Papers by Zixuan Xia

3 papers
MDR: Model-Specific Demonstration Retrieval at Inference Time for In-Context Learning (2024.naacl-long)

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Challenge: Existing methods for retrieval-based in-context learning ignore model biases and fail to retrieve the most appropriate demonstrations for different LLMs.
Approach: They propose a model-specific demonstration retrieval method that considers the biases of different LLMs at inference time.
Outcome: The proposed method improves performance on seen and unseen tasks with multi-scale inference LLMs by up to 41.2%.
SSS: Editing Factual Knowledge in Language Models towards Semantic Sparse Space (2024.findings-acl)

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Challenge: Existing methods to modify LMs suffer from sub-optimal locality, where irrelevant neighborhood examples can be adversely influenced.
Approach: They propose to use a model editing method to modify specific examples in LMs to improve locality and reasoning capability by directing the hidden state of edit example towards spaces where semantics are sparse.
Outcome: The proposed method improves locality and reasoning capability on two datasets.
The Threat of PROMPTS in Large Language Models: A System and User Prompt Perspective (2025.findings-acl)

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Challenge: Prompts are essential for guiding model output and influencing content generation.
Approach: They propose to attack models with prompt leakage and prompt jailbreak attacks . they summarize the experimental setups of these methods and explore the relationship between prompt threats and prompt injection attacks.
Outcome: The proposed methods summarize the experimental setups and examine the relationship between prompt threats and prompt injection attacks.

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