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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…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
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…