| Challenge: | a recent study has shown that personas influence LLM performance, but their direct impact remains unclear. |
| Approach: | They propose a novel approach to guiding LLM behaviour through role vectors . they construct 29 role vector derived from model activations and evaluate their impact . |
| Outcome: | The proposed approach improves in-domain task performance while yielding unexpected gains. |
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| Challenge: | Existing literature on leveraging persona in large language models is disorganized and lacks a systematic taxonomy . leveraging peopleas has resurfaced as an ideal lens for adapting LLMs for specific contexts . |
| Approach: | They propose to categorize current research on leveraging persona in large language models . they propose to use a comprehensive survey to categorize existing studies . |
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Style Vectors for Steering Generative Large Language Models (2024.findings-eacl)
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The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead? (2024.emnlp-main)
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| Challenge: | Large language models have shown capabilities close to human performance in various analytical tasks. |
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| Challenge: | Large language models (LLMs) exhibit remarkable versatility in adopting diverse personas. |
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| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
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When ”A Helpful Assistant” Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models (2024.findings-emnlp)
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| Challenge: | Commercial AI systems often define the role of the LLM in system prompts. |
| Approach: | They conduct a systematic evaluation of personas in system prompts by adding 162 roles covering 6 types of interpersonal relationships and 8 domains of expertise. |
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Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs (2026.findings-eacl)
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| Challenge: | Despite efforts to mitigate social bias in large language models, representational harms such as stereotyping continue to exist in both open and closed-source models. |
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Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in various tasks, but may rely on dataset biases as shortcuts for prediction. |
| Approach: | They propose to use a test suite to evaluate the impact of shortcuts on LLMs' performance. |
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Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance (2025.emnlp-main)
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| Challenge: | Prior work on persona prompting has shown mixed results on its effectiveness . prior work did not consider when and why personas should affect performance . |
| Approach: | They analyze literature on persona prompting and distill three desiderata for their effectiveness . they propose mitigation strategies to improve robustness but find they only work for the largest, most capable models . |
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