their training data. If this data contains:
  • Gender or racial biases
  • Cultural stereotypes
  • Incorrect or biased information
  • One-sided perspectives
The model will reproduce these as well.

Practical Examples

Research has shown that language models might:
  • Associate certain professions more with one gender (e.g., nurse=female, engineer=male)
  • Use stereotypes in describing ethnic groups
  • Show bias in political topics
These problems occur even when there's no intent to create bias, because the statistical structure of the model simply reproduces patterns present in the data.

The Challenge of Removing Bias

Complete bias removal is difficult because:
  • Real-world data itself contains biases
  • The definition of "neutrality" is subjective and cultural
  • Removing one type of bias might create another

13. Limitations in Understanding Causality and Conditional Reasoning

The Challenge of Causal Relationships

Language models struggle with understanding complex causal relationships. They can detect correlations, but understanding true causality is different.
For example:
  • "Ice cream sales and drownings both increase in summer" → correlation
  • But ice cream is not the cause of drowning
Humans know a third variable (hot weather) causes both phenomena. Language models may have learned this information from data, but they lack independent causal reasoning ability.

Conditional Reasoning and Hypothetical Scenarios

"If...then" scenarios involving long reasoning chains are challenging for models:
"If it rains, the ground gets wet. If the ground is wet, people carry umbrellas. If people carry umbrellas, umbrella sales decrease. So if it rains, umbrella sales..."
A human understands that umbrella sales actually increase (because people buy more umbrellas), but the model might make a mistake by following the pattern "people carry umbrellas → sales decrease."

14. Lack of Understanding Time and Temporal Experience

Time as Logical Structure

Language models can follow temporal sequences in text, but don't understand the experience of time. They don't know:
  • How long a minute feels while waiting
  • How past memories connect with emotions
  • Why "tomorrow" seems far away to a child

Practical Example

The sentence: "Waiting for medical test results was the hardest hours of my life."
A human understands the feeling of anxiety, the stretching of time, and the emotional pressure of waiting. The model only knows this sentence expresses difficulty, but doesn't understand the quality of experience.

15. Limitations in Learning from Interaction

Difference Between Human and Machine Learning

Humans learn from direct interaction with the world. A child playing with a ball develops a physical understanding of mass, speed, and gravity. This learning is experiential and multisensory.
Language models only learn from text. They have never:
  • Touched anything
  • Seen anything (in the classic sense)
  • Heard any sound
  • Performed any movement
Although multimodal models and vision models are bridging this gap, they're still far from human experience.

16. The Challenge of Understanding Text at Deep Layers

Different Levels of Meaning

Human language has multiple semantic layers:
  1. Literal level: Direct meaning of words
  2. Inferential level: What's inferred from text
  3. Emotional level: Hidden feelings in text
  4. Philosophical level: Deeper concepts and hidden messages
Example: "Open the window."
  • Literal level: Request to open the window
  • Inferential level: The air might be hot or stuffy
  • Emotional level: The speaker might be irritated or frustrated
  • Symbolic level: Might be a metaphor for freedom
Language models perform relatively well at first and second levels but are weak at deeper levels.

17. Inability for Self-Awareness and Reflection

Lack of Self-Awareness

Humans can think about themselves, learn from their mistakes, and change their beliefs. This self-awareness and reflection is an essential part of human intelligence.
Language models lack this ability. They cannot:
  • Say "I was wrong in my previous answer and now have a better understanding"
  • Truly learn from past experiences
  • Change their position based on new reasons
Although models can simulate reconsidering, this is a linguistic pattern, not an actual cognitive process.

18. Limitations in Understanding Values and Ethics

Complexity of Moral Judgments

Ethical decision-making requires:
  • Understanding human values
  • Balancing different interests
  • Considering long-term consequences
  • Feeling responsibility and conscience
Language models can describe ethical principles but have no real moral judgment. They're merely reflections of ethical consensuses present in training data.

Ethical Dilemmas

When facing questions like:
"Is it right to lie to save someone's life?"
The model can present different viewpoints but cannot take a real ethical position. This limitation is very important in AI applications in law and AI ethics.

19. Challenges of Specialized Languages and Specific Domains

Depth of Specialized Knowledge

Language models have broad knowledge across topics, but their depth of understanding is limited. In specialized fields like:
A human expert not only knows information but also:
  • Can perform deep reasoning
  • Has practical experience
  • Has contextual and situational understanding
  • Knows when rules have exceptions

The Danger of Overreliance

Using language models in sensitive areas without human expert supervision can be dangerous. The model might:
  • Give incorrect medical diagnoses
  • Provide misleading legal advice
  • Make mistakes in financial decisions

20. The Future: Can Models Achieve True Understanding?

Possible Paths

Current research is progressing in several directions:
  1. Advanced multimodal models: Combining text, image, audio, and video for richer understanding
  2. Reinforcement learning from human feedback: Improving responses based on human preferences
  3. Integration with structured knowledge: Using knowledge bases and knowledge graphs
  4. Agent-based models: Multi-agent systems that can interact with environments

Fundamental Challenges

However, some limitations are fundamental:
  • Lack of consciousness: Models don't feel and have no awareness
  • Absence of sensory experience: They don't experience the world
  • No intent: They have no goals or purposes
The main question is: Is machine consciousness possible? And is consciousness necessary for true language understanding?

Artificial General Intelligence (AGI)

Some believe achieving true understanding requires developing AGI—a system that can perform at human level across all domains. But this is still a distant goal and may never be realized.

Conclusion: The Value of Understanding Limitations

Language models are powerful tools useful in many applications:
But understanding their limitations is essential:

For Users:

  • Use models in appropriate domains
  • Always verify important information
  • Rely on human judgment for sensitive decisions
  • Benefit from combining human and artificial intelligence

For Developers:

  • Design systems that acknowledge limitations
  • Use optimization techniques to improve performance
  • Be transparent about capabilities and limitations
  • Keep humans in the decision-making loop

For Society:

Conclusion

Language models are exceptional simulators in text generation, but despite their intelligent appearance, they lack deep understanding of human language. They don't understand, have no feelings, no intent, don't experience, and are merely mirrors of the linguistic data they've seen.
Although technology is rapidly advancing and the future of AI is full of possibilities, the path to achieving true human understanding requires overcoming the fundamental limitations of these models. Until then, human language remains something beyond mere statistics and algorithms—a complex phenomenon arising from consciousness, experience, emotion, and culture.
Intelligent use of this technology requires awareness of limitations and optimal combination of machine computational power with deep human understanding. It is in this combination that we can derive the most benefit from artificial intelligence without ignoring the irreplaceable role of human intelligence.