Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models

Sachin Pawar, Nitin Ramrakhiyani, Anubhav Sinha, Manoj Apte, Girish Palshikar


Abstract
In this paper, we propose a novel two-step technique for text classification using autoregressive Language Models (LM). In the first step, a set of perplexity and log-likelihood based numeric features are elicited from an LM for a text instance to be classified. Then, in the second step, a classifier based on these features is trained to predict the final label. The classifier used is usually a simple machine learning classifier like Support Vector Machine (SVM) or Logistic Regression (LR) and it is trained using a small set of training examples. We believe, our technique presents a whole new way of exploiting the available training instances, in addition to the existing ways like fine-tuning LMs or in-context learning. Our approach stands out by eliminating the need for parameter updates in LMs, as required in fine-tuning, and does not impose limitations on the number of training examples faced while building prompts for in-context learning. We evaluate our technique across 5 different datasets and compare with multiple competent baselines.
Anthology ID:
2024.findings-eacl.74
Volume:
Findings of the Association for Computational Linguistics: EACL 2024
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1099–1114
Language:
URL:
https://aclanthology.org/2024.findings-eacl.74
DOI:
Bibkey:
Cite (ACL):
Sachin Pawar, Nitin Ramrakhiyani, Anubhav Sinha, Manoj Apte, and Girish Palshikar. 2024. Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1099–1114, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models (Pawar et al., Findings 2024)
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