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This paper explores cross-lingual prompting strategies to improve access to parametric knowledge in large language models, demonstrating significant gains in knowledge transfer and factual recall across 17 languages on multilingual benchmarks.
This paper investigates seemingly contradictory findings on whether large vision-language models (LVLMs) can coordinate efficient referring expressions. The authors show that models can achieve efficiency when explicitly prompted, but fail to infer the need for efficiency from implicit prompts, revealing key differences between human and AI communication.
This paper analyzes when persona prompting improves LLM responses, finding that it increases expertise depth at the cost of clarity, with effectiveness varying by domain and question type. The study introduces hybrid retrieval for role selection and advocates for multi-metric evaluation.
A user describes the problem of AI assistants confidently giving unverified advice for technical tasks like WordPress optimization, requiring users to slow down and demand verification. The article explores prompting strategies to avoid waste of time.
This paper introduces SCALAR, a structured critic-actor loop framework, to evaluate how different interaction patterns between AI agents improve reasoning in theoretical physics problems.