Chapter 77
Open Domain Question Answering with LLMs
Open Domain Question Answering with LLMs
Background
The following prompt tests an LLM's capabilities to answer open-domain questions which involves answering factual questions without any evidence provided.
code
Note that due to the challenging nature of the task, LLMs are likely to hallucinate when they have no knowledge regarding the question.Prompt
markdown
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?Code / API
code
```python
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
)
```
```python
import fireworks.client
fireworks.client.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
)
```