| Challenge: | Using a zero-shot prompting, large language models can be used to share images in a multi-tasking environment. |
| Approach: | They introduce a dataset that includes enriched annotations and a framework to evaluate LLMs. |
| Outcome: | The proposed framework unlocks image-sharing capability of LLMs in zero-shot prompting, with ChatGPT achieving the best performance. |
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Revisiting Large Language Models as Zero-shot Relation Extractors (2023.findings-emnlp)
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| Challenge: | Recent studies show that large language models (LLMs) transfer well to new tasks out-of-the-box . relationship extraction (RE) involves a certain degree of labeled or unlabeled data even under zero-shot setting. |
| Approach: | They propose a simple prompt recursively using LLMs to transform RE inputs to QA format . they propose qq prompting and qt prompting to improve their results . |
| Outcome: | The proposed method improves on different model sizes, benchmarks and settings. |
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks (2023.findings-acl)
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| Challenge: | Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. |
| Approach: | They propose to use open-source, open-access language models to make visual input accessible to the model using separate verbalisation models. |
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ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are becoming general-purpose APIs, requiring visual knowledge to be understood. |
| Approach: | They propose to evaluate the visual capability of large-scale large-language models through visual commonsense evaluation using a human-annotated dataset. |
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Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science (2024.lrec-main)
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Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song
| Challenge: | Existing instruction-tuned Large Language Models (LLMs) have impressive language understanding and the capacity to generate responses that follow specific prompts. |
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A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored . |
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Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods. |
| Approach: | They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs. |
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Empirical Study of Zero-shot Keyphrase Extraction with Large Language Models (2025.coling-main)
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| Challenge: | a prompting-based approach can effectively supersede traditional KE methods, a study shows . our code is available at https://github.com/kangnlp/zero-shot-keyphrase-extraction-with-LLMs. |
| Approach: | They propose four prompting strategies for zero-shot keyphrase extraction using Large Language Models. |
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Empirical Study of Zero-Shot NER with ChatGPT (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have been a key component of natural language processing (NLP) . |
| Approach: | They propose to decompose the NER task into simpler subproblems by labels and propose a syntactic augmentation strategy to stimulate model's intermediate thinking. |
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Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)
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| Challenge: | Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations. |
| Approach: | They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch. |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance. |
LLMs Are Zero-Shot Context-Aware Simultaneous Translators (2024.emnlp-main)
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| Challenge: | Existing SiMT systems operate on a sentence level, disregarding the context established by previous sentences or the broader context implied by previous words. |
| Approach: | They show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
| Outcome: | The proposed models perform on par with or better than state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |