Title: Development and Research of a Method for the Combined Use of Large Language Models for Text Generation
Authors: Anna Suprun, Iryna Tvoroshenko, Volodymyr Gorokhovatskyi, Olena Yakovleva
Volume: 9
Issue: 10
Pages: 249-263
Publication Date: 2025/10/28
Abstract:
This paper presents a comparative analysis of three advanced large language models (GPT-4o, Claude 4 Opus, and Gemini 2.5 Pro) applied to creative text analysis and generation tasks. The study examines each model's performance across multiple narrative scenarios of varying complexity, assessing both analytical accuracy and literary expressiveness using integrated linguistic, logical, and stylistic metrics. Experimental evaluation showed that Claude 4 Opus achieved the highest analytical consistency with minimal hallucination rate and strong logical reasoning, while Gemini 2.5 Pro excelled in generation quality, demonstrating superior stylistic coherence, emotional depth, and grammatical precision. GPT-4o, in turn, maintained high contextual completeness but revealed a tendency toward interpretive creativity and higher variance in factual precision. Building on these findings, a new method for combined utilization of LLMs was developed and tested. In this approach, Claude 4 Opus serves as the analytic module, performing structured narrative decomposition and contextual synthesis, while Gemini 2.5 Pro acts as the generative module, transforming the processed analytical output into artistically refined text. Experimental validation demonstrated that the proposed method achieved an average generation quality index that surpassed each model's individual results in coherence, emotional integrity, and stylistic harmony. These outcomes confirm the effectiveness of inter-model collaboration for enhancing both analytical precision and creative depth in LLM-based literary text generation, offering a promising direction for future hybrid human-AI creative systems.