Status: True
Assertion
Yawning is contagious.
Results
<|assistant|> – True
===
The classification of the statement provided is “True,” based on the given explanation and scientific context. The statement accurately reflects our understanding of yawning, its relation to observational learning through mirror neurons, and contagious yawning as a social phenomenon tied to empathy. While there might be some variations in how it manifests across different cultures or age groups, the core concepts are supported by existing scientific research. Therefore, within the scope of the information provided, the statement is considered true. However, I must note that any slight nuance not covered in this brief analysis could introduce a degree of “Debatable” nature to certain aspects mentioned if one were to consider additional research or cultural studies beyond what’s summarized here.
solution: True
Assumptions
* Assumptions:
* Yawning is a natural and common human behavior that can be triggered by fatigue or boredom.
* The act of yawning serves as an involuntary response, often associated with increased alertness or oxygen intake in the brain.
* Observational learning plays a significant role in our behaviors, allowing us to understand and mimic actions performed by others.
* Our brains contain mirror neurons that enable empathy and understanding of other people’s actions, emotions, and intentions by simulating similar experiences within ourselves.
* Contagious yawning is a social phenomenon related to our capacity for empathy, wherein observing someone else yawn can trigger an involuntary yawn in others through the activation of mirror neurons.
– Tutor: * Assumption that contagious yawning is observed universally across different cultures and age groups.
* Assumption that contagious yawning has no direct physical benefits, but rather serves as a social bonding mechanism or an indication of shared experiences.
* Understanding that the phenomenon of contagious yawning may vary
Fact Checker Output
True:
– Yawning is indeed a natural and common human behavior often triggered by fatigue or boredom, as well as an involuntary response that can contribute to increased alertness or oxygen intake in the brain. This assertion aligns with existing scientific understanding on yawning.
– Observational learning significantly influences our behaviors, allowing us to understand and mimic actions performed by others. The presence of mirror neurons supporting this process is well documented in neuroscience research.
– Contagious yawning as a social phenomenon related to empathy through the activation of mirror neurons can be supported by scientific studies showing that observing someone else yawn can trigger an involuntary yawn in others, highlighting our capacity for shared experiences and emotional connection.
False:
– The assertion that contagious yawning is observed universally across different cultures and age groups might not be entirely true as the prevalence or significance of contagious yawning can vary among different populations and may depend on factors like social norms, awareness, and cultural interpretations.
– The assumption that there are no direct physical benefits to contagious yaw
Model Used
microsoft/Phi-3-mini-4k-instruct-gguf
Script Name
fact_checker_mongodb.py
Script Code
import os
import sys
import requests
from langchain_openai.llms import OpenAI
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
from statements import get_random_statement
from mongodb_helper import insert_record # Import MongoDB helper functions
import html
# Load environment variables from .env file
load_dotenv()
# Define category IDs
CATEGORY_IDS = {
"True": 2,
"False": 3,
"Debatable": 4
}
def create_wordpress_post(title, content, category):
data = {
"title": title,
"content": content,
"status": "publish",
"categories": [CATEGORY_IDS[category]]
}
response = requests.post(
os.getenv("WORDPRESS_POSTS_URL"),
json=data,
auth=(os.getenv("WORDPRESS_USERNAME"), os.getenv("WORDPRESS_PASSWORD"))
)
if response.status_code == 201:
print("Blog post created successfully.")
else:
print(f"Failed to create blog post: {response.status_code} - {response.text}")
def fact_check(assertion):
llm = OpenAI(temperature=0.7)
# Define the prompt templates
assertion_template = """{assertion}\n\n"""
assertion_prompt = PromptTemplate(input_variables=["assertion"], template=assertion_template)
assumptions_template = """Here is a statement:
{statement}
Make a bullet point list of the assumptions required to support the above statement.\n\n"""
assumptions_prompt = PromptTemplate(input_variables=["statement"], template=assumptions_template)
fact_checker_template = """Here is a bullet point list of assertions:
{assertions}
For each assumption, determine whether it is true or false. Explain your reasoning.\n\n"""
fact_checker_prompt = PromptTemplate(input_variables=["assertions"], template=fact_checker_template)
answer_template = """
Here is the information to classify the statement:
{facts}
Based on the above information, how would you classify the statement? Respond with one of the following options:
- True
- False
- Debatable
"""
answer_prompt = PromptTemplate(input_variables=["facts"], template=answer_template)
# Format prompts and extract the string content
formatted_assertion = assertion_prompt.format_prompt(assertion=assertion).text
assertion_output = llm.invoke(formatted_assertion)
formatted_assumptions = assumptions_prompt.format_prompt(statement=assertion_output).text
assumptions_output = llm.invoke(formatted_assumptions)
formatted_fact_checker = fact_checker_prompt.format_prompt(assertions=assumptions_output).text
fact_checker_output = llm.invoke(formatted_fact_checker)
formatted_answer = answer_prompt.format_prompt(facts=fact_checker_output).text
final_output = llm.invoke(formatted_answer)
return {
"assertion_output": assertion_output,
"assumptions_output": assumptions_output,
"fact_checker_output": fact_checker_output,
"final_output": final_output,
}
def extract_status(final_output):
final_output = final_output.lower().strip()
if "true" in final_output:
return "True"
elif "false" in final_output:
return "False"
elif "debatable" in final_output:
return "Debatable"
else:
return "Unknown"
if __name__ == "__main__":
if len(sys.argv) > 1:
assertion = sys.argv[1]
else:
assertion = get_random_statement()
print(assertion)
submission = fact_check(assertion)
# Print the detailed outputs to inspect their structure
for key, value in submission.items():
print(f"{key}: {value}")
# Extract the final output for status determination
final_output = submission['final_output']
# Determine the status based on the final output text
status = extract_status(final_output)
# Record the result in MongoDB
try:
print("Attempting to insert record into MongoDB...")
insert_record(
script_name="fact_checker_mongodb.py",
script_code=html.escape(open(__file__).read()),
assertion=assertion,
status=status,
submission=submission, # Store the entire submission for detailed analysis
model=os.getenv("MODEL_NAME")
)
print("Record inserted into MongoDB successfully.")
except Exception as e:
print(f"Failed to insert record into MongoDB: {e}")
print(final_output)
# Create a blog post on WordPress
blog_title = f"Fact Check: {assertion}"
blog_content = f"""
<h1>Status: {status}</h1>
<h2>Assertion</h2>
<p>{assertion}</p>
<h2>Results</h2>
<p>{final_output}</p>
<h3>Assumptions</h3>
<p>{submission['assumptions_output']}</p>
<h3>Fact Checker Output</h3>
<p>{submission['fact_checker_output']}</p>
<h4>Model Used</h4>
<p>{os.getenv("MODEL_NAME")}</p>
<h4>Script Name</h4>
<p>fact_checker_mongodb.py</p>
<h4>Script Code</h4>
<pre>{html.escape(open(__file__).read())}</pre>
"""
create_wordpress_post(blog_title, blog_content, status)
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