Papers by Tie Xu

    2 papers
    SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator (2026.acl-long)

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    Challenge: SafeAgent improves agent safety through fully automated synthetic data generation.
    Approach: They propose a framework that improves agent safety through fully automated synthetic data generation.
    Outcome: The proposed framework outperforms closed-source models on two safety benchmarks and one real-world task.
    Incorporating Task-Specific Concept Knowledge into Script Learning (2023.eacl-main)

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    Challenge: Existing work on Goal-Oriented Scripts ignore usage context and personal preferences . proposed tasks are restrictive and rely on overly simplified assumptions .
    Approach: They propose a novel approach to Goal-Oriented Script Completion that uses concept prompting and script-oriented contrastive learning to improve performance.
    Outcome: The proposed approach improves on a WikiHow-based dataset.

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