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cs.CL2025
Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration
Sibo Xiao, Zixin Lin, Wenyang Gao +2
Processing long contexts has become a critical capability for modern large language models (LLMs). Existing works leverage agent-based divide-and-conquer methods for processing lon…
cs.CL2023
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue +13
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outpu…
cs.CL2021★ 2 cited
RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models
Bill Yuchen Lin, Wenyang Gao, Jun Yan +2
To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples. Specifically, at the ent…