Papers with persona alignment

3 papers
Enhancing Persona Consistency for LLMs’ Role-Playing using Persona-Aware Contrastive Learning (2025.findings-acl)

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Challenge: Existing methods for analyzing and analyzing large language models (LLMs) lack of emotion and fine-grained role awareness limits the model’s ability to provide personalized and diverse interactions further.
Approach: They propose an annotation-free framework to align LLMs’ behavior during role-playing, enhancing the model’s role consistency.
Outcome: The proposed framework outperforms vanilla LLMs under automatic evaluation methods and human expert evaluation.
Spotting Out-of-Character Behavior: Atomic-Level Evaluation of Persona Fidelity in Open-Ended Generation (2025.findings-acl)

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Challenge: Existing evaluation methods struggle to capture subtle inconsistencies in large language models.
Approach: They propose an atomic-level evaluation framework that quantifies persona fidelity at a finer granularity.
Outcome: The proposed framework detects inconsistencies that prior evaluation methods overlook . it captures subtle deviations that real users would encounter .
DualAlign: Generating Clinically Grounded Synthetic Data (2026.findings-acl)

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Challenge: Large language models (LLMs) can generate fluent clinical text, but ensuring that such outputs are clinically grounded and useful for downstream modeling remains challenging.
Approach: They propose a disease-agnostic framework for generating privacy-preserving, clinically faithful synthetic EHR narratives.
Outcome: The proposed framework produces context-aware, symptom-rich sentences that more closely reflect real-world clinical documentation.

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