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洞察 - Prompt engineering for large language models
Automated Prompt Optimization and Adaptation Using Contrastive Learning
A framework for automated prompt optimization and adaptation that leverages contrastive learning to enhance prompt effectiveness across different model versions, families, and languages.
Exploring the Impact of Native and Non-Native Language Prompts on Large Language Model Performance Across Social Media and News Tasks
Across different prompting techniques and language models, non-native language prompts outperform native language prompts in eliciting desired outputs for a variety of social media and news-related NLP tasks.
Improving Language Model Performance by Allowing Them to Rephrase and Respond to Questions
Allowing large language models to rephrase and expand on questions before responding can significantly improve their performance across a wide range of reasoning tasks.
The Profound Impact of Prompt Variations on Large Language Model Predictions
Even minor changes to prompts can significantly alter the predictions of large language models, with some variations leading to substantial performance degradation.
