Key Technologies for Building Context-Aware Dialogue Systems
The following composite solution can be used to address the continuity challenges of multi-round conversations:
- Dialog state management::
- Maintain conversation history in JSON format (last 3 rounds recommended)
- utilization
tokenizer.apply_chat_templateBuilding session formats
- Model Call Optimization::
- Pass in the full dialog context for each generation (requires setting up the
truncation=True) - increase
past_key_valuesParameter Cache History Attention
- Pass in the full dialog context for each generation (requires setting up the
- post-processing enhancement::
- Rule-based denotational disambiguation (e.g., replacing 'it' with a concrete noun)
- Consistency check (key information for comparing historical responses)
In the customer service scenario test, the system was able to improve the conversation coherence score by 371 TP3T. It is recommended to work with frameworks such as Rasa to implement more complex conversation logic.
This answer comes from the articleBonsai: A three-valued weighted language model suitable for operation on edge devicesThe
May not be reproduced without permission:AI productivity tools " How to improve Bonsai's coherence in multi-round conversations?
