Papers with Natural Instructions dataset
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models (2023.eacl-main)
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| Challenge: | Recent work aimed to improve task performance of large language models by rewriting or tuning them manually, but manual rewrite is time-consuming and requires subjective interpretation. |
| Approach: | They propose a gradient-free, edit-based search approach for improving task instructions for large language models. |
| Outcome: | The proposed approach outperforms manual rewriting and purely example-based prompts while allowing for API-based tuning. |
When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are sensitive to subtle, non-semantic variations in prompt phrasing and formatting. |
| Approach: | They propose to evaluate 4 methods for improving prompt robustness within a unified experimental framework. |
| Outcome: | The proposed methods are compared to 8 models from Llama, Qwen and Gemma families and are generalized against multiple types of distribution shifts. |