Academic

PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations

arXiv:2603.06485v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) seeks to enhance the transparency and accountability of machine learning systems, yet most methods follow a one-size-fits-all paradigm that neglects user differences in expertise, goals, and cognitive needs. Although Large Language Models can translate technical explanations into natural language, they introduce challenges related to faithfulness and hallucinations. To address these challenges, we present PONTE (Personalized Orchestration for Natural language Trustworthy Explanations), a human-in-the-loop framework for adaptive and reliable XAI narratives. PONTE models personalization as a closed-loop validation and adaptation process rather than prompt engineering. It combines: (i) a low-dimensional preference model capturing stylistic requirements; (ii) a preference-conditioned generator grounded in structured XAI artifacts; and (iii) verification modules enforcing numerical faithfulness, infor

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Vittoria Vineis, Matteo Silvestri, Lorenzo Antonelli, Filippo Betello, Gabriele Tolomei · · 1 min read · 14 views
#cs.CL #cs.AI

arXiv:2603.06485v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) seeks to enhance the transparency and accountability of machine learning systems, yet most methods follow a one-size-fits-all paradigm that neglects user differences in expertise, goals, and cognitive needs. Although Large Language Models can translate technical explanations into natural language, they introduce challenges related to faithfulness and hallucinations. To address these challenges, we present PONTE (Personalized Orchestration for Natural language Trustworthy Explanations), a human-in-the-loop framework for adaptive and reliable XAI narratives. PONTE models personalization as a closed-loop validation and adaptation process rather than prompt engineering. It combines: (i) a low-dimensional preference model capturing stylistic requirements; (ii) a preference-conditioned generator grounded in structured XAI artifacts; and (iii) verification modules enforcing numerical faithfulness, informational completeness, and stylistic alignment, optionally supported by retrieval-grounded argumentation. User feedback iteratively updates the preference state, enabling quick personalization. Automatic and human evaluations across healthcare and finance domains show that the verification-refinement loop substantially improves completeness and stylistic alignment over validation-free generation. Human studies further confirm strong agreement between intended preference vectors and perceived style, robustness to generation stochasticity, and consistently positive quality assessments.

Executive Summary

The article introduces PONTE, a human-in-the-loop framework for personalized and trustworthy explanations in Explainable Artificial Intelligence (XAI). PONTE addresses the limitations of one-size-fits-all approaches by incorporating user feedback and preferences to generate adaptive and reliable XAI narratives. The framework combines a low-dimensional preference model, a preference-conditioned generator, and verification modules to ensure faithfulness, completeness, and stylistic alignment. Evaluations across healthcare and finance domains demonstrate the effectiveness of PONTE in improving the quality and personalization of XAI explanations.

Key Points

  • ▸ PONTE is a human-in-the-loop framework for personalized XAI explanations
  • ▸ The framework combines user preferences, generation, and verification modules
  • ▸ Evaluations demonstrate improved completeness and stylistic alignment

Merits

Personalization

PONTE's ability to incorporate user preferences and feedback enables tailored explanations that meet individual needs and goals.

Verification and Validation

The framework's verification modules ensure the faithfulness, completeness, and stylistic alignment of generated explanations, addressing concerns related to hallucinations and lack of transparency.

Demerits

Complexity

PONTE's human-in-the-loop approach and multiple components may introduce complexity and require significant resources for implementation and maintenance.

Scalability

The framework's reliance on user feedback and iterative updates may limit its scalability and applicability to large-scale XAI systems.

Expert Commentary

PONTE represents a significant step forward in the development of personalized and trustworthy XAI explanations. By incorporating user feedback and preferences, the framework addresses the limitations of one-size-fits-all approaches and demonstrates the potential for human-AI collaboration in generating high-quality explanations. However, further research is needed to address the complexity and scalability concerns associated with PONTE, as well as to explore its applications in various domains and contexts.

Recommendations

  • ✓ Future research should focus on simplifying and streamlining the PONTE framework to improve its scalability and applicability
  • ✓ The development of PONTE should be accompanied by rigorous evaluations and testing to ensure its effectiveness and reliability in various contexts and domains.

Sources

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