Papers with prompt-based approach
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets (2025.acl-srw)
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| Challenge: | Recent advances in NLP have enabled the use of text-to-text annotation without providing training samples. |
| Approach: | They propose a text-to-text interface for automatic annotation using written guidelines without providing training samples. |
| Outcome: | The proposed approach is comparable with the fine-tuned BERT but without any training data. |
Bootstrapping Neural Relation and Explanation Classifiers (2023.acl-short)
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| Challenge: | supervised approaches that use only rules to explain the outputs of the relation classifier are data hungry and expensive to obtain. |
| Approach: | They propose a method that self trains (or bootstraps) neural relation and explanation classifiers by iterating the outputs into rules and applying them to unlabeled text to produce new annotations. |
| Outcome: | The proposed method outperforms the rule-based model on the TACRED dataset by 15 F1 points and performs comparatively with the prompt-based approach without an additional natural language inference component. |
PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speech (2025.emnlp-industry)
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| Challenge: | Text Normalization (TN) is a key preprocessing step in Text-to-Speech systems. |
| Approach: | They propose a prompt-based approach to TN using Large Language Models (LLMs) they propose scalable experimentation across languages to reduce the reliance on manual rules . |
| Outcome: | The proposed approach reduces the reliance on manual rules and enables broader linguistic applicability with minimal human intervention across eight languages. |
MTO: A Multi-turn Conversational Text-to-OverpassQL Dataset for Enhanced OpenStreetMap Query Generation (2026.findings-acl)
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| Challenge: | a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets is proposed . a dataset of over 7,800 dialogues contains more than 20,000 individual utterances . |
| Approach: | They propose a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets . they convert Overpass queries into syntax trees using a custom parser based on OverpassQl . |
| Outcome: | The proposed dataset includes over 7,800 dialogues, each containing 2 to 4 user utterances . it is the first multi-turn Text-to-OverpassQL dataset built upon the OverpassNL corpus . |
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels (2023.findings-acl)
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| Challenge: | Existing approaches to generate informative titles for products with limited labels are inadequate for novel products. |
| Approach: | They propose a prompt-based approach to generate attractive titles for novel products . they use multimodal prompts to preserve characteristics and writing styles of novel products. |
| Outcome: | The proposed approach achieves state-of-the-art results on novel product categories with limited labels. |
Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery (2025.findings-emnlp)
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| Challenge: | Current causal discovery methods rely on pairwise or iterative strategies that fail to capture global dependencies, amplify local biases, and reduce overall accuracy. |
| Approach: | They propose a framework for one-step full causal graph discovery using prompt-based discovery and a data-driven method for settings without metadata. |
| Outcome: | The proposed framework outperforms state-of-the-art models by approximately 40% in edge accuracy on datasets like Asia and Sachs while maintaining strong performance on more complex graphs. |
Few-shot Table-to-text Generation with Prefix-Controlled Generator (2022.coling-1)
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| Challenge: | Neural table-to-text generation approaches are data-hungry and lack labeled data. |
| Approach: | They propose a prompt-based approach for few-shot table-to-text generation using a task-specific prefix and an input-specific input prefix. |
| Outcome: | The proposed approach is able to generate table-to-text summaries with a few instances and is validated on human, book and song datasets. |
GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization (2023.findings-acl)
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| Challenge: | Existing datasets are limited to newswire text, which is a fraction of extant genres in general and on the Web. |
| Approach: | They present a small but carefully crafted dataset of English summaries in 12 written and spoken genres for evaluation of abstractive summarization. |
| Outcome: | The proposed dataset of English summaries in 12 written and spoken genres is compared with human outputs and compared to untuned and prompt-based approaches. |
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities (2023.emnlp-main)
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| Challenge: | Existing methods for event coreference resolution (ECR) do not leverage human-summarized rules to guide the model. |
| Approach: | They propose to transform ECR into a cloze-style MLM task using a prompt-based approach . they introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility . |
| Outcome: | The proposed method performs well in a state-of-the-art (SOTA) benchmark. |
Localizing and Mitigating Errors in Long-form Question Answering (2025.findings-acl)
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| Challenge: | Long-form question answering (LFQA) answers are prone to hallucinations and factual inconsistencies, challenging their faithful evaluation. |
| Approach: | They propose a dataset with localized error annotations for human-written and model-generated LFQA answers. |
| Outcome: | The proposed approach reduces errors and improves quality of the answers across multiple models. |
Non-Essential Is NEcessary: Order-agnostic Multi-hop Question Generation (2024.lrec-main)
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| Challenge: | Existing multi-hop question generation methods treat answer-irrelevant documents as non-essential and remove them as impurities, which can lead to a decrease in model performance. |
| Approach: | They propose a task which leverages non-essential data in the training phase to create a robust model and extract the consistent embeddings in real-world inference environments. |
| Outcome: | The proposed model can perform ranker and generator without external modules and achieves state-of-the-art on a hotpotQA dataset. |
Easy as PIE? Identifying Multi-Word Expressions with LLMs (2025.emnlp-main)
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| Challenge: | Multiword expressions (MWEs) are a semantically non-compositional subclass of multiword expression . authors show that prompt-based LLMs can perform competitively with supervised models . |
| Approach: | They propose a prompt-based approach to identify idiomatic expressions in running text . they find prompt-driven LLMs can perform competitively with supervised models . |
| Outcome: | The proposed approach can perform well with supervised models on annotated data. |