1 citations · 2 across the 6 of their papers we have counts for
8 papers
Forging LLM Authorship Fingerprints with Targeted Rewriting
Haohan Yuan, Simin Chen, Xi Niu +3
Model-attribution classifiers can often identify which language model produced a text, making model-specific writing patterns a signal of provenance. Accurate attribution on unmodi…
GenCoord: Executable Commitments for Embodied LLM Agents under Private Information
Peng He, Junning Zhu, Haohan Yuan +1
Embodied language-model agents must coordinate interdependent actions while each holds private task information. In dialogue-based coordination, models generate messages and interp…
Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots
Yizhu Wen, Nan Zhang, Haohan Yuan +3
Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers. This enables Generative Engin…
Understanding LLM Reasoning for Abstractive Summarization
Haohan Yuan, Haopeng Zhang
Reasoning has substantially improved Large Language Models (LLMs) on analytical tasks such as mathematics and code generation, but its value for abstractive summarization remains u…
StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs
Haohan Yuan, Sukhwa Hong, Haopeng Zhang
Large language models (LLMs) have shown strong performance in zero-shot summarization, but often struggle to model document structure and identify salient information in long texts…
Wi-Chat: Large Language Model Powered Wi-Fi Sensing
Haopeng Zhang, Yili Ren, Haohan Yuan +2
Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks. However, their potential to integrate physical model knowledge f…
DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization
Haohan Yuan, Haopeng Zhang
Most research on abstractive summarization focuses on single-domain applications, often neglecting how domain shifts between documents affect performance and the generalization abi…
A Structure-aware Generative Model for Biomedical Event Extraction
Haohan Yuan, Siu Cheung Hui, Haopeng Zhang
Biomedical Event Extraction (BEE) is a challenging task that involves modeling complex relationships between fine-grained entities in biomedical text. BEE has traditionally been fo…