Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

arXiv:2607.14109v1 Announce Type: new Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding. Furthermore, the rapid proliferation of LLMs has created the implicit assumption that more sophisticated prompting techniques yield better performance. Several studies claim.

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