Papers with prompt engineering strategies
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
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| Challenge: | Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. |
| Approach: | They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. |
AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation (2026.eacl-long)
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Potsawee Manakul, Woody Haosheng Gan, Michael J Ryan, Ali Sartaz Khan, Warit Sirichotedumrong, Kunat Pipatanakul, William Barr Held, Diyi Yang
| Challenge: | Current speech evaluation systems rely on specialized systems for individual audio characteristics and poor correlation between automatic methods and human preferences. |
| Approach: | They propose a unified evaluation framework for Large Audio Models as a Judge, AudioJudge . they propose specialized judges that can be prompted to perform audio characteristic detection tasks . |
| Outcome: | The proposed method improves performance across audio characteristic detection and human preference simulation tasks. |
Position Engineering: Boosting Large Language Models through Positional Information Manipulation (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated significant strides towards achieving artificial general intelligence. |
| Approach: | They propose a technique termed position engineering which alters the positional information in the prompt without modifying the text itself. |
| Outcome: | The proposed technique significantly improves on the baseline in retrieval-augmented generation and in-context learning scenarios. |
Comparative Study of Multilingual Idioms and Similes in Large Language Models (2025.coling-main)
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Paria Khoshtab, Danial Namazifard, Mostafa Masoudi, Ali Akhgary, Samin Mahdizadeh Sani, Yadollah Yaghoobzadeh
| Challenge: | figurative language is one of the most challenging aspects of human language for LLMs to comprehend . |
| Approach: | They evaluate LLMs using two multilingual datasets on simile and idiom interpretation and two new evaluation sets for Persian . they find prompt engineering methods are generally effective, but their success varies by figurative type, language, and model. |
| Outcome: | The proposed models perform better in simile and idiom interpretations across languages and figurative types. |
Auto-Evolve: Enhancing Large Language Model’s Performance via Self-Reasoning Framework (2024.findings-emnlp)
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Krishna Aswani, Huilin Lu, Pranav Patankar, Priya Dhalwani, Xue Tan, Jayant Ganeshmohan, Simon Lacasse
| Challenge: | Recent advances in prompt engineering strategies rely on static seed reasoning modules to simulate human approach to problem-solving. |
| Approach: | They propose a framework that enables LLMs to self-create dynamic reasoning modules and downstream action plan. |
| Outcome: | The proposed framework outperforms existing prompting strategies on a BigBench-Hard dataset and improves performance by 2.8% over existing methods. |
PakBBQ: A Culturally Adapted Bias Benchmark for QA (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are widely adopted in language processing applications, but they often perpetuate harmful societal biases. |
| Approach: | They propose a culturally and regionally adapted extension of the original Bias Benchmark for Question Answering dataset to address this gap. |
| Outcome: | The proposed model gains 12% accuracy with disambiguation and stronger counter bias behaviors in Urdu than in English. |
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes (2025.acl-long)
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Felix Friedrich, Katharina Hämmerl, Patrick Schramowski, Manuel Brack, Jindřich Libovický, Alexander Fraser, Kristian Kersting
| Challenge: | Text-to-image (T2I) generation models have great results in image quality, flexibility, and text alignment, but they suffer from substantial gender bias. |
| Approach: | They propose a benchmark to study gender bias in multilingual T2I models . they use multilingual prompts to account for grammatical differences influencing gender . |
| Outcome: | The proposed benchmark shows strong gender biases and language-specific differences across models. |