Zero-shot Large Language Models for Automatic Readability Assessment (2026.acl-long)
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| Challenge: | Existing unsupervised automatic readability assessment methods have important practical and research applications. |
| Approach: | They propose a new zero-shot prompting methodology for automatic readability assessment (ARA) they propose combining large language models with readability formula scores to improve robustness . |
| Outcome: | The proposed method outperforms prior methods on 13 of 14 datasets and improves on contextual and shallow features of readability. |
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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. |
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| Challenge: | Large language models (LLMs) are useful for low-resource scenarios and time-restricted applications. |
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| Challenge: | Recent advances in large language models have enabled impressive zero-shot capabilities across various natural language tasks. |
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A Dataset for Expert Reviewer Recommendation with Large Language Models as Zero-shot Rankers (2025.coling-main)
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Vanja M. Karan, Stephen McQuistin, Ryo Yanagida, Colin Perkins, Gareth Tyson, Ignacio Castro, Patrick G.T. Healey, Matthew Purver
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Universal Self-Adaptive Prompting (2023.emnlp-main)
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| Challenge: | a hallmark of modern large language models is their impressive general zero-shot and few-shot abilities . however, zero- shot performances are weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks. |
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| Challenge: | Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. |
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Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability. |
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