Papers by Yuan Pu

3 papers
Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection (2026.acl-long)

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Challenge: a rapid proliferation of large language models (LLMs) generate text that increasingly resembles human writing . this makes it difficult to capture subtle cues that distinguish AI-generated content from human-written content .
Approach: They propose a framework that disentangles AI-detection semantics from generator-aware artifacts by latent encoding and perturbation-based regularization.
Outcome: The proposed framework disentangles AI-detection semantics from generator-aware artifacts on 20 representative LLMs across 7 categories.
MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication (2026.findings-acl)

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Challenge: Large language models (LLMs) are increasingly being used by lay users for medical advice, but they have not yet been tested for this crucial competency.
Approach: They develop a semi-automated pipeline to curate MedRedFlag, a dataset of 1100+ reddit questions that require redirection.
Outcome: The proposed pipeline compares state-of-the-art LLMs to those from clinicians to find out how they perform under real-world health communication.
Text Editing as Imitation Game (2022.findings-emnlp)

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Challenge: Text editing is an important domain of processing tasks to edit the text in a localized fashion, such as text simplification.
Approach: They propose a nonautoregressive decoder for state-to-action demonstrations that parallels the decoding while retaining the dependencies between tokens.
Outcome: The proposed model outperforms the autoregressive baselines on a suite of Arithmetic Equation benchmarks in terms of performance, efficiency, and robustness.

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