GPTs at Factify 2022: Prompt Aided Fact-Verification

TL;DR

This paper explores prompt-based and PLM-based methods for fact verification to combat false news, achieving competitive results on the FACTIFY dataset and demonstrating the effectiveness of prompt tuning over traditional fine-tuning.

Contribution

It introduces a prompt-based approach for fact verification that enhances PLM performance and outperforms standard fine-tuning methods.

Findings

Achieved an F1 score of 0.6946 on FACTIFY dataset.

Prompt-based tuning outperforms traditional fine-tuning.

Secured 7th position in the competition leaderboard.

Abstract

One of the most pressing societal issues is the fight against false news. The false claims, as difficult as they are to expose, create a lot of damage. To tackle the problem, fact verification becomes crucial and thus has been a topic of interest among diverse research communities. Using only the textual form of data we propose our solution to the problem and achieve competitive results with other approaches. We present our solution based on two approaches - PLM (pre-trained language model) based method and Prompt based method. The PLM-based approach uses the traditional supervised learning, where the model is trained to take 'x' as input and output prediction 'y' as P(y|x). Whereas, Prompt-based learning reflects the idea to design input to fit the model such that the original objective may be re-framed as a problem of (masked) language modeling. We may further stimulate the rich knowledge provided by PLMs to better serve downstream tasks by employing extra prompts to fine-tune PLMs. Our experiments showed that the proposed method performs better than just fine-tuning PLMs. We achieved an F1 score of 0.6946 on the FACTIFY dataset and a 7th position on the competition leader-board.