Introduction

With the advancement of generative artificial intelligence (AI) and automated language systems, the issue of impersonal or "robotic" text has become increasingly relevant. While AI-generated content can be highly informative, it often lacks the natural flow, emotion, and relatability of human language. Text humanization is the process of adapting machine-generated language to align with norms of human communication, including emotional tone, syntactic variety, idiomatic expressions, and audience awareness.

Theoretical Foundations of Humanization

Text humanization refers to the shift from a formal, semantic structure toward a pragmatically relevant expression. At its core are cognitive and discursive models that consider context, speaker intent, cultural factors, and genre conventions. Key criteria for evaluating the "humanity" of a text include:

• Emotional richness
• Stylistic flexibility
• Audience adaptation
• Use of natural language constructions

Methods and Technologies

Modern approaches to text humanization can be grouped into three main categories: linguistic, machine learning-based, and hybrid systems. Below are the most prominent methods:

1. Linguistic Rule-Based Approach

This method relies on predefined transformation rules. It uses synonym dictionaries, idiom lists, and rhetorical templates to convert formal phrases into more conversational language. Often applied in translation post-editing or content proofreading tools.

2. Neural Language Models

Advanced language models (e.g., GPT, T5, LLaMA) are trained on vast corpora of human-written texts, enabling them to generate outputs that closely resemble natural communication. However, default outputs may still lack diversity or emotional depth.

Fine-tuning on domain-specific or stylistically rich datasets (e.g., literature, blogs, social media) improves emotional nuance and contextual alignment.

3. Style and Prompt Engineering

One effective technique is instructing the AI to write in a particular tone or register (e.g., “Make it friendly and warm”). This prompt-based method allows dynamic adjustment of tone, syntax, and lexical choices according to context.

4. Interactive Feedback Systems

Some platforms incorporate user feedback—such as "like/dislike" ratings or manual edits—to continuously improve model outputs. Reinforcement learning or rule refinement is used to evolve the system based on real-world user responses.

5. Hybrid Approaches

Combining neural generation with manual review or editing delivers the highest quality results. For instance, a system may offer three humanized variants from which a user selects the best. This is particularly effective in marketing, journalism, and customer service.

Evaluation Criteria for Humanized Text

Humanization quality can be assessed using several metrics:

• Reader perception and usability studies
• Readability indices (e.g., Flesch-Kincaid)
• Sentiment and emotional tone analysis
• Comparative analysis with human-written reference texts

Conclusion

Text humanization is a critical interdisciplinary challenge that bridges linguistics, cognitive science, and artificial intelligence. It enhances machine-human interaction, improves user experience, and broadens the usability of generative text systems. Future developments will focus on creating more adaptive models capable of culturally and contextually appropriate communication.