A study evaluates the effectiveness of profession-specific system prompts on scientific tasks, comparing them to generic and unrelated prompts. The results show that longer prompts do not improve accuracy and increase costs.

Researchers presented a blackbox prompt-minimization framework for large language models (LLMs) that reduces few-shot prompts to their necessary minimal subset. The framework, called ramework, preserves propositional output fidelity and shows that models preferentially retain logical identifiers and constraint declarations while discarding natural language prose and cross-prompt relational annotations.

This paper explores the effect of prompt choice on transfer in on-policy distillation, a technique for training AI agents. The study finds that a few well-chosen prompts can be as effective as a large pool of prompts, but the effectiveness depends on the teacher-student pair and target capability. This research has implications for the development of AI agents and the choice of prompts for on-policy distillation.

This paper studies the use of intuitive prompting to improve the fidelity of language models (LLMs) simulating social media reactions. The authors found that instructing LLMs to respond intuitively and immediately resulted in higher fidelity and better performance on unfamiliar content, suggesting potential applications for general-purpose simulated users.