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20232026
most citedWhat Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation

2 citations · 4 across the 19 of their papers we have counts for

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cs.CL2023

Improving Input-label Mapping with Demonstration Replay for In-context Learning

Zhuocheng Gong, Jiahao Liu, Qifan Wang +4

In-context learning (ICL) is an emerging capability of large autoregressive language models where a few input-label demonstrations are appended to the input to enhance the model's…

cs.CL2023

Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression

Jiduan Liu, Jiahao Liu, Qifan Wang +5

Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these mode…