most citedPersona is a Double-edged Sword: Mitigating the Negative Impact of Role-playing Prompts in Zero-shot Reasoning Tasks

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Personalized Privacy Control in LLMs via Attention Head Intervention

Junseok Kim, Nakyeong Yang, Kyomin Jung

The rise of agentic AI enables LLMs to access diverse user data, raising critical privacy concerns. Prior work on contextual privacy studies whether LLMs regulate information discl…

cs.CL2026

Persona Switch: Mixing Distinct Perspectives in Decoding Time

Junseok Kim, Nakyeong Yang, Kyomin Jung

Role-play prompting is known to steer the behavior of language models by injecting a persona into the prompt, improving their zero-shot reasoning capabilities. However, such improv…

cs.CL2026

Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning

Junseok Kim, Nakyeong Yang, Kyungmin Min +1

Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost. Adaptive self-consistency methods mitigate this issue by ad…

cs.CL2024★ 2 cited

Persona is a Double-edged Sword: Mitigating the Negative Impact of Role-playing Prompts in Zero-shot Reasoning Tasks

Junseok Kim, Nakyeong Yang, Kyomin Jung

Recent studies demonstrate that prompting a role-playing persona to an LLM improves reasoning capability. However, assigning an adequate persona is difficult since LLMs are extreme…

cs.CL2024

Unplug and Play Language Models: Decomposing Experts in Language Models at Inference Time

Nakyeong Yang, Jiwon Moon, Junseok Kim +2

Enabled by large-scale text corpora with huge parameters, pre-trained language models operate as multi-task experts using a single model architecture. However, recent studies have…