What methods can you apply to fix contextual drift in language models during dialogue generation

Question

With the help of code can i know What methods can you apply to fix contextual drift in language models during dialogue generation?

Answer 1

Contextual drift in dialogue generation can be mitigated by using techniques like memory-augmented attention, sliding context windows, response grounding, and reinforcement learning from human feedback (RLHF).

Here is the code snippet you can refer to:

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

• Maintains conversation history to keep context across turns.
• Uses a sliding context window to avoid excessive drift from old conversations.
• Limits response length and trims unnecessary information to stay on topic.

Hence, managing context length and ensuring focused, consistent dialogue history prevents contextual drift, keeping language models coherent and relevant in ongoing conversations.