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Etremely Weakly Supervised Text Classification (XWS-TC) refers to textclassification based on minimal high-level human guidance, such as a fewlabel-indicative seed words or classification instructions. There are twomainstream approaches for XWS-TC, however, never being rigorously compared: (1)training classifiers based on pseudo-labels generated by (softly) matching seedwords (SEED) and (2) prompting (and calibrating) language models usingclassification instruction (and raw texts) to decode label words (PROMPT). Thispaper presents the first XWS-TC benchmark to compare the two approaches on fairgrounds, where the datasets, supervisions, and hyperparameter choices arestandardized across methods. Our benchmarking results suggest that (1) BothSEED and PROMPT approaches are competitive and there is no clear winner; (2)SEED is empirically more tolerant than PROMPT to human guidance (e.g., seedwords, classification instructions, and label words) changes; (3) SEED isempirically more selective than PROMPT to the pre-trained language models; (4)Recent SEED and PROMPT methods have close connections and a clusteringpost-processing step based on raw in-domain texts is a strong performancebooster to both. We hope this benchmark serves as a guideline in selectingXWS-TC methods in different scenarios and stimulate interest in developingguidance- and model-robust XWS-TC methods. We release the repo athttps://github.com/ZihanWangKi/x-TC.

A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches