Papers with zero-shot methods
Meta-Learning a Cross-lingual Manifold for Semantic Parsing (2023.tacl-1)
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| Challenge: | Recent work has found success with machine translation or zero-shot methods . however, these approaches can struggle to model how native speakers ask questions . |
| Approach: | They propose a meta-learning algorithm to leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. |
| Outcome: | The proposed approach trains a parser with maximum sample efficiency in six languages on ATIS. |
Z3D: Zero-Shot 3D Visual Grounding from Images (2026.acl-short)
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Nikita Drozdov, Andrey Lemeshko, Nikita Gavrilov, Anton Konushin, Danila Rukhovich, Maksim Kolodiazhnyi
| Challenge: | 3D visual grounding (3DVG) aims to localize objects in a 3D scene based on natural language queries. |
| Approach: | They propose a zero-shot 3D visual grounding pipeline that operates on multi-view images without geometric supervision and without object priors. |
| Outcome: | Experiments on ScanRefer and Nr3D show that the proposed method outperforms existing methods. |
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA (2024.naacl-long)
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| Challenge: | Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. |
| Approach: | They propose a framework to explicitly utilize the massive knowledge encoded in LLM parameters and their strong instruction understanding abilities. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on three widely-used ODQA datasets and achieves comparable performance with customized fine-tuned models on full training data. |
Strong Heuristics for Named Entity Linking (2022.naacl-srw)
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| Challenge: | Named entity linking (NEL) is a challenging task due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods. |
| Approach: | They propose to map speaker-attributed quotes to a unique identifier in a referent knowledge base and then use it to resolve the ambiguity. |
| Outcome: | The proposed method disambiguates 94% and 63% of the mentions on Quotebank and the AIDA-CoNLL benchmark, respectively. |
PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale (2023.findings-emnlp)
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| Challenge: | Existing question answering systems rely on large, high-quality training data. |
| Approach: | They propose a synthetic data generation method which decomposes cross-lingual QA into two stages . they apply a question generation model to the English side and annotation projection to translate both questions and answers. |
| Outcome: | The proposed method outperforms existing methods on cross-lingual QA datasets. |
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding (2023.findings-acl)
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| Challenge: | supervised methods for vision-language tasks have been well-studied, but they lack the fine-grained information needed for semantics understanding. |
| Approach: | They propose a framework to take advantage of fine-grained information for zero-shot vision-language learning, covering multiple tasks such as VQA, SNLI-VE, and VCR. |
| Outcome: | The proposed framework outperforms previous zero-shot methods on VQA and achieves substantial improvement on SNLI-VE and VCR. |
Devil’s Advocate: Anticipatory Reflection for LLM Agents (2024.findings-emnlp)
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| Challenge: | Introspection-driven approach equips LLM agents with introspection, enhancing consistency and adaptability in solving complex tasks. |
| Approach: | They propose a zero-shot approach that equips LLM agents with introspection, enhancing consistency and adaptability in solving complex tasks. |
| Outcome: | The proposed approach improves performance and efficiency by reducing the number of trials and plan revisions by 45%. |
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing (2023.tacl-1)
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| Challenge: | Existing work on cross-lingual semantic parsing has focused on English . a few-shot approach to parse from natural languages is comparatively unexplored . |
| Approach: | They propose a method that minimizes cross-lingual divergence between probabilistic latent variables by Optimal Transport. |
| Outcome: | The proposed method improves performance even without parallel input translations on two datasets. |
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations (2023.acl-long)
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| Challenge: | Existing methods for zero-shot learning are based on in-context training, but performance drops when no demonstrations are available. |
| Approach: | They propose a new method that constructs pseudo-demonstrations for a given test input using a raw text corpus and applies techniques to reduce copying. |
| Outcome: | The proposed method outperforms previous zero-shot methods on nine classification datasets and is on par with in-context learning with labeled training data in the few-shot setting. |
In-Context Learning for Few-Shot Dialogue State Tracking (2022.findings-emnlp)
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| Challenge: | Existing methods for zero-shot and few-shot learning dialogue state tracking are hard and expensive. |
| Approach: | They propose an in-context learning framework for zero-shot and few-shot learning dialogue state tracking (DST) a large pretrained language model takes a test instance and a few exemplars as input and directly decodes the dialogue state . |
| Outcome: | The proposed framework outperforms state-of-the-art models in few-shot settings . it is flexible and scalable, and requires less data to adapt to new domains and scenarios . |
Low-Cost Generation and Evaluation of Dictionary Example Sentences (2024.naacl-long)
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| Challenge: | Prior studies have shown that language models can be trained to generate example sentences, but they relied on costly customized models and word sense datasets for generation and evaluation. |
| Approach: | They propose a new automatic evaluation metric called OxfordEval that measures the win-rate of generated sentences against existing Oxford Dictionary sentences. |
| Outcome: | The proposed model achieves over 85.1% win rate against baseline sentences compared to 39.8% win rate for prior model-generated sentences. |
RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models (2025.findings-acl)
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| Challenge: | Object navigation is a fundamental task in embodied artificial intelligence. |
| Approach: | They propose a region-aware Termination-Enhanced method that incorporates visual language models and exploration rates to enable efficient termination. |
| Outcome: | The proposed method achieves a success rate of 67.8% and an SPL of 31.3% on the HM3D dataset. |
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search (2020.emnlp-main)
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| Challenge: | Existing search methods for COVID-19 are not based on scientific data, but use a neural re-ranking model pre-trained on scientific text. |
| Approach: | They propose a zero-shot ranking algorithm that adapts to COVID-related scientific literature . they use a neural re-ranking model pre-trained on scientific text and filters the target document . |
| Outcome: | The proposed algorithm outperforms models on the TREC COVID Round 1 leaderboard . it outperformed models that do not rely on TREC-COVID data . |
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces (D18-1)
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| Challenge: | Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group) and labels that never appear in the training dataset (zero-shot groups). |
| Approach: | They perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. |
| Outcome: | The proposed methods improve on two publicly available datasets for multi-label text classification. |
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)
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| Challenge: | Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data . |
| Approach: | They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space. |
| Outcome: | The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines. |
CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization (2024.lrec-main)
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| Challenge: | Cross-lingual summarization (CLS) has attracted increasing interest due to the availability of large-scale web-mined datasets and the advancements of multilingual language models. |
| Approach: | They propose a dataset of cross-lingual code-switched summaries in Chinese and English . they show that leveraging existing CLS resources does not improve performance . |
| Outcome: | The proposed method does not improve on CroCoSum, indicating the limited generalizability of existing approaches. |
Distribution Shift Alignment Helps LLMs Simulate Survey Response Distributions (2026.findings-acl)
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| Challenge: | Existing methods to simulate survey responses are based on zero-shot methods, but they are sensitive to prompt changes and deviate from the real-world distributions. |
| Approach: | They propose a distribution shift alignment method that aligns both the output distributions and the distribution shifts across different backgrounds to provide results closer to the true distribution than the training data. |
| Outcome: | The proposed method outperforms zero-shot methods on five public survey datasets and reduces the required real data by 53.48-69.12%. |
Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations (2022.emnlp-main)
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| Challenge: | Existing methods for zero-shot text classification involve heavy human engineering or complicated self-training pipelines. |
| Approach: | They propose to fit unlabeled text with a Bayesian Gaussian Mixture Model and use class names to cluster them. |
| Outcome: | The proposed approach outperforms prompt-based methods on topic and sentiment datasets and outperformed previous studies significantly on unbalanced datasets. |
Zero-shot Visual Question Answering with Language Model Feedback (2023.findings-acl)
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| Challenge: | Existing methods for knowledge-based visual question answering are based on pre-trained language models. |
| Approach: | They propose a language model guided captioning approach that leverages a pre-trained language model to generate captions for an image to help answer a visual question. |
| Outcome: | The proposed method outperforms several competing methods on the knowledge-based VQA task and achieves comparable results to a fine-tuned VLP model. |
DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models (2025.naacl-long)
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| Challenge: | Existing zero-shot methods for Question Answering (QA) are efficient but fail to consider context and prevent bias propagation in the answers. |
| Approach: | They propose a method for debiasing Large Language Models using context-adaptive prompt generation that takes appropriate debiased actions based on the context and aNeutral Answer Guidance Generation to suppress the LLMs make objective judgments about the context. |
| Outcome: | The proposed method achieves state-of-the-art zero-shot debiased QA performance across eight LLMs. |
CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision (2022.emnlp-main)
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| Challenge: | Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers . previous attempts failed to scale up due to heavy human annotation and domain expertise . |
| Approach: | They propose a method to automatically extract TLDR summaries from scientific papers . they propose 'citeSum' with no human annotation, which is 30 times larger than SciTLDR . |
| Outcome: | The proposed approach outperforms most fully-supervised methods on SciTLDR without fine-tuning and achieves state-of-the-art results with only 128 examples. |
ZeroNER: Fueling Zero-Shot Named Entity Recognition via Entity Type Descriptions (2025.findings-acl)
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Alessio Cocchieri, Marcos Martínez Galindo, Giacomo Frisoni, Gianluca Moro, Claudio Sartori, Giuseppe Tagliavini
| Challenge: | Existing zero-shot learning methods rely on entity type names for generalization . current solutions require large datasets and prioritize a handful of commonly occurring types . |
| Approach: | They propose a description-driven framework that enhances hard zero-shot NER in low-resource settings. |
| Outcome: | The proposed framework outperforms existing models by up to 16% in the F1 score . it also surpasses baseline models that use type names alone . |
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text (2023.findings-emnlp)
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| Challenge: | Large language models generate huge amounts of text, making it impractical to manually distinguish whether a text is machine-generated. |
| Approach: | They propose two methods to detect machine-generated text by leveraging Log-Rank information and propose a faster method that uses less perturbations to achieve the same level of performance. |
| Outcome: | The proposed methods improve over the state of the art by 3.9 and 1.75 AUROC points absolute and require less perturbations to achieve the same level of performance. |
Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of Pre-trained Multimodal Representation via Text Updates (2025.acl-long)
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| Challenge: | Recent advances in multimodal systems have demonstrated remarkable capabilities in generating multimodal content from multimodal inputs. |
| Approach: | They propose a benchmark that leverages large language models to generate deceptive text samples to exploit compositional vulnerabilities across different modalities. |
| Outcome: | The proposed approach exploits compositional vulnerabilities across images, videos, and audios. |
LCES: Zero-shot Automated Essay Scoring via Pairwise Comparisons Using Large Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to automate essay scoring rely on LLMs to generate absolute scores . however, these methods diverge from human evaluations due to model biases and inconsistent scoring . |
| Approach: | They propose a method that formulates AES as a pairwise comparison task using large language models. |
| Outcome: | The proposed method outperforms conventional zero-shot methods in accuracy while maintaining computational efficiency. |
Incomplete In-context Learning (2026.acl-long)
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Wenqiang Wang, Wen Yujia, Yan Xiao, Zhifeng Chen, Yangshijie Zhang, Peng Chen, Mingbo Yang, Xiaochun Cao
| Challenge: | Existing in-context learning assumes the retrieval dataset contains demonstrations for all output label spaces. |
| Approach: | They propose a framework with train-free and train-based variants to address IICL . they propose to integrate a dataset with labeled demonstrations for each output space . |
| Outcome: | The proposed framework outperforms existing methods under incomplete retrieval datasets and even outperformed ICL with complete labels. |