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A Disentangled-Attention Based Framework with Persona-Aware Prompt Learning for Dialogue Generation

AAAI 2023 · Large language models › Language, safety and interpretability · approach: Faithfulness of LLM explanations

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the proposed framework instead of P² BotPersona-grounded dialogue generation · PERSONA-CHAT original testBLEU: 26.90 -> 28.54Helped
Use the proposed framework instead of P² BotPersona-grounded dialogue generation · PERSONA-CHAT original testF1: 19.67 -> 19.84Helped
Use the proposed framework instead of P² BotPersona-grounded dialogue generation · PERSONA-CHAT revised testBLEU: 26.25 -> 29.68Helped
Use the proposed framework instead of P² BotPersona-grounded dialogue generation · PERSONA-CHAT revised testConsistency: 1.325 -> 1.527Helped
Add Sequential Persona Selection (ablation)Proposed dialogue generation framework · PERSONA-CHAT revised dataBLEU: 28.20 -> 29.68Helped
Add persona-aware prompting (ablation)Proposed dialogue generation framework · PERSONA-CHAT revised dataBLEU: 28.76 -> 29.68Helped
Add the Disentangled-Attention decoder (ablation)Proposed dialogue generation framework · PERSONA-CHAT revised dataBLEU: 28.45 -> 29.68Helped
Add the Persona-aware adapter (ablation)Disentangled-Attention decoder · PERSONA-CHAT revised dataBLEU: 27.38 -> 29.68Helped
Add the Context-aware adapter (ablation)Disentangled-Attention decoder · PERSONA-CHAT revised dataBLEU: 28.65 -> 29.68Helped
Add the Persona-Context-aware adapter (ablation)Disentangled-Attention decoder · PERSONA-CHAT revised dataBLEU: 27.87 -> 29.68Helped
Add A*-like keyword heuristics (ablation)Proposed dialogue generation framework · PERSONA-CHAT revised dataBLEU: 28.93 -> 29.68Helped

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