Papers by Chenyan WU

2 papers
DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models (2026.findings-acl)

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Challenge: Current methods for editing personality traits in large language models can change personalities but reduce performance.
Approach: They propose a novel paradigm for personality editing that locates and edits LLM neurons and enables competitive personality control at inference time.
Outcome: Experiments on LLaMA-3-8B-Instruct and Qwen2.5-7B-instruct show that the proposed approach can improve performance and improve performance.
Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)

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Challenge: Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult.
Approach: They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples.
Outcome: The proposed approach improves preference while preserving utility.

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