Papers with zero-shot models
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)
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| Challenge: | Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data. |
| Approach: | They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions . |
| Outcome: | The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data. |
BigTokDetect: A Clinically-Informed Vision–Language Modeling Framework for Detecting Pro-Bigorexia Videos on TikTok (2026.eacl-long)
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Minh Duc Chu, Kshitij Pawar, Zihao He, Roxanna Sharifi, Ross M. Sonnenblick, Magdalayna Curry, Laura DAdamo, Lindsay Young, Stuart Murray, Kristina Lerman
| Challenge: | Social media platforms face escalating challenges in detecting harmful content that promotes muscle dysmorphic behaviors and cognitions (bigorexia). |
| Approach: | They propose a framework for detecting pro-bigorexia content on TikTok using an expert-annotated multimodal benchmark dataset of over 2,200 Tiktok videos labeled by clinical psychiatrists. |
| Outcome: | The proposed framework improves on fine-grained subcategories while commercial models achieve the highest accuracy on primary categories. |
Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark (2026.eacl-long)
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| Challenge: | This paper presents a novel task of extracting low-resourced and noisy Latin fragments from mixed-language historical documents with varied layouts. |
| Approach: | They propose to extract low-resourced and noisy Latin fragments from mixed-language historical documents with varied layouts using a multimodal dataset. |
| Outcome: | The proposed model lacks a functional comprehension of Latin, but reliable detection is achievable with zero-shot models. |
VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation (2026.eacl-long)
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| Challenge: | Existing approaches to machine translation (MT) systems degrade when faced with code-mixed text. |
| Approach: | They propose a system that can augment Vietnamese-English code-mixed text with iterative fine-tuning and targeted filtering. |
| Outcome: | The proposed framework outperforms strong back-translation baselines and improves zero-shot models by up to +11.9 points. |
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
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| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
| Approach: | They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages. |
| Outcome: | The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases. |
Zero-Shot Data Maps. Efficient Dataset Cartography Without Model Training (2023.findings-emnlp)
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| Challenge: | Existing methods to diagnose large annotated datasets require the fitting of a strong model to the dataset. |
| Approach: | They propose a new approach to compute confidence and variability over an ensemble of zero-shot models constructed with different but semantically equivalent label descriptions. |
| Outcome: | The proposed method can be used to diagnose large annotated datasets with accuracy up to 14x faster than the current method. |
Description Boosting for Zero-Shot Entity and Relation Classification (2024.findings-acl)
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Gabriele Picco, Leopold Fuchs, Marcos Martínez Galindo, Alberto Purpura, Vanessa López, Hoang Thanh Lam
| Challenge: | Named Entity Recognition and Relation Extraction (RE) methods are expensive and require domain experts for data acquisition and labeling. |
| Approach: | They propose a strategy for generating variations of an initial description, a heuristic for ranking them and an ensemble method capable of boosting the predictions of zero-shot models. |
| Outcome: | The proposed method outperforms existing approaches and achieves new SOTA results on four different entity and relation classification datasets. |
Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation (2025.findings-acl)
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| Challenge: | Sarcasm is a complex form of sentiment expression widely used in human daily life. |
| Approach: | They propose a device-aware sarcasm dataset with counterfactually augmented data to capture its complexity. |
| Outcome: | The proposed dataset shows that it is more balanced than zero-shot models. |
ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation (2024.findings-acl)
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| Challenge: | Until recently, zero-shot stance detection was limited to in-domain tasks. |
| Approach: | They propose a method for stance detection that trains a model that can generalize well to unseen targets across multiple domains. |
| Outcome: | The proposed method generalizes well to unseen targets across multiple domains over baselines on most benchmarks. |
Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks (2024.lrec-main)
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| Challenge: | a recent study focused on intrinsic evaluation, which assesses the quality of summaries, e.g. coherence, fluency, and informativeness, but it focused on task-based extrinsic evaluation to determine the usefulness of summarizations. |
| Approach: | They incorporate three downstream tasks to measure the usefulness of summaries . they find that fine-tuned models produce more useful summary across all three tasks . |
| Outcome: | The proposed model produces more useful summaries across all three tasks compared to zero-shot models . human evaluation provides more reliable performance assessment compared with automatic methods . |
Pushing the Limits of Zero-shot End-to-End Speech Translation (2024.findings-acl)
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| Challenge: | Existing approaches to end-to-end Speech Translation (ST) systems require limited data, which can cause data scarcity and performance degradation. |
| Approach: | They propose a method for zero-shot ST that bridges the modality gap without any paired ST data. |
| Outcome: | The proposed method bridges the modality gap without any paired ST data on a speech encoder and on MT models. |
Predicting Language Models’ Success at Zero-Shot Probabilistic Prediction (2025.findings-emnlp)
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Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño, Ananya Joshi, Ruiqi Lyu, Jingjing Tang, Alistair Turcan, Khurram Yamin, Steven Wu, Bryan Wilder
| Challenge: | Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics. |
| Approach: | They conduct a large-scale empirical study of large language models’ zero-shot predictive capabilities across a wide range of tabular prediction tasks. |
| Outcome: | The results show that LLMs perform well on the base prediction task, and when they perform well, they are more likely to provide high-quality predictions. |
Multilingual Simplification of Medical Texts (2023.emnlp-main)
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Sebastian Joseph, Kathryn Kazanas, Keziah Reina, Vishnesh Ramanathan, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Existing work on medical text simplification has focused on monolingual settings . important findings in medicine are typically presented in technical, jargon-laden language . text simulating models can generate viable simplified texts, but there are outstanding challenges . |
| Approach: | They propose a dataset for medical text simplification in four languages . they evaluate fine-tuned and zero-shot models across these languages based on human assessments and analyses . |
| Outcome: | The proposed dataset evaluates models in English, Spanish, French, and Farsi . it shows that the models can generate viable simplified texts, but there are challenges . |