Papers by Youcheng Sun
BEDAA: Bayesian Enhanced DeBERTa for Uncertainty-Aware Authorship Attribution (2025.findings-acl)
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| Challenge: | Existing methods for authorship attribution struggle with trustworthiness and interpretability across domains, languages, and stylistic variations. |
| Approach: | They propose a Bayesian-Enhanced DeBERTa framework that integrates Bayes' reasoning with transformer-based language models to enable uncertainty-aware authorship attribution. |
| Outcome: | The proposed framework achieves 19.69% improvement in F1-score across multiple authorship attribution tasks, including binary, multiclass, and dynamic authorship detection. |
Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage (2026.acl-long)
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| Challenge: | Large language models (LLMs) evolve to autonomous agents synthesizing real-time information, but their reasoning capabilities introduce an unexpected attack surface. |
| Approach: | They propose a framework that constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions. |
| Outcome: | The proposed framework constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions. |
A Lifelong Multilingual Multi-granularity Semantic Alignment Approach via Maximum Co-occurrence Probability (2024.lrec-main)
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| Challenge: | Existing methods to mask and predict tokens in multilingual text limit multilingual interaction . |
| Approach: | They propose a lifelong multilingual multi-granularity semantic alignment approach which continuously extracts massive aligned linguistic units from noisy data via a maximum co-occurrence probability algorithm. |
| Outcome: | The proposed approach improves translation performance on WMT14 18 benchmarks in twelve directions. |
Probing the Uniquely Identifiable Linguistic Patterns of Conversational AI Agents (2024.findings-acl)
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| Challenge: | Recent advances in deep learning and natural language processing have led to the proliferation of conversational AI agents. |
| Approach: | They construct linguistic profiles for five CAAs and use authorship attribution techniques to identify uniquely identifieable linguistic patterns for each model. |
| Outcome: | The proposed model identifies unique identifiers (UILPs) for each CAA using authorship attribution techniques. |
GRADA: Graph-based Reranking against Adversarial Documents Attack (2025.emnlp-main)
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Jingjie Zheng, Aryo Pradipta Gema, Giwon Hong, Xuanli He, Pasquale Minervini, Youcheng Sun, Qiongkai Xu
| Challenge: | Retrieval Augmented Generation (RAG) frameworks are susceptible to adversarial attacks that manipulate the retrieval process by introducing documents that are adversarially similar to the query. |
| Approach: | They propose a framework that integrates external retrieval modules into RAG frameworks to improve the factual accuracy of large language models. |
| Outcome: | The proposed framework reduces adversarial attacks by 80% while maintaining minimal loss in accuracy. |
Multi-Loss Fusion: Angular and Contrastive Integration for Machine-Generated Text Detection (2024.findings-emnlp)
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| Challenge: | Modern natural language generation systems have led to the development of synthetic human-like open-ended texts, posing concerns as to who the original author of a text is. |
| Approach: | They propose a custom DeBERTa model with angular loss and contrastive loss functions for effective class separation in neural text classification tasks. |
| Outcome: | The proposed model improves on binary machine-generated text detection and multi-class neural authorship attribution tasks on a number of benchmark datasets. |