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finetuning

Prompt engineering vs. fine-tuning: What’s better for your use case?

Discover the key differences between prompt engineering and model fine-tuning. Learn when to use each approach, how to measure effectiveness and the best tools for optimizing LLM performance.

Instruction Tuning with GPT-4 - Summary

The paper presents the first attempt to use GPT-4 to generate instruction-following data for Large Language Models (LLMs) finetuning. The 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks compared to the instruction-following

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts - Summary

The paper introduces AUTOPROMPT, an automated method to create prompts for a diverse set of tasks based on a gradient-guided search. The prompts elicit more accurate factual knowledge from masked language models (MLMs) than manually created prompts on the LAMA benchmark. MLMs can perform sentiment