Spotting Out-of-Character Behavior: Atomic-Level Evaluation of Persona Fidelity in Open-Ended Generation
Authors: Jisu Shin, Juhyun Oh, Eunsu Kim, Hoyun Song, Alice Oh
Accepted at: acl25


Reviewer paper summaries: This paper addresses the problem of evaluating persona fidelity in large language models (LLMs) through a novel atomic-level evaluation framework. The authors identify a significant limitation in current evaluation methods: the tendency to assign single scores to entire responses, which fails to capture subtle inconsistencies in persona expression throughout long-form text. Their approach introduces three metrics (ACCatom, ICatom, and RCatom) that analyze persona adherence at a finer granularity, enabling more precise detection of Out-of-Character (OOC) behavior.

The authors then provide a more detailed analysis of these three metrics along different axes such as as length of generation, base LLM model, number of sentences in the generation etc. By analyzing individual sentences as atomic units, the approach claims to capture nuanced persona misalignments that previous metrics overlook.
Summary of strengths: The experimental design is robust, evaluating 12 different LLMs across three distinct task types (interviews, essays, social media posts) and 15 personality personas based on the Big Five personality traits.

The analysis reveals important insights about model behavior under different conditions, such as the finding that models perform better with socially desirable personas and struggle with neutral personas.

By analyzing individual sentences as atomic units, the approach captures nuanced persona misalignments that previous metrics overlook.
The three proposed metrics effectively capture different dimensions of persona fidelity: accuracy (ACCatom), internal consistency (ICatom), and reproducibility across generations (RCatom).
Summary of weaknesses: The study focuses exclusively on personality as the domain for persona creation and evaluation. While personality is a well-established domain, testing the framework on other domains (political views, values, expertise levels) would strengthen claims about its generalizability.

Using GPT-4o as the scoring model introduces potential biases and circularity in the evaluation. The authors acknowledge this limitation, but additional validation approaches (e.g., human evaluation correlation) would strengthen the methodology.

The paper sets sentences as atomic units without exploring alternative segmentation approaches. The authors acknowledge that contradictions within sentences might go undetected, but don't explore information-level or phrase-level analysis as potential improvements.
Comments, suggestions and typos: Expanded Domain Testing: Test the framework on domains beyond personality, such as expertise levels or ethical values.

Human Validation: Include human evaluation studies to validate that the atomic-level detection of inconsistencies aligns with human perception.

Alternative Segmentation: Explore different approaches to defining atomic units beyond sentence-level segmentation.

Error Analysis: Provide deeper analysis of cases where metrics diverge to better understand their complementary nature.

Mitigation Strategies: Discuss potential approaches to improve persona fidelity based on the insights gained from the evaluation framework.
Overall assessment scores: 3