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ChatGPT Usage Guide

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LangChain for LLMs Usage

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Prompt

Index

Memory

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Methodology Chapter

Design Principle

Framework

Basic Prompt

Advanced Prompt

Batch Prompting

Successive Prompting

PAL

ReAct

Self-Ask

Context-faithful Prompting

REFINER

Reflections

Progressive-Hint Prompt

Self-Refine

RecurrentGPT

Automatic Prompt

Optimization with Gradient Descent and Beam Search

Genetic Prompt Search for Efficient Few-shot Learning

Explaining Data Patterns in Natural Language

Automatically Generate Prompts using Generative Models

Automatic Prompt Editing to Refine AI-Generative Art

CoT

Auto-COT Prompting

One-Few Shot CoT Prompting

Self-consistency

Zero-shot CoT Prompting

In-Context Learning

Transformers learn in-context by gradient descent

Language Models Secretly Perform Gradient Descent as Meta-Optimizers

Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale

Complementary Explanations for Effective In-Context Learning

PLACES: Prompting Language Models for Social Conversation Synthesis

In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Diverse Demonstrations Improve In-context Compositional Generalization

Knowledge Augumented Prompt

Evaluation and Reliability

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Have LLMs Advanced Enough?A Challenging Problem Solving Benchmark For Large Language Models

EVEVAL:A Comprehensive Evaluation of Event Semantics for Large Language Model

Is GPT-4 a Good Data Analyst?

Theory Chapter

The Overview of LLM

Transformer

Tokenizer

BERT

GPT Series

T5

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📡 Advanced Prompt

Overview

Batch Prompting
Successive Prompting
PAL Prompting
ReAct Prompting
Self-ask
Context-faithful Prompting
REFINER: Reasoning Feedback on Intermediate Representations
Reflexion
Progressive-Hint Prompt
Self-Refine
RecurrentGPT

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