Papers with PI

3 papers
Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning (2026.findings-acl)

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Challenge: Large language model (LLM)-integrated applications face security vulnerabilities from prompt injection (PI) attacks.
Approach: They propose a model enhancement method that synthesizes diverse training data and employs instruction-level chain-of-thought fine-tuning to enable LLMs to effectively identify and reject malicious instructions regardless of their source or position in the context.
Outcome: The proposed method outperforms baselines in three critical dimensions while maintaining utility performance without degradation.
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction (2024.lrec-main)

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Challenge: Existing studies present tokens, examples, and hints for corrections, but do not directly explain the reasons in natural language.
Approach: They propose a method called controlled generation with Prompt Insertion that uses Large Language Models to explain the reasons for corrections in natural language.
Outcome: The proposed method can explain the reasons for corrections in natural language by guiding the LLMs to generate explanations for all correction points.
GBT: Generative Boosting Training Approach for Paraphrase Identification (2023.findings-emnlp)

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Challenge: Paraphrase Identification (PI) is a fundamental natural language understanding task with non-trivial challenges.
Approach: They propose a Generative Boosting Training approach for Paraphrase Identification (PI) they use a seq2seq model to perform DA on misclassified instances periodically .
Outcome: The proposed method outperforms state-of-the-art PI models on English and Chinese PI tasks with good efficiency and effectiveness.

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