Papers by Xiaoling Bai

4 papers
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)

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Challenge: Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text.
Approach: They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types.
Outcome: The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages.
Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction (2026.findings-acl)

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Challenge: Prompt injection attacks manipulate large language models (LLMs) by misleading them to deviate from the original input instructions and execute maliciously injected instructions.
Approach: They propose a prompt injection defense method that suppresses the model's instruction-following tendencies rather than suppressing them.
Outcome: The proposed method outperforms prompt-engineering-based approaches and fine-tuning methods and reduces the ASR to nearly 0% in some scenarios.
Event-enhanced Retrieval in Real-time Search (2024.lrec-main)

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Challenge: Existing embedding-based retrieval models face the "semantic drift" problem . a low adoption rate of retrieval results is evident in real-time search scenarios .
Approach: They propose an embedding-based retrieval approach that enhances real-time retrieval performance by adding contrastive learning to the dual-encoder model.
Outcome: The proposed approach improves the dual-encoder model of traditional EBR.
Event-Centric Query Expansion in Web Search (2023.acl-industry)

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Challenge: Existing studies rely on long-term search log mining to improve search experience . EQE system is a novel event retrieval framework that can select the best expansion from a significant amount of potential events quickly and accurately.
Approach: They propose a QE system that uses a four-stage event retrieval framework . they collect news headlines and then refine a dual-tower semantic model to serve as an encoder .
Outcome: The proposed system can select the best expansion from a significant amount of potential events quickly and accurately.

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