Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification
This work addresses the problem of reducing manual annotation effort in stance detection for researchers and practitioners, but it is incremental as it shows LLMs do not consistently outperform smaller supervised models.
The paper tackled stance classification by evaluating 10 open-source large language models (LLMs) with 7 prompting schemes, finding they are competitive with in-domain supervised models but inconsistent in performance, and fine-tuning did not reliably improve results.
Stance classification, the task of predicting the viewpoint of an author on a subject of interest, has long been a focal point of research in domains ranging from social science to machine learning. Current stance detection methods rely predominantly on manual annotation of sentences, followed by training a supervised machine learning model. However, this manual annotation process requires laborious annotation effort, and thus hampers its potential to generalize across different contexts. In this work, we investigate the use of Large Language Models (LLMs) as a stance detection methodology that can reduce or even eliminate the need for manual annotations. We investigate 10 open-source models and 7 prompting schemes, finding that LLMs are competitive with in-domain supervised models but are not necessarily consistent in their performance. We also fine-tuned the LLMs, but discovered that fine-tuning process does not necessarily lead to better performance. In general, we discover that LLMs do not routinely outperform their smaller supervised machine learning models, and thus call for stance detection to be a benchmark for which LLMs also optimize for. The code used in this study is available at \url{https://github.com/ijcruic/LLM-Stance-Labeling}