Papers by Zhiyi Luo

3 papers
ExtRA: Extracting Prominent Review Aspects from Customer Feedback (D18-1)

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Challenge: Existing methods for analyzing and summarizing customer reviews are based on a number of prominent review aspects.
Approach: They propose a framework for extracting the most prominent aspects of a given product type from textual reviews.
Outcome: The proposed framework extracts K most prominent aspect terms which do not overlap semantically without supervision.
StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs (2026.findings-acl)

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Challenge: Prior work focused on typographic and pixel-level perturbations, leaving the study of SCO unexplored.
Approach: They propose a framework that exploits MLLMs' diagrammatic reasoning capabilities to bypass safety guardrails.
Outcome: The proposed framework exploits the model's reasoning capabilities to bypass safety guardrails.
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (D18-1)

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Challenge: Convolutional neural networks (CNNs) can't generate summaries of desired lengths due to space or length constraints.
Approach: They propose an approach to constrain the summary length by extending a convolutional sequence to sequence model.
Outcome: The proposed model outperforms baseline models in terms of ROUGE score, length variations and semantic similarity.

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