Skip to content

Using the Three-Step Reflection Translation Method

Using AI translation (such as ChatGPT, Gemini, etc.) for subtitle translation can significantly improve translation quality by fully leveraging contextual information, despite occasional minor issues such as empty lines.

Video Translation v3.12 has added the "Three-Step Reflection Translation Method" to further optimize AI translation results. This method requires the AI to first perform a literal translation, then reflect on the shortcomings of the literal translation and propose modifications, and finally perform an interpretive translation of the original text based on the suggestions. These three iterations can significantly improve translation accuracy.

However, the "Three-Step Reflection Translation Method" also has some limitations: Firstly, it consumes more tokens; secondly, it requires a higher level of intelligence from the AI model, and the output results of low-intelligence models are often disorganized. Therefore, this function only supports online large language models such as OpenAI ChatGPT, Gemini, and does not support local small models.

How to Enable:

Step 1: Enable Advanced Options

In the menu bar, click "Tools" -> "Options" -> "Advanced Options" in sequence, and then check the two options shown in the figure below.

image.png

Step 2: Select an AI Translation Channel

In the "Translation Channel" option, select a channel that supports AI translation, such as "OpenAI ChatGPT/Gemini/Claude AI/302.AI".

image.png

Step 3: Select a Suitable Model

When selecting a model, for the ChatGPT series, be sure to select the "gpt-4" or "gpt-4-mini" model, and avoid using "gpt-3.5". This is because the higher the intelligence of the model, the better the effect of the "Three-Step Reflection Translation Method"; otherwise, you may get meaningless results.

In summary, combining the "Three-Step Reflection Translation Method" with a suitable AI model can greatly improve the quality and efficiency of subtitle translation. Please pay attention to selecting the appropriate model for optimal results.