What techniques mitigate semantic drift in multi-turn Generative AI conversations

Question

With the help of code in python can you explain What techniques mitigate semantic drift in multi-turn Generative AI conversations?

Answer 1

Techniques like grounding responses to external knowledge, maintaining dialogue history, and using attention mechanisms mitigate semantic drift by aligning responses with consistent and relevant context in multi-turn conversations.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Dialogue History: Maintains consistent context for multi-turn conversations.
• Tokenizer and Model: Ensures semantic alignment through pre-trained conversational models.
• Grounding Responses: Generates contextually relevant answers by referencing prior interactions.

Hence, maintaining dialogue history and leveraging attention mechanisms reduce semantic drift, ensuring coherent and contextually aligned multi-turn conversations.