response = chat_completion (messages=[user ("My favorite color is blue."),assistant ("That's great to hear!"),user ("What is my favorite color?"),])print (response)# "Sure, I can help you with that! Your favorite color is blue."
LLM 超参数LLM API 通常会采用影响输出的创造性和确定性的参数。在每一步中，LLM 都会生成 token 及其概率的列表。可能性最小的 token 会从列表中「剪切」（基于 top_p），然后从剩余候选者中随机（温度参数 temperature）选择一个 token。换句话说：top_p 控制生成中词汇的广度，温度控制词汇的随机性，温度参数 temperature 为 0 会产生几乎确定的结果。
def print_tuned_completion (temperature: float, top_p: float):response = completion ("Write a haiku about llamas", temperature=temperature, top_p=top_p)print (f'[temperature: {temperature} | top_p: {top_p}]n {response.strip ()}n')print_tuned_completion (0.01, 0.01)print_tuned_completion (0.01, 0.01)# These two generations are highly likely to be the sameprint_tuned_completion (1.0, 1.0)print_tuned_completion (1.0, 1.0)# These two generations are highly likely to be different
prompt 技巧详细、明确的指令会比开放式 prompt 产生更好的结果：
complete_and_print (prompt="Describe quantum physics in one short sentence of no more than 12 words")# Returns a succinct explanation of quantum physics that mentions particles and states existing simultaneously.
我们可以给定使用规则和限制，以给出明确的指令。
风格化，例如：
向我解释一下这一点，就像儿童教育网络节目中教授小学生一样；
我是一名软件工程师，使用大型语言模型进行摘要。用 250 字概括以下文字；
像私家侦探一样一步步追查案件，给出你的答案。
格式化
使用要点；
以 JSON 对象形式返回；
使用较少的技术术语并用于工作交流中。
限制
仅使用学术论文；
切勿提供 2020 年之前的来源；
如果你不知道答案，就说你不知道。
以下是给出明确指令的例子：
complete_and_print ("Explain the latest advances in large language models to me.")# More likely to cite sources from 2017complete_and_print ("Explain the latest advances in large language models to me. Always cite your sources. Never cite sources older than 2020.")# Gives more specific advances and only cites sources from 2020
complete_and_print ("Text: This was the best movie I've ever seen! n The sentiment of the text is:")# Returns positive sentimentcomplete_and_print ("Text: The director was trying too hard. n The sentiment of the text is:")# Returns negative sentiment
def sentiment (text):response = chat_completion (messages=[user ("You are a sentiment classifier. For each message, give the percentage of positive/netural/negative."),user ("I liked it"),assistant ("70% positive 30% neutral 0% negative"),user ("It could be better"),assistant ("0% positive 50% neutral 50% negative"),user ("It's fine"),assistant ("25% positive 50% neutral 25% negative"),user (text),])return responsedef print_sentiment (text):print (f'INPUT: {text}')print (sentiment (text))print_sentiment ("I thought it was okay")# More likely to return a balanced mix of positive, neutral, and negativeprint_sentiment ("I loved it!")# More likely to return 100% positiveprint_sentiment ("Terrible service 0/10")# More likely to return 100% negative
complete_and_print ("Explain the pros and cons of using PyTorch.")# More likely to explain the pros and cons of PyTorch covers general areas like documentation, the PyTorch community, and mentions a steep learning curvecomplete_and_print ("Your role is a machine learning expert who gives highly technical advice to senior engineers who work with complicated datasets. Explain the pros and cons of using PyTorch.")# Often results in more technical benefits and drawbacks that provide more technical details on how model layers
思维链简单地添加一个「鼓励逐步思考」的短语可以显著提高大型语言模型执行复杂推理的能力（Wei et al. (2022)），这种方法称为 CoT 或思维链 prompting：
complete_and_print ("Who lived longer Elvis Presley or Mozart?")# Often gives incorrect answer of "Mozart"complete_and_print ("Who lived longer Elvis Presley or Mozart? Let's think through this carefully, step by step.")# Gives the correct answer "Elvis"
import refrom statistics import modedef gen_answer ():response = completion ("John found that the average of 15 numbers is 40.""If 10 is added to each number then the mean of the numbers is?""Report the answer surrounded by three backticks, for example:```123```",model = LLAMA2_70B_CHAT)match = re.search (r'```(d+)```', response)if match is None:return Nonereturn match.group (1)answers = [gen_answer () for i in range (5)]print (f"Answers: {answers}n",f"Final answer: {mode (answers)}",)# Sample runs of Llama-2-70B (all correct):# [50, 50, 750, 50, 50] -> 50# [130, 10, 750, 50, 50] -> 50# [50, None, 10, 50, 50] -> 50
complete_and_print ("What was the temperature in Menlo Park on December 12th, 2023?")# "I'm just an AI, I don't have access to real-time weather data or historical weather records."complete_and_print ("What time is my dinner reservation on Saturday and what should I wear?")# "I'm not able to access your personal information [..] I can provide some general guidance"
检索增强生成（RAG）是指在 prompt 中包含从外部数据库检索的信息（Lewis et al. (2020)）。RAG 是将事实纳入 LLM 应用的有效方法，并且比微调更经济实惠，微调可能成本高昂并对基础模型的功能产生负面影响。
MENLO_PARK_TEMPS = {"2023-12-11": "52 degrees Fahrenheit","2023-12-12": "51 degrees Fahrenheit","2023-12-13": "51 degrees Fahrenheit",}def prompt_with_rag (retrived_info, question):complete_and_print (f"Given the following information: '{retrived_info}', respond to: '{question}'")def ask_for_temperature (day):temp_on_day = MENLO_PARK_TEMPS.get (day) or "unknown temperature"prompt_with_rag (f"The temperature in Menlo Park was {temp_on_day} on {day}'", # Retrieved factf"What is the temperature in Menlo Park on {day}?", # User question)ask_for_temperature ("2023-12-12")# "Sure! The temperature in Menlo Park on 2023-12-12 was 51 degrees Fahrenheit."ask_for_temperature ("2023-07-18")# "I'm not able to provide the temperature in Menlo Park on 2023-07-18 as the information provided states that the temperature was unknown."
程序辅助语言模型LLM 本质上不擅长执行计算，例如：
complete_and_print ("""Calculate the answer to the following math problem:((-5 + 93 * 4 - 0) * (4^4 + -7 + 0 * 5))""")# Gives incorrect answers like 92448, 92648, 95463
Gao et al. (2022) 提出「程序辅助语言模型（Program-aided Language Models，PAL）」的概念。虽然 LLM 不擅长算术，但它们非常擅长代码生成。PAL 通过指示 LLM 编写代码来解决计算任务。
