Papers with prompt engineering techniques
Rad-Flamingo: A Multimodal Prompt driven Radiology Report Generation Framework with Patient-Centric Explanations (2026.findings-eacl)
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| Challenge: | Existing reports are labor-intensive and expert-intensive, resulting in inconsistencies and a lack of patient-centered insight. |
| Approach: | They propose a multimodal prompt-driven report generation framework that integrates diverse data modalities to produce comprehensive and context-aware radiology reports. |
| Outcome: | The proposed framework improves report quality, improves understandability and could foster better patient-doctor communication. |
ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases (2024.eacl-long)
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| Challenge: | Reasoning over Commonsense Knowledge Bases (CSKBs) is a way to acquire new commonsense knowledge based on reference knowledge in original CSKB and external prior knowledge. |
| Approach: | They propose a plugin to provide and check explicit relational constraints over prompting techniques. |
| Outcome: | The proposed method improves on existing prompting techniques and CSKB reasoning. |
Automatic Multiple-Choice Question Generation and Evaluation Systems Based on LLM: A Study Case With University Resolutions (2025.coling-main)
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Sérgio Silva Mucciaccia, Thiago Meireles Paixão, Filipe Wall Mutz, Claudine Santos Badue, Alberto Ferreira de Souza, Thiago Oliveira-Santos
| Challenge: | Multiple choice questions (MCQs) are often used in employee selection and training, but their creation is resource-intensive and requires significant effort and investment. |
| Approach: | They propose to use large language models and prompt engineering techniques to automate the generation and validation of MCQs. |
| Outcome: | The proposed system reduces the burden on human resources and enables scalable, cost-effective MCQ generation. |
Human and LLM-Based Resume Matching: An Observational Study (2025.findings-naacl)
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Swanand Vaishampayan, Hunter Leary, Yoseph Berhanu Alebachew, Louis Hickman, Brent A. Stevenor, Weston Beck, Chris Brown
| Challenge: | Resume matching assesses the extent to which candidates qualify for jobs based on the content of resumes. |
| Approach: | They compare GPT-4 and human ratings for resumes submitted to job openings from diverse fields using real-world evaluation criteria. |
| Outcome: | The proposed model improves the quality of LLM ratings and does not show bias. |
Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering) (2024.naacl-long)
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| Challenge: | Large language models can generate synthetic data resembling real-world data, but their generative performance depends on the quality of the prompt used to instruct the model. |
| Approach: | They propose a Spanish Adaptive Prompt Engineering method that uses genetic algorithms to generate and select prompts that resemble real-world data. |
| Outcome: | The proposed method produces Spanish therapy transcripts that more closely resemble authentic therapy transcript compared to other prompt engineering techniques that are based on Reflexion and Chain-of-Thought. |
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications? (2024.findings-emnlp)
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Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar
| Challenge: | Numerical data is pivotal for medical questions and answers, but tabular data is not fully integrated into LLMs. |
| Approach: | They examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record data. |
| Outcome: | The proposed representations outperform those using raw numerical EHR data in medical diagnostics and prognostics. |
Self-Pluralising Culture Alignment for Large Language Models (2025.naacl-long)
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| Challenge: | Existing approaches to align large language models don't take cultural diversity into account. |
| Approach: | They propose a framework that generates questions on various culture topics and outputs to LLMs under both culture-aware and culture-unaware settings. |
| Outcome: | The proposed framework improves the alignment of large language models to diverse cultures without compromising general abilities. |
Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space (2025.findings-acl)
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| Challenge: | Existing methods to exploit black-box jailbreaks fail to capture key attack patterns . a novel framework decomposes jailbreak strategies into essential components . |
| Approach: | They propose a framework that decomposes jailbreak strategies into essential components and develops genetic-based optimization with intention evaluation mechanisms. |
| Outcome: | The proposed framework achieves 90% success rate on Claude-3.5, where prior methods completely fail . it also surpasses specialized safeguard models in evaluation accuracy . |
Detecting Cybercrimes in Accordance with Pakistani Law: Dataset and Evaluation Using PLMs (2024.lrec-main)
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| Challenge: | Roman Urdu is a widely used language in Pakistan but lacks sufficient resources and tools for text-based cybercrime detection. |
| Approach: | They propose to use a benchmark dataset for text-based cybercrime detection in Roman Urdu to improve the performance of pre-trained language models. |
| Outcome: | The proposed model achieves the highest performance on all metrics. |
AraSafe: Benchmarking Safety in Arabic LLMs (2025.findings-emnlp)
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| Challenge: | AraSafe is the first large-scale native Arabic safety benchmark for large language models (LLMs) it addresses the pressing need for culturally and linguistically representative evaluation resources. |
| Approach: | They propose to use Arabic prompts to annotate harmful and non-harmful prompts into nine fine-grained safety categories to support classifiers for harmful content. |
| Outcome: | The proposed benchmarks address the need for culturally and linguistically representative evaluation resources. |