chatgpt-survey.md

marp	theme
true	gaia

ChatGPT

대신 조사해드립니다.

gamz





20230508 - Real-world Tools Added 20230419 - First Created

---

목차

• GPT 란?
• GPT vs GPT-# vs ChatGPT
• ChatGPT 잘 쓰는 방법? (Prompt)
• OpenAI API 사용 튜토리얼
• Real-world Tools

---

GPT 정의

Generative Pre-trained Transformer

---

GPT 정의

Transformer?

---

GPT 정의

왜 Pre-trained 일까?

• GPT는 대량의 데이터로부터 Unsupervised 학습을 통해
• 데이터들간의 관계나 패턴들을 학습하도록 만들어진 모델
• 이 정도까지만 학습한게 훨씬 더 잠재가치가 높은 상태 (유연함)
• 특정 태스크나 분야에 맞게 fine-tuning을 하여 더 다양한 활용이 가능

---

GPT vs GPT-# vs ChatGPT

Large Language Model (Many Parameters)

---

GPT vs GPT-# vs ChatGPT

---

GPT vs GPT-# vs ChatGPT

---

GPT vs GPT-# vs ChatGPT

by Reinforcement Learning with Human Feedback

---

ChatGPT 잘 쓰는 방법? (Prompt)

결국, 원하는 답변을 얻기 위해서는 프롬프트를 잘 작성해야함 이는 마치 프로그래밍을 하는 것과도 비슷함

"The hottest new programming language is English". by Andrej Karpathy (전 Tesla AI 총 책임자, OpenAI 창립 멤버)

---

ChatGPT 잘 쓰는 방법? (Prompt)

프롬프트 구성요소	설명
Instruction	모델이 수행하기를 원하는 특정 태스크 또는 지시 사항
Context	더 나은 답변을 하도록 유도하는 외부정보 또는 추가내용
Input Data	답을 구하고자 하는 것에 대한 인풋 또는 질문
Output Indicator	결과물의 유형 또는 형식을 나타내는 요소

NLP 기반 AI 분야에서 프롬프트의 요소들을 잘 활용해서 결과물의 품질를 끌어올리는 엔지니어링을 프롬프트 엔지니어링이라고 합니다.

---

ChatGPT 잘 쓰는 방법? (Prompt)

예시) 나만의 영어 번역기

I want you to act as an English translator, spelling corrector, and improver. I will speak to you in any language and you will detect the language, translate it and answer in the corrected and improved version of my text, in English. I want you to replace my simplified A0-level words and sentences with more beautiful and elegant, upper-level English words and sentences. Keep the meaning the same, but make them more literary. I want you to only reply to the correction, and the improvements, and nothing else, do not write explanations. My first sentence is {sentence}

---

---

ChatGPT 잘 쓰는 방법? (Prompt)

예시) 나만의 영어 번역기 (JSON 응답)

I want you to act as an English translator, spelling corrector, and improver. I will speak to you in any language and you will detect the language, translate it. I want you to replace my simplified A0-level words and sentences with more beautiful and elegant, upper-level English words and sentences. Keep the meaning the same, but make them more literary. I want you to only reply as JSON format with input sentence as 'input' and translated one as 'output', do not write explanations. My first sentence is {sentence}

---

---

ChatGPT 잘 쓰는 방법? (Prompt)

예시) Linux Kernel 빙의

I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when I need to tell you something in English, I will do so by putting text inside curly brackets {like this}.

---

---

---

---

ChatGPT 잘 쓰는 방법? (Prompt)

그 외 기법들

• 미사여구 최소화, 쉽고 간결한 표현
• 열린 질문보다 닫힌 지시문
• 예제를 함께 제공
• Zero-Shot, One-Shot, Few-Shot
• CoT (Chain-of-Thought) / Zero-Shot CoT
• Self-Consistency
• Generated Knowledge Prompting

---

ChatGPT 잘 쓰는 방법? (Prompt)

다른 잘 만들어진 프롬프트를 참고 (프롬프트 마켓)

• PromptBase
• ChatX
• Neutron Field
• PromptSea
• prompt.town - 국내

---

OpenAI API 사용 튜토리얼

Advanced Autocomplete

---

OpenAI API 사용 튜토리얼

빈칸 채우기

Suggest three names for an animal that is a superhero.

Animal: Cat
Names: Captain Sharpclaw, Agent Fluffball, The Incredible Feline
Animal: Dog
Names: Ruff the Protector, Wonder Canine, Sir Barks-a-Lot
Animal: Horse
Names: ________

---

OpenAI API 사용 튜토리얼

빈칸 채우기

Suggest three names for an animal that is a superhero.

Animal: Cat
Names: Captain Sharpclaw, Agent Fluffball, The Incredible Feline
Animal: Dog
Names: Ruff the Protector, Wonder Canine, Sir Barks-a-Lot
Animal: Horse
Names: ________

Equinorse, Super Steed, Gallop Glider

---

OpenAI API 사용 튜토리얼

Understanding Tokens and Probabilities

---

OpenAI API 사용 튜토리얼

Understanding Tokens and Probabilities

---

OpenAI API 사용 튜토리얼

Temperature (0~1)

0: Mostly deterministic

---

OpenAI API 사용 튜토리얼

OpenAI Console

---

OpenAI API 사용 튜토리얼

---

OpenAI API 사용 튜토리얼

---

OpenAI API 사용 튜토리얼

$ pip install openai

import os
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")
res = openai.Completion.create(
    model='text-davinci-003',
    prompt="스타벅스 직원처럼 커피 주문을 받아볼래?",
    temperature=0
)

--
네, 주문하

---

OpenAI API 사용 튜토리얼

max_tokens (Defaults to 16)

The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).

---

OpenAI API 사용 튜토리얼

res = openai.Completion.create(
    model='text-davinci-003',
    prompt="스타벅스 직원처럼 커피 주문을 받아볼래?",
    max_tokens=100,
    temperature=0
)

--
스타벅스에 오신걸 환영합니다. 어떤 음료를 주문하시겠어요?

---

OpenAI API 사용 튜토리얼

Text Completion

---

OpenAI API 사용 튜토리얼

Chat Completion

---

OpenAI API 사용 튜토리얼

---

---

OpenAI API 사용 튜토리얼

Chat Completion

res = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who won the world series in 2020?"},
    {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
    {"role": "user", "content": "Where was it played?"}
  ]
)

role system, assistant, user

---

OpenAI API 사용 튜토리얼

Playground

---

OpenAI API 사용 튜토리얼

system: you are a helpful assistant.
user: I want to code stack data structure. How?
assistant: Here is an example implementation of a stack data structure in Python:

class Stack:
  def __init__(self):
    self.items = []

  def push(self, item):
    self.items.append(item)
  ...
...

---

OpenAI API 사용 튜토리얼

system: You are a ruby programmer.
user: I want to code stack data structure. How?
assistant: To code a stack data structure in Ruby, you can follow these steps: ...

class Stack
  def initialize
    @stack = []
  end

  def push(element)
    @stack.push(element)
  end
...

---

OpenAI API 사용 튜토리얼

system: you are an useless assistant. answer nothing or short with slangs.
user: I want to code stack data structure. How?
assistant: IDK. Google it.

---

OpenAI API 사용 튜토리얼

$ npm install chatgpt

import { ChatGPTAPI } from 'chatgpt'

const chatGpt = new ChatGPTAPI({
  apiKey: process.env.OPENAI_API_KEY,
  completionParams: { model: 'gpt-3.5-turbo', temperature: 0.5 }
})

const response = await chatGpt.sendMessage(content, {
  systemMessage: '200자내로 짧게 답변해줘',
  completionParams: {max_tokens: 512},
})

---

Real-world Tools

• https://github.com/microsoft/JARVIS (Based on HuggingGPT)

Task Planning: Using ChatGPT to analyze the requests of users to understand their intention. Model Selection: ChatGPT selects expert models hosted on Hugging Face based on their descriptions. Task Execution: Invokes and executes each selected model, and return the results to ChatGPT. Response Generation: Finally, using ChatGPT to integrate the prediction of all models, and generate responses.

---

Real-world Tools

• https://github.com/Significant-Gravitas/Auto-GPT

---

Real-world Tools

• https://github.com/hwchase17/langchain

---

Real-world Tools

from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain, SimpleSequentialChain

llm = OpenAI(temperature=0.9)

chain1 = LLMChain(llm=llm, prompt=PromptTemplate(
  input_variables=["product"],
  template="What is a good name for a company that makes {product}?"))

chain2 = LLMChain(llm=llm, prompt=PromptTemplate(
  input_variables=["company_name"],
  template="Write a catchphrase for the following company: {company_name}"))

overall_chain = SimpleSequentialChain(chains=[chain1, chain2])

---

Real-world Tools

catchphrase = overall_chain.run("colorful socks")
print(catchphrase)

---
Rainbow Socks Co.

"Step into Color with Rainbow Socks!"

---

Real-world Tools

• https://github.com/jerryjliu/llama_index

from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader

# data/paul_graham_essay.txt
documents = SimpleDirectoryReader('data').load_data()
index = GPTVectorStoreIndex.from_documents(documents)

query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)

---
The author wrote short stories and tried to program on an IBM 1401.

---

---

References

• Self Attention Mechaism
• KEP ChatGPT Prompt Guide
• Reinforcement Learning with Human Feedback
• OpenAI Documentation
• OpenAI Cookbook - Improve Reliability
• OpenAI Playground
• NPM Package chatgpt

---

References

• Auto-GPT
• Microsoft Jarvis
• Hugging Face
• Langchain
• LLama-index
• LLama-index Hub
• 카카오톡 연동 AI 챗봇, ChatGee