1 citations · 2 across the 9 of their papers we have counts for
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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…