Evolution of computer-assisted translation systems: from traditional CAT-tools to large language models

Abstract

The study is devoted to a profound analysis of how the rapid digitalization of the translation industry dictates new rules for training future linguists. Today, we witness not merely a software update, but a genuine shift in the professional paradigm: from conventional CAT-tools and translation memory accumulation to the large-scale involvement of large language models (LLMs). The paper traces the evolution where neural networks and generative artificial intelligence become not just assistants, but full-fledged cognitive partners of a translator.

The focus is on the radical transformation of a translatorʼs role. The modern translator is gradually moving away from the traditional “blank slateˮ approach, mastering the skills of prompt engineering and intelligent post-editing. Analyzing the experience of such platforms as RWS Trados and MemoQ, the article emphasizes that the future of the translation industry lies in hybrid ecosystems, where the technological power of AI is combined with the terminological rigor of classical tools.

The central idea of the research is the transition from the closed “black boxˮ model to the transparent “white boxˮ concept. This allows for shifting the use of neural networks from the realm of academic dishonesty into the zone of conscious learning. A viable mechanism is proposed the introduction of mandatory AI interaction reports for translators. In this format, students do not merely report on the finished text but justify the logic of their queries and every correction made after the machine output. This transforms the requirement of integrity from a formal prohibition into an organic element of professional ethics.

The practical significance of the study is in the development of a laboratory work algorithm based on critical literacy. The proposed method of multi-system translation comparison teaches students to distinguish between literal transcoding and AI “hallucinations ˮ. The conclusions emphasize that the new digital capability of a linguist is, above all, a synergy of high technology, ethical responsibility, and critical thinking within the “human-in-the-loop” model.