CAT - GPT : A Skopos-Driven, LLM -Based Computer-Assisted Translation Tool (2025)

Abstract: This paper introduces CAT-GPT, an innovative Computer-Assisted Translation (CAT) tool designed to address context-awareness and terminological consistency challenges often encountered in standard CAT workflows. Grounded in Skopos theory (Vermeer, 2014) and powered by a Large Language Model (LLM) backend, CAT-GPT integrates context-sensitive segmentation, automatically generated and adjustable translation instructions, and an advanced machine translation component. Comparative observations with a widely used CAT tool (e.g., Trados Studio) suggest that CAT-GPT reduces post-editing effort and improves text-level coherence, especially in specialized or domain-specific scenarios.

## introduction cat tools form a cornerstone of modern translation workflows, providing features such as translation memories, terminology management, and built-in machine translation @xcite . however, many of these systems rely on sentencelevel segmentation without robust methods for maintaining broader context @xcite . consequently, translations can become fragmented, leading to increased post-editing and potential inconsistencies in specialized content @xcite . furthermore, texts requiring high terminological precision and clear functional alignment-such as legal or technical documentation-can suffer when each sentence is treated in isolation. to address these gaps, i present cat-gpt, a tool that combines gpt-4o with context-sensitive segmentation and user-defined instructions grounded in skopos theory. by allowing translators to specify functional goals and revise guidelines throughout the process, cat-gpt aligns the final product with the intended communicative purpose @xcite . ## comparison with existing cat tools a mainstream sentence-based cat tool (rws trados studio 2022) was observed alongside cat-gpt for a project that contained repeated references to specific articles and multiple official titles across several paragraphs. the sentencebased system often broke up closely related items, forcing minor inconsistencies to accumulate whenever a role or article name recurred @xcite . for instance, designations might appear in slightly varied translations across different sentences, requiring corrections each time. cat-gpt's paragraph-level segmentation, by contrast, preserved the logical structure of these references, enabling them to be rendered consistently each time they appeared. once the translator or reviewer introduced updated guidelines-for example, a new stylistic approach to referencing articles-cat-gpt immediately integrated these changes into subsequent machine translation output. as a result, the text maintained a uniform presentation of repeated terms from one paragraph to another, reducing editing passes and aligning with the text's overall communicative objectives @xcite . future work will expand testing against other llm-based cat tools. ## conclusion by merging gpt-4o with context-sensitive segmentation and skopos-focused instructions, cat-gpt addresses key deficiencies in conventional cat workflows. early outcomes suggest that it reduces post-editing demands, enhances terminological consistency, and preserves the text's communicative purpose. future development plans include scaling up to larger projects, expanding language support, and refining the interface to meet varied professional and academic needs.