Do Large Language Models "Cut Corners" Like Humans? A Deep Study of the Dependency Length Minimization Phenomenon

A study explores whether large language models (LLMs) follow the principle of dependency length minimization (DLM) in human language. The core question is whether LLM-generated language optimizes syntax to reduce cognitive load like humans do. By comparing human corpora, LLM-generated texts, and random baselines, the study finds that LLMs do exhibit DLM, but the degree of optimization differs from that of humans. This research bridges computational linguistics and psycholinguistics, providing a new perspective for evaluating LLM language capabilities.

Basics of Dependency Grammar

Dependency grammar focuses on the dependency relationships between words, unlike constituency grammar. In a dependency tree, each word is a node, and dependency relationships are edges (e.g., "sleeps" is the head word in "The cat sleeps").

Definition of Dependency Length

Refers to the linear distance between words in a dependency relationship (e.g., the distance between "saw" and "cat" in "The cat that I saw sleeps" is 3).

DLM in Human Language

Cross-linguistic studies show that human languages generally tend toward DLM to reduce working memory burden, but this needs to balance constraints such as word order stability—it is a compromise result under multiple constraints.

Core Question

Do LLM-generated texts exhibit DLM patterns similar to humans? This relates to whether LLMs capture the deep cognitive constraints of human language.

Experimental Design

1. Human corpora: as the natural language baseline;
2. LLM-generated texts: control prompts to ensure comparability;
3. Random baselines: shuffle word order or dependency trees to provide a non-optimized benchmark.

Analytical Methods

• Dependency syntax parsing: Use tools like spaCy to extract dependency relationships and calculate distances;
• Statistical comparison: Analyze differences in dependency length distribution among the three groups of data (e.g., average distance, sentence length effect).

Key Findings

1. LLMs do exhibit DLM, with dependency lengths significantly lower than random baselines;
2. The degree of optimization differs from humans (longer/shorter distances under some conditions);
3. Differences in sentence length effect: Human optimization remains stable for long sentences, while LLM efficiency declines more noticeably.

In-depth Interpretation

• LLMs have learned the deep statistical laws of human language (not explicitly encoded, emerging from data);
• Reasons for differences: Lack of cognitive constraints (LLMs have no working memory bottlenecks), training data biases, and influence of generation strategies;
• Implications: Traditional evaluation metrics (e.g., perplexity) do not reflect deep syntactic differences.

Theoretical Significance

Bridges computational and psycholinguistics, provides a quantitative framework for comparing human and machine language, challenges the definition of "language understanding", and offers a new perspective for language evolution research.

Practical Value

• Model evaluation: Dependency length can serve as a supplementary indicator;
• Prompt engineering: Understanding syntactic preferences helps design better prompts;
• Post-processing optimization: Targeted improvement of the verbosity issue in LLM outputs.

Methodological Contribution

Applies traditional linguistic analysis to AI texts; the interdisciplinary approach can be extended to other studies (e.g., model comparison, training tracking).

Current Limitations

• Limited corpus size (course project);
• Narrow model scope (only a few LLMs);
• Language limitations (mainly focusing on English).

Future Directions

• Cross-linguistic research;
• Model size effect (relationship between size and syntactic optimization ability);
• Training process tracking (when DLM is learned);
• Cognitive modeling (comparing human and machine understanding processes).

This study raises a profound question: What is the essence of LLM "understanding" of language? The results show that LLMs have learned the deep laws of human language, but their optimization differs from that under human cognitive constraints. AI and human language abilities each have their strengths and weaknesses; understanding these differences helps better utilize AI tools. As LLMs become more popular, such basic research becomes increasingly important—only by understanding how AI "speaks" can we make it a capable assistant.