2023-03-02 · article · Topic Modeling · en
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This paper addresses topic modeling by investigating parameter-efficient fine-tuning of large-scale pre-trained language models, and as a published article, its findings have passed peer review and carry greater weight.
3 cited facts
This work is the product of a small team of 3 researchers.
1 cited fact
With 942 citations, this work is highly cited, serving as an established reference point in its field. It references 38 other works.
3 cited facts
Parameter-efficient fine-tuning of large-scale pre-trained language models is an article, focusing on Topic Modeling, published in 2023.
Parameter-efficient fine-tuning of large-scale pre-trained language models has 942 citations in our record, placing it among the highly cited works we track.
Our record lists 3 authors for Parameter-efficient fine-tuning of large-scale pre-trained language models and references 38 other works. Author names aren't in our data yet — the source link on the page lists the full byline.
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