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cs.CL2024
Deep Learning Based Dense Retrieval: A Comparative Study
Ming Zhong, Zhizhi Wu, Nanako Honda
Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but their robustness against tokenizer poisoning remains underexplored. In this…
cs.CL2024★ 3 cited
Why Does the Effective Context Length of LLMs Fall Short?
Chenxin An, Jun Zhang, Ming Zhong +5
Advancements in distributed training and efficient attention mechanisms have significantly expanded the context window sizes of large language models (LLMs). However, recent work r…