# Large Language Models Are Zero-Shot Text Classifiers

arXiv: https://arxiv.org/abs/2312.01044 · HTML: https://mlsanity.com/paper/2312.01044/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 57 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Zero-Shot Learning by Convex Combination of Semantic Embeddings](https://mlsanity.com/paper/1312.5650/) (2013)
- [From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models](https://mlsanity.com/paper/2212.10846/) (2022)
- [Examining Zero-Shot Vulnerability Repair with Large Language Models](https://mlsanity.com/paper/2112.02125/) (2021)
- [Tensor Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2001.05313/) (2020)
- [DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2008.06914/) (2020)
- [Zero-Shot Learning for Requirements Classification: An Exploratory Study](https://mlsanity.com/paper/2302.04723/) (2023)

## Built on by (most cited first, 30 of 35)

- [LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild](https://mlsanity.com/paper/2405.20363/) (2024)
- [Unlocking Memorization in Large Language Models with Dynamic Soft Prompting](https://mlsanity.com/paper/2409.13853/) (2024)
- [Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives](https://mlsanity.com/paper/2507.15783/) (2025)
- [Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features](https://mlsanity.com/paper/2412.00142/) (2024)
- [Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Research](https://mlsanity.com/paper/2404.05213/) (2024)
- [DiffGuard: Text-Based Safety Checker for Diffusion Models](https://mlsanity.com/paper/2412.00064/) (2024)
- [Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2502.13044/) (2025)
- [Adaptable and Reliable Text Classification using Large Language Models](https://mlsanity.com/paper/2405.10523/) (2024)
- [MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation](https://mlsanity.com/paper/2410.02458/) (2024)
- [Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors](https://mlsanity.com/paper/2505.09949/) (2025)
- [Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning](https://mlsanity.com/paper/2408.09914/) (2024)
- [DeepDiveAI: Identifying AI Related Documents in Large Scale Literature Data](https://mlsanity.com/paper/2408.12871/) (2024)
- [Structure-Augmented Reasoning Generation](https://mlsanity.com/paper/2506.08364/) (2025)
- [LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data](https://mlsanity.com/paper/2508.09263/) (2025)
- [Neural Models and Language Model Prompting for the Multidimensional Evaluation of Open-Ended Conversations](https://mlsanity.com/paper/2509.00841/) (2025)
- [Predicting Language Models' Success at Zero-Shot Probabilistic Prediction](https://mlsanity.com/paper/2509.15356/) (2025)
- [LLM Based Bayesian Optimization for Prompt Search](https://mlsanity.com/paper/2510.04384/) (2025)
- [Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection](https://mlsanity.com/paper/2511.21066/) (2025)
- [Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis](https://mlsanity.com/paper/2502.03843/) (2025)
- [Class Distillation with Mahalanobis Contrast: An Efficient Training Paradigm for Pragmatic Language Understanding Tasks](https://mlsanity.com/paper/2505.11829/) (2025)
- [Hierarchical Text Classification Using Black Box Large Language Models](https://mlsanity.com/paper/2508.04219/) (2025)
- [LabelFusion: Fusing Large Language Models with Transformer Encoders for Robust Financial News Classification](https://mlsanity.com/paper/2512.10793/) (2025)
- [Multilingual Reference Need Assessment System for Wikipedia](https://mlsanity.com/paper/2603.17146/) (2026)
- [Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes](https://mlsanity.com/paper/2608.02415/) (2026)
- [LLMs as Feature Engineers for Text-and-Tabular Prediction](https://mlsanity.com/paper/2609.21894/) (2026)
- [Improving Multi-turn Task Completion in Task-Oriented Dialog Systems via Prompt Chaining and Fine-Grained Feedback](https://mlsanity.com/paper/2502.13298/) (2025)
- [Extreme Speech Classification in the Era of LLMs: Exploring Open-Source and Proprietary Models](https://mlsanity.com/paper/2502.15155/) (2025)
- [Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs](https://mlsanity.com/paper/2503.16655/) (2025)
- [Online Anti-sexist Speech: Identifying Resistance to Gender Bias in Political Discourse](https://mlsanity.com/paper/2508.11434/) (2025)
- [AI-Powered Early Diagnosis of Mental Health Disorders from Real-World Clinical Conversations](https://mlsanity.com/paper/2510.14937/) (2025)

## Abstract (arXiv)

Retrained large language models (LLMs) have become extensively used across various sub-disciplines of natural language processing (NLP). In NLP, text classification problems have garnered considerable focus, but still faced with some limitations related to expensive computational cost, time consumption, and robust performance to unseen classes. With the proposal of chain of thought prompting (CoT), LLMs can be implemented using zero-shot learning (ZSL) with the step by step reasoning prompts, instead of conventional question and answer formats. The zero-shot LLMs in the text classification problems can alleviate these limitations by directly utilizing pretrained models to predict both seen and unseen classes. Our research primarily validates the capability of GPT models in text classification. We focus on effectively utilizing prompt strategies to various text classification scenarios. Besides, we compare the performance of zero shot LLMs with other state of the art text classification methods, including traditional machine learning methods, deep learning methods, and ZSL methods. Experimental results demonstrate that the performance of LLMs underscores their effectiveness as zero-shot text classifiers in three of the four datasets analyzed. The proficiency is especially advantageous for small businesses or teams that may not have extensive knowledge in text classification.