Zero-shot Entity and Tweet Characterization with Designed Conditional Prompts and Contexts

TL;DR

This paper explores zero-shot entity and tweet characterization using GPT-2 with designed prompts and contexts, demonstrating effective subjective classification without extensive task-specific training.

Contribution

It introduces a novel approach of using human psychology-inspired prompts with GPT-2 for zero-shot characterization of entities and tweets, validated through experiments.

Findings

Positive results in entity characterization across measures

Effective tweet characterization with prompts and contexts

Human evaluation confirms the approach's validity

Abstract

Online news and social media have been the de facto mediums to disseminate information globally from the beginning of the last decade. However, bias in content and purpose of intentions are not regulated, and managing bias is the responsibility of content consumers. In this regard, understanding the stances and biases of news sources towards specific entities becomes important. To address this problem, we use pretrained language models, which have been shown to bring about good results with no task-specific training or few-shot training. In this work, we approach the problem of characterizing Named Entities and Tweets as an open-ended text classification and open-ended fact probing problem.We evaluate the zero-shot language model capabilities of Generative Pretrained Transformer 2 (GPT-2) to characterize Entities and Tweets subjectively with human psychology-inspired and logical conditional prefixes and contexts. First, we fine-tune the GPT-2 model on a sufficiently large news corpus and evaluate subjective characterization of popular entities in the corpus by priming with prefixes. Second, we fine-tune GPT-2 with a Tweets corpus from a few popular hashtags and evaluate characterizing tweets by priming the language model with prefixes, questions, and contextual synopsis prompts. Entity characterization results were positive across measures and human evaluation.