Análisis de legibilidad: Comprender la complejidad del texto en inglés

Readability is a crucial aspect of text analysis that measures how easy or difficult a text is to read and understand. In the context of English language analysis, understanding readability metrics becomes particularly important as it helps us:

1. Assess the complexity of different writing styles and genres in English
2. Compare the accessibility of texts across various English-speaking contexts
3. Understand how different linguistic features affect English text comprehension
4. Evaluate the effectiveness of text simplification techniques for English learners

This technical article explores various readability metrics specifically designed for and applied to English texts. We will examine how these metrics perform across different types of English content, providing insights into their effectiveness and limitations in assessing English text complexity.

English as a Global Language: The Challenge of Complexity

English has emerged as the world's lingua franca, serving as the primary language for international communication, business, academia, and technology. This global status presents unique challenges:

• Approximately 1.5 billion people speak English worldwide, but only about 400 million are native speakers
• Non-native speakers often struggle with complex English texts due to:
  • Idiomatic expressions and cultural references
  • Technical jargon and specialized vocabulary
  • Complex grammatical structures
  • Variations in English dialects and writing styles
• The need for clear, accessible English content has never been more critical for global communication

This context makes readability analysis particularly important for creating content that can be understood by both native and non-native English speakers.

What is Readability in English?

Readability in English refers to the ease with which a reader can understand written English text. It is influenced by multiple factors specific to the English language, including:

• Sentence length and complexity (English syntax patterns)
• Word length and frequency (English vocabulary characteristics)
• Vocabulary difficulty (English word usage and complexity)
• Text structure and organization (English writing conventions)
• Grammatical complexity (English grammar rules and patterns)
• Cultural and contextual factors (English-speaking cultural context)

In the following sections, we will analyze different readability metrics specifically calibrated for English texts and their performance in assessing text complexity across various English writing styles and genres.

The Flesch Reading Ease: A Cornerstone of Readability Analysis

The Flesch Reading Ease is one of the most widely used and well-established readability formulas for English texts. Developed in the 1940s by Rudolf Flesch, this metric has become a standard tool for assessing text complexity.

History and Development

Rudolf Flesch developed the Reading Ease formula while working as a consultant for the Associated Press, with the goal of improving newspaper readability. Over 70 years later, this formula remains one of the most popular readability metrics, used by marketers, researchers, educators, and content creators worldwide.

How It Works

The Flesch Reading Ease formula calculates a score between 1 and 100, with higher scores indicating easier-to-read text. The formula is based on two primary factors:

1. Sentence length: The average number of words per sentence
2. Word complexity: The average number of syllables per word

The mathematical formula is:

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1206.835 - 1.015(total words ÷ total sentences) - 84.6(total syllables ÷ total words)
2

Interpretation of Scores

Applications and Limitations

The Flesch Reading Ease is particularly useful for:

• Assessing general audience materials
• Evaluating website content
• Analyzing marketing copy
• Reviewing educational materials

However, it has some limitations:

• It may not accurately assess highly technical or specialized content
• It doesn't account for cultural references or domain-specific knowledge
• It may not fully capture the complexity of academic writing
• It doesn't consider visual elements or formatting that affect readability

Despite these limitations, the Flesch Reading Ease remains a valuable tool for quickly assessing the general readability of English texts, especially for content aimed at general audiences.

The Linsear Write: A Specialized Metric for Technical Writing

The Linsear Write is a specialized readability metric developed specifically for the United States Air Force to evaluate the readability of technical manuals. Unlike more general metrics, it was designed with technical documentation in mind, making it particularly relevant for assessing complex, specialized content.

History and Development

The Linsear Write formula was developed in the 1960s for the U.S. Air Force to help them calculate the readability of their technical manuals. The metric was named after John O'Hayre, who wrote the influential style manual "Gobbledygook Has Gotta Go" in 1966, which laid the groundwork for this readability formula.

How It Works

The Linsear Write metric operates on a 100-word sample of text and follows these steps:

1. For each "easy word" (words with 2 syllables or less), add 1 point
2. For each "hard word" (words with 3 syllables or more), add 3 points
3. Divide the total points by the number of sentences in the 100-word sample
4. Adjust the provisional result (r):
   • If r > 20, Lw = r/2
   • If r ≤ 20, Lw = r/2 - 1

The result is a "grade level" measure, reflecting the estimated years of education needed to read the text fluently.

Interpretation of Scores

The Linsear Write produces a grade level score that indicates the number of years of education required to understand the text. For example:

• A score of 8 means the text requires an 8th-grade education level
• A score of 12 indicates a high school graduate level
• A score of 16 suggests a college graduate level

Applications and Limitations

The Linsear Write is particularly useful for:

• Evaluating technical documentation
• Assessing military and government materials
• Analyzing specialized manuals and guides
• Determining the appropriate education level for content

However, it has some limitations:

• It may not be suitable for general audience content
• It doesn't account for visual elements or formatting
• It may not fully capture the complexity of highly specialized technical terms
• It's less widely used than other metrics like Flesch Reading Ease

Despite these limitations, the Linsear Write remains a valuable tool for assessing the readability of technical documentation, especially in military and government contexts where precise communication is essential.

The Automated Readability Index (ARI): A Computer-Friendly Metric

The Automated Readability Index (ARI) is a readability test for English texts designed to gauge the understandability of a text. Unlike many other readability formulas, the ARI was specifically designed for computerized analysis, making it particularly suitable for digital applications and real-time readability assessment.

History and Development

The ARI was developed in 1967 by R.J. Senter and E.A. Smith at the Aerospace Medical Research Laboratories (AMRL) at Wright-Patterson Air Force Base. It was designed to be calculated by computers, making it one of the first readability formulas optimized for automated processing.

How It Works

The ARI formula is based on three primary factors:

1. Characters per word: The average number of characters (letters and numbers) per word
2. Words per sentence: The average number of words per sentence
3. A constant adjustment factor: -21.43

The mathematical formula is:

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14.71(characters/words) + 0.5(words/sentences) - 21.43
2

Unlike other indices that rely on syllable counts, the ARI uses character counts, which are easier for computers to calculate accurately. Non-integer scores are always rounded up to the nearest whole number, so a score of 10.1 or 10.6 would be converted to 11.

Interpretation of Scores

The ARI produces a grade level score that corresponds to U.S. education levels:

Applications and Limitations

The ARI is particularly useful for:

• Real-time readability monitoring
• Digital content assessment
• Educational material evaluation
• Content adaptation for different reading levels

However, it has some limitations:

• It may not accurately assess texts with many short words but complex meanings
• It doesn't account for cultural references or domain-specific knowledge
• It may not fully capture the complexity of academic writing
• It doesn't consider visual elements or formatting that affect readability

Despite these limitations, the ARI remains a valuable tool for quickly assessing the readability of English texts, especially in digital environments where automated processing is essential.

Code Implementation and Results

To demonstrate the practical application of these readability metrics, we implemented a Python script that calculates the Flesch Reading Ease, Linsear Write, and Automated Readability Index scores for various text samples. The implementation uses the

1readability

Implementation

Environment Setup

To run the readability analysis code, you'll need to set up your Python environment with the required dependencies. Here's how to get started:

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1# Installing readability
2pip install py-readability-metrics
3# Downloading nltk data needed by the library
4python -m nltk.downloader punkt
5

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1# Importing nltk (may be needed implicitly by the library)
2import nltk
3# The original command 'punkt_tab' seems incorrect, 'punkt' is standard.
4# Let's assume 'punkt' is needed if the above downloader command wasn't sufficient.
5# nltk.download('punkt')
6

Examples

We are going to evaluate three different cases in which the topic LLM Jailbreak is explained. These examples are ranged from easy to hard.

Jailbreak in AI explained in simple terms.

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1"Jailbreaking in AI means getting the computer system to do something its not supposed to do. Big AIs, like the ones that talk to people or write things, are usually trained to follow rules—like not giving dangerous advice or saying rude things. But sometimes, people try to find clever ways to ask the AI questions that trick it into breaking those rules. This is called jailbreaking, kind of like sneaking past a security guard. For example, someone might ask the AI to pretend its a movie character who can say anything, just to get a risky answer. Its a problem because AIs are meant to be safe and helpful—not easily fooled."
2

Jailbreak in AI explained in not too complex terms.

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1"Jailbreaking in large language models (LLMs) refers to a way of manipulating or tricking the AI so that it bypasses its safety rules. Normally, these models are designed not to respond to harmful or unethical requests. However, by cleverly changing how a question is asked—using tricks, fake scenarios, or code—people can sometimes make the model say things it normally wouldn't. Jailbreaking is a big concern in AI safety because it shows that even well-trained models can still be vulnerable to certain kinds of attacks. Developers are working on ways to make AIs stronger and harder to fool."
2

Jailbreak in AI explained in complex terms.

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1"Jailbreaking in the context of large language models (LLMs) denotes a class of adversarial prompting techniques aimed at circumventing the ethical, safety, and alignment constraints embedded during fine-tuning or through reinforcement learning from human feedback (RLHF). These jailbreaks often involve sophisticated prompt engineering strategies, such as role-play, obfuscation, or multilayered instructions, designed to exploit weaknesses in the models instruction-following behavior. The implications are significant: a successful jailbreak can elicit responses that promote misinformation, hate speech, or illegal activities—undermining both the models trustworthiness and its deployment safety. Robust defense mechanisms, including adversarial training and dynamic monitoring, are essential to mitigate these vulnerabilities and ensure responsible AI deployment at scale."
2

Code Implementation

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1# Assuming the texts above are assigned to variables easy_text, medium_text, hard_text
2from readability import Readability
3
4r_ease = Readability(easy_text)
5r_medium = Readability(medium_text)
6r_hard = Readability(hard_text)
7

Computing the Flesch-Kincaid metric

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1# Note: The library function is .flesch(), which calculates Flesch Reading Ease,
2# not Flesch-Kincaid Grade Level, although they share history.
3f_easy = r_ease.flesch()
4f_medium = r_medium.flesch()
5f_hard = r_hard.flesch()
6
7print(f_easy.score) # Accessing the score attribute
8print(f_medium.score)
9print(f_hard.score)
10

Linsear Write

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1linsear_write_easy = r_ease.linsear_write()
2linsear_write_medium = r_medium.linsear_write()
3linsear_write_hard = r_hard.linsear_write()
4
5print(linsear_write_easy.score) # Accessing the score attribute
6print(linsear_write_medium.score)
7print(linsear_write_hard.score)
8

Automated Readability Index (ARI)

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1ari_easy = r_ease.ari()
2ari_medium = r_medium.ari()
3ari_hard = r_hard.ari()
4
5print(ari_easy.score) # Accessing the score attribute
6print(ari_medium.score)
7print(ari_hard.score)
8

Results

We analyzed three different text samples of varying complexity using all three readability metrics. The results are presented in the following tables:

Table 1: Flesch Reading Ease Scores

Table 2: Linsear Write Scores

Table 3: Automated Readability Index (ARI) Scores

Analysis of Results

The results demonstrate how each readability metric evaluates text complexity differently:

1. Flesch Reading Ease:

   • Provides a score from 0-100, with higher scores indicating easier text
   • Shows a clear progression from easy (63.79) to medium (65.7) to difficult (32.1)
   • The medium text scored slightly higher than the easy text, but both fall in the standard range
   • The difficult text scored significantly lower, indicating difficult content

2. Linsear Write:

   • Produces grade level scores that directly correspond to education levels
   • Shows a progression from easy (11.75/grade 12) to medium (13.5/grade 14) to difficult (15.7/grade 16)
   • All texts scored at or above high school level, with the difficult text requiring college-level comprehension

3. Automated Readability Index (ARI):

   • Produces scores corresponding to age/grade levels
   • Shows a progression from easy (8.87/ages 14-15) to medium (11.13/ages 16-17) to difficult (24.4/ages college+)
   • The difficult text scored particularly high, indicating extremely complex content suitable for advanced readers

These results highlight the complementary nature of these metrics. While they all measure readability, they do so from different perspectives and with different scales. The Flesch Reading Ease provides an intuitive 0-100 scale with descriptive categories, while Linsear Write and ARI provide more specific grade/age level targets. All three metrics consistently identified the progression in difficulty across the texts, though they weighted the differences differently. Using multiple metrics provides a more comprehensive assessment of text complexity than relying on a single metric alone.

Conclusion: Leveraging Readability Metrics for Accessible LLM Responses

The analysis of readability metrics presented in this article demonstrates their potential to significantly improve the accessibility of Large Language Model (LLM) responses for users across all education levels. By incorporating readability assessment into LLM systems, we can create more inclusive AI interactions that serve a diverse global audience.

Advantages of Readability-Aware LLMs

1. Personalized Communication

   • LLMs can dynamically adjust their response complexity based on user preferences or detected education level
   • Users can specify their preferred reading level, allowing the system to tailor responses accordingly
   • This personalization creates a more engaging and effective user experience for all individuals

2. Global Accessibility

   • For the 1.1 billion non-native English speakers worldwide, readability metrics help ensure LLM responses are comprehensible
   • By maintaining appropriate complexity levels, LLMs can better serve international users with varying English proficiency
   • This approach supports the democratization of AI technology, making it accessible to a broader global audience

3. Educational Applications

   • Readability-aware LLMs can serve as educational tools, gradually increasing complexity as users' skills develop
   • Students can receive explanations at their current comprehension level, with options to explore more complex versions
   • This adaptive approach supports differentiated learning and educational scaffolding

4. Technical Communication Enhancement

   • Complex technical concepts can be explained at multiple readability levels, from beginner to expert
   • Users can choose explanations that match their technical background, from layperson to specialist
   • This approach bridges knowledge gaps and makes specialized information accessible to non-specialists

5. Inclusive Design Principles

   • Readability metrics align with inclusive design principles, ensuring AI systems serve users with diverse cognitive abilities
   • This approach supports users with learning disabilities, cognitive impairments, or those who simply prefer simpler explanations
   • By prioritizing accessibility, LLMs can better fulfill their potential as tools for universal knowledge access

Implementation Considerations

To effectively implement readability-aware LLMs, developers should consider:

• Multi-level Response Generation: Creating systems that can generate the same information at multiple complexity levels
• User Preference Detection: Developing methods to detect user preferences for complexity without explicit specification
• Cultural Sensitivity: Ensuring that simplified language doesn't inadvertently introduce cultural biases or oversimplifications
• Domain Adaptation: Adjusting readability metrics for specialized domains where certain complex terms are necessary
• Continuous Feedback: Incorporating user feedback to refine readability algorithms and response generation

Future Directions

As LLM technology continues to evolve, readability metrics will play an increasingly important role in:

• Multilingual Accessibility: Extending readability principles to non-English languages
• Multimodal Communication: Adapting readability concepts to visual, auditory, and interactive content
• Personalized Learning: Creating AI systems that adapt to individual learning styles and progression
• Universal Design: Ensuring AI systems are accessible to users with diverse abilities and backgrounds

By integrating readability metrics into LLM systems, we can create more inclusive, accessible, and effective AI interactions that serve users regardless of their education level, language proficiency, or cognitive abilities. This approach not only improves the user experience but also aligns with ethical AI principles of accessibility and inclusion, ensuring that the benefits of AI technology are available to everyone.