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cs.CL2024★ 2 cited
Understanding Privacy Risks of Embeddings Induced by Large Language Models
Zhihao Zhu, Ninglu Shao, Defu Lian +4
Large language models (LLMs) show early signs of artificial general intelligence but struggle with hallucinations. One promising solution to mitigate these hallucinations is to sto…
cs.CL2024★ 1 cited
Extensible Embedding: A Flexible Multipler For LLM's Context Length
Ninglu Shao, Shitao Xiao, Zheng Liu +1
Large language models (LLMs) call for extension of context to handle many critical applications. However, the existing approaches are prone to expensive costs and inferior quality…
cs.CL2024
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization
Ninglu Shao, Shitao Xiao, Zheng Liu +1
Large language models (LLMs) are in need of sufficient contexts to handle many critical applications, such as retrieval augmented generation and few-shot learning. However, due to…