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UPCS: Unbiased Persona Construction for Dialogue Generation

Kuiyun Chen, Yanbin Wei

TL;DR

The paper tackles toxic biases in persona-based dialogue systems by introducing the Unbiased Persona Construction System (UPCS), which maintains two complementary persona sets—Debiased and Unbiased—within an eight-dimension persona model. It combines automated generation (via BART and GPT-3.5) with manual validation and a four-phase process (Description, Initial Construction, Bias Elimination, and Collaborative Filtering) to reduce bias while preserving expressive richness. The Debiased set systematically removes biased content, whereas the Unbiased set re-samples dimensions according to unbiased distributions $D_{unbias}$, enabling robust, fair personalization when generating dialogues with a model like $P^{2}$Bot. Across subjective and objective evaluations, UPCS outperforms baselines (including PeaCok) in bias reduction and user satisfaction while maintaining comparable dialogue quality, demonstrating its practical relevance for fair, personalized dialogue systems. The approach offers avenues for online bias adaptation and topic-constrained debiasing, and it can be extended with graph-based or multimodal integrations to further enhance reliability and inclusivity in real-world deployments.

Abstract

Narrative systems, such as dialogue and storytelling systems, often utilize persona profiles to enhance personalized interactions. Existing persona profiles frequently exhibit biases, posing risks to system integrity and fairness. To address this, we introduce the UPCS framework, which categorizes character descriptions into eight dimensions, including bias mitigation strategies. Experimental results demonstrate UPCS's superiority in accuracy, diversity, bias elimination, and user satisfaction, marking a significant advancement in persona construction for reliable narrative systems.

UPCS: Unbiased Persona Construction for Dialogue Generation

TL;DR

The paper tackles toxic biases in persona-based dialogue systems by introducing the Unbiased Persona Construction System (UPCS), which maintains two complementary persona sets—Debiased and Unbiased—within an eight-dimension persona model. It combines automated generation (via BART and GPT-3.5) with manual validation and a four-phase process (Description, Initial Construction, Bias Elimination, and Collaborative Filtering) to reduce bias while preserving expressive richness. The Debiased set systematically removes biased content, whereas the Unbiased set re-samples dimensions according to unbiased distributions , enabling robust, fair personalization when generating dialogues with a model like Bot. Across subjective and objective evaluations, UPCS outperforms baselines (including PeaCok) in bias reduction and user satisfaction while maintaining comparable dialogue quality, demonstrating its practical relevance for fair, personalized dialogue systems. The approach offers avenues for online bias adaptation and topic-constrained debiasing, and it can be extended with graph-based or multimodal integrations to further enhance reliability and inclusivity in real-world deployments.

Abstract

Narrative systems, such as dialogue and storytelling systems, often utilize persona profiles to enhance personalized interactions. Existing persona profiles frequently exhibit biases, posing risks to system integrity and fairness. To address this, we introduce the UPCS framework, which categorizes character descriptions into eight dimensions, including bias mitigation strategies. Experimental results demonstrate UPCS's superiority in accuracy, diversity, bias elimination, and user satisfaction, marking a significant advancement in persona construction for reliable narrative systems.
Paper Structure (16 sections, 3 equations, 2 figures, 3 tables)

This paper contains 16 sections, 3 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Debiased Persona Set Construction
  5. Phase 1: Character Description Generation
  6. Phase 2: Initial Persona Construction
  7. Phase 3: Bias Elimination
  8. Phase 4: Collaborative Filtering
  9. Unbiased Persona Set Construction
  10. Experiment
  11. Experiment Setup
  12. Results
  13. Evaluation Results
  14. Ablation Study
  15. Conclusion
  16. ...and 1 more sections

Figures (2)

  • Figure 1: The UPCS framework innovatively combines debiased and unbiased personas for personalized dialogue generation, with an eight-dimensional role modeling for general personality.
  • Figure 2: Rough descriptions of the eight dimensions of character roles.