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cs.CL2026
On Meta-Prompting
Adrian de Wynter, Xun Wang, Qilong Gu +1
Modern large language models (LLMs) are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them. Unlike traditional learners, LLMs cann…
cs.CL2026★ 33 cited
An Evaluation on Large Language Model Outputs: Discourse and Memorization
Adrian de Wynter, Xun Wang, Alex Sokolov +2
We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-…
cs.CL2024
In-context Autoencoder for Context Compression in a Large Language Model
Tao Ge, Jing Hu, Lei Wang +3
We propose the In-context Autoencoder (ICAE), leveraging the power of a large language model (LLM) to compress a long context into short compact memory slots that can be directly c…