Papers by Zihan Niu

2 papers
Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation (2024.emnlp-main)

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Challenge: Large language models (LLMs) often produce factual errors due to limited internal knowledge.
Approach: They propose a retrieval-augmented generation framework that generates plan tokens to guide subsequent generation.
Outcome: The proposed framework improves the accuracy of large language models with external knowledge sources.
iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)

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Challenge: Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings.
Approach: They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences.
Outcome: The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications.

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