Taming the stochastic parrot

Abstract

This article proposes a Translation Studies–oriented approach to understanding and managing variability in machine translation outputs generated by large language models (LLMs). Drawing on the metaphor of the “stochastic parrot,” the study introduces the concept of temperature as a means for controlling stochasticity in LLM-based translation. Through a practical and replicable experiment conducted in Google Colab, technical texts are translated from English to Spanish under varying temperature conditions. While the dataset is intentionally limited, the study’s primary contribution lies in establishing a replicable methodological pathway rather than in producing generalizable quantitative results. By combining computational experimentation with reflection on concepts relevant to translation theory, the study can inform both future research and practical approaches.