Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structured Pruning of Large Language Models   Large language models have recently achieved state of the art performance
across a wide variety of natural language tasks. Meanwhile, the size of these
models and their latency have significantly increased, which makes their usage
costly, and raises an interesting question: do language models need to be
large? We study this question through the lens of model compression. We present
a generic, structured pruning approach by parameterizing each weight matrix
using its low-rank factorization, and adaptively removing rank-1 components
during training. On language modeling tasks, our structured approach
outperforms other unstructured and block-structured pruning baselines at
various compression levels, while achieving significant speedups during both
training and inference. We also demonstrate that our method can be applied to
pruning adaptive word embeddings in large language models, and to pruning the
BERT model on several downstream fine-tuning classification benchmarks.

15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ