Using an ML-predicted label count to constrain LLM generation and post-editing outputs to the LCSH vocabulary lifts subject-heading prediction F1 from 0.135 to 0.300 on a 2,100-book test set.
Transformer models: an introduction and catalog
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In the past few years we have seen the meteoric appearance of dozens of foundation models of the Transformer family, all of which have memorable and sometimes funny, but not self-explanatory, names. The goal of this paper is to offer a somewhat comprehensive but simple catalog and classification of the most popular Transformer models. The paper also includes an introduction to the most important aspects and innovations in Transformer models. Our catalog will include models that are trained using self-supervised learning (e.g., BERT or GPT3) as well as those that are further trained using a human-in-the-loop (e.g. the InstructGPT model used by ChatGPT).
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
A Hybrid Framework for Subject Analysis: Integrating Embedding-Based Regression Models with Large Language Models
Using an ML-predicted label count to constrain LLM generation and post-editing outputs to the LCSH vocabulary lifts subject-heading prediction F1 from 0.135 to 0.300 on a 2,100-book test set.