Alexander Knox at S em E val-2023 Task 5: The comparison of prompting and standard fine-tuning techniques for selecting the type of spoiler needed to neutralize a clickbait (2023)

Abstract: Clickbait posts are a common problem on social media platforms, as they often deceive users by providing misleading or sensational headlines that do not match the content of the linked web page. The aim of this study is to create a technique for identifying the specific type of suitable spoiler - be it a phrase, a passage, or a multipart spoiler - needed to neutralize clickbait posts. This is achieved by developing a machine learning classifier analyzing both the clickbait post and the linked web page. Modern approaches for constructing a text classifier usually rely on fine-tuning a transformer-based model pre-trained on large unsupervised corpora. However, recent advances in the development of large-scale language models have led to the emergence of a new transfer learning paradigm based on prompt engineering. In this work, we study these two transfer learning techniques and compare their effectiveness for clickbait spoiler-type detection task. Our experimental results show that for this task, using the standard fine-tuning method gives better results than using prompting. The best model can achieve a similar performance to that presented by Hagen et al. (2022).

## introduction 1.problem description clickbaits are social media posts designed to grab the reader's attention in order to visit a linked web page. as they are used by publishers to increase the number of views, clickbait posts are becoming more and more common in the webspace. even though some clickbait posts are harmless, a significant amount of them is misleading and help spread fake news. therefore, some research works have been devoted to clickbait detection problem @xcite , leading e.g. to the proposal of an additional plug-in for the browser to alert the user of a clickbait header @xcite . however, clickbait detection alone is not enough, as it only identifies the problem, but does not solve it. therefore, @xcite proposed using automatically generated spoilers to overcome the issue of clickbait posts. spoilers are short texts that provide the most necessary information from the web page linked to the clickbait, completely neutralizing the clickbait post. three types of spoilers are put forward @xcite : ## spoiler-type classification using fine-tuning as a baseline, standard fine-tuning of the pretrained mlm model was used. fine-tuning typically requires a larger training dataset (in comparison to few-shots only), but also tends to be a more robust approach, since it allows the model to learn from many examples and adapt to the task at hand. ## experimental evaluation due to the fact that the test dataset for subtask 1 at semeval 2023 task 5 @xcite has not been made publicly available, the following experiments use the provided validation examples as the test dataset and as validation dataset a part of training data was used. as an llm, we used bloom (scao et al., 2022) since it is: for standard fine-tuning, mlm models: roberta @xcite , distilbert @xcite , and albert @xcite with a softmax layer attached to the first token were used as classifiers. each model was fine-tuned with 7 learning epochs only, since it was observed that increasing the number of epochs did not result in higher performance on the validation set. the transformers foot_1 library provided by huggingface company was used to implement the model. ## results on the semeval-2023 test set this section will present the model results obtained for the test dataset for subtask 1 at semeval 2023 task 5. the results were generated using tira @xcite . the results obtained on the test dataset for the selected model are presented in