State-to-draft auditing with provenance-verified transitions raises STALE strict-protocol accuracy from .686 to .736, a +5.0 point paired gain led by implicit policy adaptation and premise resistance.
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation
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abstract
Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines. The persona-based dialogue generation task is thus introduced to tackle the personality-inconsistent problem by incorporating explicit persona text into dialogue generation models. Despite the success of existing persona-based models on generating human-like responses, their one-stage decoding framework can hardly avoid the generation of inconsistent persona words. In this work, we introduce a three-stage framework that employs a generate-delete-rewrite mechanism to delete inconsistent words from a generated response prototype and further rewrite it to a personality-consistent one. We carry out evaluations by both human and automatic metrics. Experiments on the Persona-Chat dataset show that our approach achieves good performance.
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cs.AI 1years
2026 1verdicts
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When Memory Updates but Behavior Does Not: Repairing Implicit Stale Dependencies in Personalized Agent Responses
State-to-draft auditing with provenance-verified transitions raises STALE strict-protocol accuracy from .686 to .736, a +5.0 point paired gain led by implicit policy adaptation and premise resistance.