2021-01-01 · conference-paper · Topic Modeling · en
highly citedClaims drawn from cited facts, not live model generation.
This paper is a conference paper on topic modeling, and as such it has undergone peer review, giving it more weight than an unreviewed preprint. Since it is a published conference paper rather than a preprint, its findings have been vetted by the research community.
3 cited facts
This work is the product of a small team of three researchers.
1 cited fact
This work has received 1281 citations, confirming that it already functions as an established reference point in its field rather than merely an early signal of possible influence. Drawing on 60 references, its citation impact is therefore a settled marker of outsized relevance, not a provisional indicator.
4 cited facts
Making Pre-trained Language Models Better Few-shot Learners is a conference-paper, focusing on Topic Modeling, published in 2021.
Making Pre-trained Language Models Better Few-shot Learners has 1,281 citations in our record, placing it among the highly cited works we track.
Our record lists 3 authors for Making Pre-trained Language Models Better Few-shot Learners and references 60 other works. Author names aren't in our data yet — the source link on the page lists the full byline.
Source facts, citations, and refresh stamp for this record.
Sources: openalex_works