Zero-shot vs. Fine-tuned LLMs in Political Ideology Classification Across Textual Corpora

Abstract

Abstract: Large language models (LLMs) such as GPT-4o and Claude Sonnet 4.5 have demonstrated strong capabilities in open-ended reasoning and generative language tasks, leading to their widespread adoption across a broad range of NLP applications. However, for structured text classification problems with fixed label spaces, model selection is often driven by predictive performance alone, overlooking operational constraints encountered in production systems. In this work, we present a systematic comparison of two contrasting paradigms for text classification: zero- and few-shot prompt-based large langu

Research Question

How do zero-shot LLMs compare to fine-tuned models in accurately classifying political ideology across diverse textual corpora (e.g., news articles, social media) when evaluated on benchmarks like MNLI or HANS?

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