mahbubur-87/llm-prompt-architecture-evaluation

This thesis evaluates Large Language Models (LLMs) using the Chain-of-Thought (CoT) prompting architecture. It employs Iterative Chain-of-Thought (Iter CoT) for analysis, utilizing various LLMs including Alpaca LoRA 13B and 30B. Results show improved accuracy with Iter CoT, notably with Alpaca LoRA 13B, outperforming ChatGPT models.

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