Question Answering with LLMs

This section contains a collection of prompts for testing the question answering capabilities of LLMs.

Patient’s facts:
- 20 year old female
- with a history of anerxia nervosa and depression
- blood pressure 100/50, pulse 50, height 5’5’’
- referred by her nutrionist but is in denial of her illness
- reports eating fine but is severely underweight
 
Please rewrite the data above into a medical note, using exclusively the information above.

from openai import OpenAI
client = OpenAI()
 
response = client.chat.completions.create(
model="gpt-4",
messages=[
    {
    "role": "user",
    "content": "Patient’s facts:\n- 20 year old female\n- with a history of anerxia nervosa and depression\n- blood pressure 100/50, pulse 50, height 5’5’’\n- referred by her nutrionist but is in denial of her illness\n- reports eating fine but is severely underweight\n\nPlease rewrite the data above into a medical note, using exclusively the information above."
    }
],
temperature=1,
max_tokens=500,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)

import fireworks.client
fireworks.client.api_key = "<FIREWORKS_API_KEY>"
completion = fireworks.client.ChatCompletion.create(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    messages=[
        {
        "role": "user",
        "content": "Patient’s facts:\n- 20 year old female\n- with a history of anerxia nervosa and depression\n- blood pressure 100/50, pulse 50, height 5’5’’\n- referred by her nutrionist but is in denial of her illness\n- reports eating fine but is severely underweight\n\nPlease rewrite the data above into a medical note, using exclusively the information above.",
        }
    ],
    stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
    stream=True,
    n=1,
    top_p=1,
    top_k=40,
    presence_penalty=0,
    frequency_penalty=0,
    prompt_truncate_len=1024,
    context_length_exceeded_behavior="truncate",
    temperature=0.9,
    max_tokens=4000
)

In this conversation between a human and the AI, the AI is helpful and friendly, and when it does not know the answer it says "I don’t know".
 
AI: Hi, how can I help you?
Human: Can I get McDonalds at the SeaTac airport?

from openai import OpenAI
client = OpenAI()
 
response = client.chat.completions.create(
model="gpt-4",
messages=[
    {
    "role": "user",
    "content": "In this conversation between a human and the AI, the AI is helpful and friendly, and when it does not know the answer it says \"I don’t know\".\n\nAI: Hi, how can I help you?\nHuman: Can I get McDonalds at the SeaTac airport?"
    }
],
temperature=1,
max_tokens=250,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)

import fireworks.client
fireworks.client.api_key = "<FIREWORKS_API_KEY>"
completion = fireworks.client.ChatCompletion.create(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    messages=[
        {
        "role": "user",
        "content": "In this conversation between a human and the AI, the AI is helpful and friendly, and when it does not know the answer it says \"I don’t know\".\n\nAI: Hi, how can I help you?\nHuman: Can I get McDonalds at the SeaTac airport?",
        }
    ],
    stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
    stream=True,
    n=1,
    top_p=1,
    top_k=40,
    presence_penalty=0,
    frequency_penalty=0,
    prompt_truncate_len=1024,
    context_length_exceeded_behavior="truncate",
    temperature=0.9,
    max_tokens=4000
)

Answer the question based on the context below. Keep the answer short and concise. Respond "Unsure about answer" if not sure about the answer.
 
Context: Teplizumab traces its roots to a New Jersey drug company called Ortho Pharmaceutical. There, scientists generated an early version of the antibody, dubbed OKT3. Originally sourced from mice, the molecule was able to bind to the surface of T cells and limit their cell-killing potential. In 1986, it was approved to help prevent organ rejection after kidney transplants, making it the first therapeutic antibody allowed for human use.
 
Question: What was OKT3 originally sourced from?
Answer:

from openai import OpenAI
client = OpenAI()
 
response = client.chat.completions.create(
model="gpt-4",
messages=[
    {
    "role": "user",
    "content": "Answer the question based on the context below. Keep the answer short and concise. Respond \"Unsure about answer\" if not sure about the answer.\n\nContext: Teplizumab traces its roots to a New Jersey drug company called Ortho Pharmaceutical. There, scientists generated an early version of the antibody, dubbed OKT3. Originally sourced from mice, the molecule was able to bind to the surface of T cells and limit their cell-killing potential. In 1986, it was approved to help prevent organ rejection after kidney transplants, making it the first therapeutic antibody allowed for human use.\n\nQuestion: What was OKT3 originally sourced from?\nAnswer:"
    }
],
temperature=1,
max_tokens=250,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)

import fireworks.client
fireworks.client.api_key = "<FIREWORKS_API_KEY>"
completion = fireworks.client.ChatCompletion.create(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    messages=[
        {
        "role": "user",
        "content": "Answer the question based on the context below. Keep the answer short and concise. Respond \"Unsure about answer\" if not sure about the answer.\n\nContext: Teplizumab traces its roots to a New Jersey drug company called Ortho Pharmaceutical. There, scientists generated an early version of the antibody, dubbed OKT3. Originally sourced from mice, the molecule was able to bind to the surface of T cells and limit their cell-killing potential. In 1986, it was approved to help prevent organ rejection after kidney transplants, making it the first therapeutic antibody allowed for human use.\n\nQuestion: What was OKT3 originally sourced from?\nAnswer:",
        }
    ],
    stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
    stream=True,
    n=1,
    top_p=1,
    top_k=40,
    presence_penalty=0,
    frequency_penalty=0,
    prompt_truncate_len=1024,
    context_length_exceeded_behavior="truncate",
    temperature=0.9,
    max_tokens=4000
)