In a June 12, 2024 paper researchers from Tencent AI and the Harbin Institute of Technology introduced TasTe, a method for teaching large language models (LLMs) to translate through self-reflection.

The key idea is to enable LLMs to generate preliminary translations (i.e., drafts), self-evaluate their own translations, and make refinements based on the evaluation.

The researchers explained that LLMs have shown exceptional performance across various natural language processing tasks, including machine translation (MT). However, their translations still do not match the quality of supervised neural machine translation (NMT) systems.

To address this, the authors proposed the TasTe framework (translating through self-reflection), which improves the translation capabilities of LLMs by incorporating a self-reflection process. 

Source:https://slator.com/

Full article: https://slator.com/how-to-teach-large-language-models-to-translate-through-self-reflection/