Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
This paper introduces 26 guiding principles designed to streamline the process of querying and prompting large language models. Our goal is to simplify the underlying concepts of formulating questions for various scales of large language models, examining their abilities, and enhancing user comprehension on the behaviors of different scales of large language models when feeding into different prompts. Extensive experiments are conducted on LLaMA-1/2 (7B, 13B and 70B), GPT-3.5/4 to verify the effectiveness of the proposed principles on instructions and prompts design. We hope that this work can provide a better guide for researchers working on the prompting of large language models. Project page is available at https://github.com/VILA-Lab/ATLAS.
Code (2)
Tasks
AllMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
How Do Language Models Process Ethical Instructions? Deliberation, Consistency, and Other-Recognition Across Four Models
Alignment safety research assumes that ethical instructions improve model behavior, but how language models internally process such instructions remains unknown. We conducted over 600 multi-agent simulations across four …
Take That for Me: Multimodal Exophora Resolution with Interactive Questioning for Ambiguous Out-of-View Instructions
Daily life support robots must interpret ambiguous verbal instructions involving demonstratives such as ``Bring me that cup,'' even when objects or users are out of the robot's view. Existing approaches to exophora resol…
Sound Source LocalizationLLaMA-Omni: Seamless Speech Interaction with Large Language Models
Models like GPT-4o enable real-time interaction with large language models (LLMs) through speech, significantly enhancing user experience compared to traditional text-based interaction. However, there is still a lack of …
Structured Uncertainty guided Clarification for LLM Agents
LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and task failures. Existing approaches operate in unstructured language spaces, ge…
Reinforcement LearningQuestion SelectionPlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator
The unparalleled performance of closed-sourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT dialogues, as evidenced by Vicuna. However, due to cha…
DiversityLanguage ModellingLarge Language Model