Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4 (AI summary)

Key Points

1. The article introduces a highly capable multimodal model called Gemini.

2. It discusses a foundation language model named Llama.

3. Llama 2 is open foundation and fine-tuned chat models.

4. The research describes how chain-of-thought prompting elicits reasoning in large language models.

5. It includes a prompt pattern catalog to enhance prompt engineering with ChatGPT.

6. The research evaluates the performance of large language models with mt-bench and chatbot arena.

7. Least-to-most prompting enables complex reasoning in large language models.

8. It introduces the concept of judging LLM-as-a-judge with mt-bench and chatbot arena.

9. The article also presents the Llama 2: Open foundation and fine-tuned chat models.

Summary

The paper introduces 26 guiding principles to optimize the interaction with large language models (LLMs), aiming to simplify the process of formulating questions and prompts for LLMs. The principles guide prompt engineering and are designed to enhance user comprehension of LLM behaviors. The researchers conducted experiments with LLaMA-1/2, GPT-3.5/4 to verify the effectiveness of the proposed principles on instructions and prompts design. The principles cover various aspects, including prompt structure, specificity, user interaction, content, and language style. The paper evaluates the impact of the principles on boosting response quality and correctness in the context of different scales of LLMs. The study demonstrates that the principles can significantly improve the effectiveness of prompts, leading to higher quality and more accurate responses from LLMs. Throughout the paper, the focus is on providing practical, evidence-based advice for improving the interaction with large language models.

The research paper presents a family of highly capable multimodal models called Gemini. The team involved in this study comprises a large number of researchers. The paper aims to introduce new models for natural language processing (NLP) tasks. This includes Llama and Llama 2, as well as the application of Chain-of-thought prompting and prompt pattern catalog to enhance prompt engineering with ChatGPT. The team also explores models for judgment tasks and reasoning in large language models. Overall, the paper addresses the development of advanced models for NLP tasks and their potential applications in various domains.

Reference: https://arxiv.org/abs/2312.16171v1