Zero-Shot Text Classification

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

28 shown of 28 papers with code (48 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Making Pre-trained Language Models Better Few-shot Learners
  31 Dec 2020 9 repositories listed Syntology ran 2 of 9 samples · 7 unverified · 7 pointer-only (licence)
  We present LM-BFF--better few-shot fine-tuning of language models--a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples.
• Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
  31 Aug 2019 4 repositories listed Syntology ran 2 of 6 samples · 4 unverified · 3 pointer-only (licence)
  0Shot-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e.
• Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches
  29 Nov 2022 2 repositories listed
  Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches.
• Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
  29 May 2022 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.
• Text Classification Using Label Names Only: A Language Model Self-Training Approach
  14 Oct 2020 2 repositories listed
  In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents.
• Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
  29 Mar 2019 2 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification.
• Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token Prediction
  4 Apr 2025 1 repository listed
  Zero-shot text classification typically relies on prompt engineering, but the inherent prompt brittleness of large language models undermines its reliability.
• Analysis of Socially Unacceptable Discourse with Zero-shot Learning
  10 Sep 2024 1 repository listed
  Socially Unacceptable Discourse (SUD) analysis is crucial for maintaining online positive environments.
• Retrieval Augmented Zero-Shot Text Classification
  21 Jun 2024 1 repository listed
  A simple approach often relies on comparing embeddings of query (text) to those of potential classes.
• Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
  30 Jan 2024 1 repository listed
  We show that a linear transformation of the text representation from any transformer model using the task-specific concept operator results in a projection onto the latent concept space, referred to as context…
• An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph
  4 Dec 2023 1 repository listed
  In this paper, we present a methodology for creating a benchmark dataset of news headlines mapped to event classes in Wikidata, and resources for the evaluation of methods that perform the mapping.
• This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models
  24 Oct 2023 1 repository listed
  We have used our dataset with the largest available open LLMs in a zero-shot approach to grasp their generalization and inference capability and we have also fine-tuned some of the models to assess whether the…
• WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories
  28 Jul 2023 1 repository listed
  To achieve this objective, we propose a novel self-training strategy that uses labels rather than text for training, significantly reducing the model's training time.
• Label Agnostic Pre-training for Zero-shot Text Classification
  25 May 2023 1 repository listed
  In addition, depending on the aspect (sentiment, topic, etc.)
• Zero-Shot Text Classification via Self-Supervised Tuning
  19 May 2023 1 repository listed
  In this work, we propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks by tuning the language models with unlabeled data, called self-supervised tuning.
• ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
  18 May 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks.
• The Benefits of Label-Description Training for Zero-Shot Text Classification
  3 May 2023 1 repository listed
  Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data in order to classify among specific label sets in downstream tasks.
• Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM
  24 Apr 2023 1 repository listed
  To overcome these limitations, we introduce a novel method, namely GenCo, which leverages the strong generative power of LLMs to assist in training a smaller and more adaptable language model.
• Zero-Shot Text Classification with Self-Training
  31 Oct 2022 1 repository listed
  Recent advances in large pretrained language models have increased attention to zero-shot text classification.
• Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
  29 Oct 2022 1 repository listed
  We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes.
• Hypothesis Engineering for Zero-Shot Hate Speech Detection
  3 Oct 2022 1 repository listed
  We find that the zero-shot baseline used for the initial error analysis already outperforms commercial systems and fine-tuned BERT-based hate speech detection models on HateCheck.
• What Makes Pre-trained Language Models Better Zero-shot Learners?
  30 Sep 2022 1 repository listed
  Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori.
• Learn to Adapt for Generalized Zero-Shot Text Classification
  1 May 2022 1 repository listed
  Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes.
• Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification
  11 Feb 2022 1 repository listed
  Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set.
• Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
  9 Feb 2022 1 repository listed
  Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e.
• NSP-BERT: A Prompt-based Few-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction
  8 Sep 2021 1 repository listed
  Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune…
• Issues with Entailment-based Zero-shot Text Classification
  1 Aug 2021 1 repository listed
  The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be…
• Unsupervised Label Refinement Improves Dataless Text Classification
  8 Dec 2020 1 repository listed
  This reliance causes dataless classifiers to be highly sensitive to the choice of label descriptions and hinders the broader application of dataless classification in practice.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.