What is Self-Consistency in Prompt Engineering?

Introduction 

Have you ever wondered what it takes to communicate effectively with today’s most advanced AI models? As Large Language Models (LLMs) like Claude, GPT-3, and GPT-4 become more sophisticated, how we interact with them has evolved into a precise science. No longer just an art, creating effective prompts has become essential to harnessing the full potential of these powerful tools. One key concept in this domain is self-consistency, a technique that significantly boosts the accuracy and reliability of LLM responses. In this article, we will talk about self-consistency, which is revolutionizing prompt engineering, and explore its many benefits.

If you want to brush up your Prompt Engineering knowledge, then this guide is for you – Prompt Engineering: Definition, Examples, Tips & More.

Overview

• Self-consistency in prompt engineering enhances LLM accuracy by generating multiple responses and combining them to mitigate errors.
• Prompt engineering involves creating precise, clear prompts to communicate effectively with AI models like GPT-4.
• The self-consistency method relies on the principle that multiple responses help identify the most accurate answer among them.
• Implementing self-consistency includes creating a clear prompt, generating multiple responses, analyzing them, and aggregating the results.
• Benefits of self-consistency include increased accuracy, reduced impact of outliers, and better handling of ambiguous tasks.

What is Self Consistency?

In prompt engineering, self-consistency generates several answers to a single prompt and then combines them to create an output. This method lessens the effect of occasional errors or inconsistencies and increases overall accuracy by utilizing the inherent variety in LLM outputs.

The fundamental tenet of self-consistency is that, although an LLM may occasionally yield inconsistent or inaccurate findings, it is more likely to generate accurate responses than inaccurate ones. We can determine which answer is more consistent and most likely to be right by requesting many responses and comparing them.

Implementing Self Consistency

Here are the following actions to integrate self-consistency into your prompt engineering workflow:

1. Create a specific and clear prompt
2. generate multiple responses based on the same prompt.
3. Compare and examine the responses
4. Aggregate the results to produce a final Response.

Let’s look at these steps with some Python and OpenAI API code examples.

Pre-Requisite and Setup

Installation of dependencies

!pip install openai --upgrade

Importing libraries

import os
from openai import OpenAI

Setting Api key configuration

os.environ["OPENAI_API_KEY"]= “Your open-API-Key”

Step 1. Create a specific and clear prompt

The first step is to craft a well-defined prompt that clearly communicates your intended task or question. For example:

prompt = """
Solve the following math problem step by step:
A train travels at a speed of 60 km/h for 2 hours, then at 80 km/h for 1 hour.
What is the average speed of the train for the entire journey?
Provide your answer in km/h, rounded to two decimal places.
"""

Step 2: Generate multiple responses based on the same prompt.

Next, we’ll use the OpenAI API to generate multiple responses based on our prompt. We’ll create a function to do this:

#creating Client
client = OpenAI()
def generate_responses(prompt, n=5):
    responses = []
    for _ in range(n):
        response= client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": prompt,
                }
            ],
            model="gpt-3.5-turbo",
        )
        responses.append(response.choices[0].message.content.strip())
    return responses
# Generate 5 responses
results = generate_responses(prompt, n=5)
for i, result in enumerate(results):
    print(f"Response {i+1}:\n{result}\n")

Output

Response 1

Response 2

Response 3

Response 4

Response 5

Step 3: Compare and examine the responses

Now that we have multiple responses, we need to analyze and compare them. This step can vary depending on the type of task. For our math problem, we’ll extract the final answer from each response and compare them:

import re
def extract_answer(response):
   match = re.search(r'(\d+\.\d+)\s*km/h', response)
   if match:
       return float(match.group(1))
   return None
answers = [extract_answer(response) for response in results]
valid_answers = [answer for answer in answers if answer is not None]
valid_answers

Output

Step 4: Aggregate the results to produce a final Response.

Ultimately, we will combine the outcomes to generate our final output. The mean or median can be used for numerical responses. In this instance, the median will be used to lessen the impact of outliers:

import statistics
if valid_answers:
   final_answer = statistics.median(valid_answers)
   print(f"The most consistent answer is: {final_answer:.2f} km/h")
else:
   print("Unable to determine a consistent answer.")

Output

Benefits of Self Consistency

Here are the benefits of self-consistency:

• Increased Accuracy: Self-consistency frequently yields more accurate results than depending on a single response because it generates and aggregates several responses.
• Diminished Effect of Outliers: By taking into account several responses, occasional mistakes or discrepancies in LLM outcomes are reduced.
• Measuring Confidence: The degree of consistency between responses can be used to gauge one is confidence in the final outcome.
• Handling Ambiguity: Self-consistency can help determine the most prevalent or likely interpretation of tasks when there are several legitimate interpretations.

More Advanced Techniques of Self-Consistency

Although self-consistency, when applied simply is a potent strategy, there are a few more sophisticated approaches that can increase its efficacy even further:

• Weighted Aggregation: You can assign weights based on respondent confidence or similarity to other responses rather than evaluating all responses equally.
• Clustering: To group related responses and find the most prominent clusters, apply clustering techniques to increasingly complicated jobs.
• Chain-of-Thought Prompting: To produce more thorough and well-reasoned answers, combine self-consistency with chain-of-thought prompting.

Here’s an example of how you might implement weighted aggregation:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
def weighted_aggregation(responses):
   # Convert responses to TF-IDF vectors
   vectorizer = TfidfVectorizer()
   tfidf_matrix = vectorizer.fit_transform(responses)
   # Calculate pairwise similarities
   similarities = cosine_similarity(tfidf_matrix)
   # Calculate weights based on average similarity to other responses
   weights = similarities.mean(axis=1)
   # Extract answers and apply weights
   answers = [extract_answer(response) for response in responses]
   weighted_answers = [a * w for a, w in zip(answers, weights) if a is not None]
   # Calculate weighted average
   if weighted_answers:
       return sum(weighted_answers) / sum(weights)
   return None
final_answer = weighted_aggregation(results)
if final_answer:
   print(f"The weighted average answer is: {final_answer:.2f} km/h")
else:
   print("Unable to determine a consistent answer.")

Challenges and Limitations of Self-consistency

Although self-consistency is an effective tactic, it’s crucial to understand its limitations:

• Computational Cost: Producing several answers uses more processing power, which could increase API fees.
• Time Complexity: When dealing with complicated jobs, generating and analyzing several responses can take a while.
• Consensus Bias: Common misunderstandings or biases found in the model’s training set may be strengthened by self-consistency.
• Task Dependence: The effectiveness of self-consistency can vary depending on the nature of the task. For extremely creative or subjective activities, it might be less helpful.

Conclusion

In prompt engineering, self-consistency is a useful strategy that can greatly increase the accuracy and dependability of LLM outputs. By generating several responses and combining them, we can lessen the effects of occasional mistakes and inconsistencies. As prompt engineering develops, self-consistency will probably become a crucial element in the creation of durable and dependable AI systems.

As with any technique, you should consider the trade-offs and the particular requirements of the task at hand. When used carefully, self-consistency can be a significant weapon in your quick engineering toolbox, enabling you to fully utilize big language models.

Frequently Asked Questions

Shikha Sen

With 4 years of experience in model development and deployment, I excel in optimizing machine learning operations. I specialize in containerization with Docker and Kubernetes, enhancing inference through techniques like quantization and pruning. I am proficient in scalable model deployment, leveraging monitoring tools such as Prometheus, Grafana, and the ELK stack for performance tracking and anomaly detection.

My skills include setting up robust data pipelines using Apache Airflow and ensuring data quality with stringent validation checks. I am experienced in establishing CI/CD pipelines with Jenkins and GitHub Actions, and I manage model versioning using MLflow and DVC.

Committed to data security and compliance, I ensure adherence to regulations like GDPR and CCPA. My expertise extends to performance tuning, optimizing hardware utilization for GPUs and TPUs. I actively engage with the LLMOps community, staying abreast of the latest advancements to continually improve large language model deployments. My goal is to drive operational efficiency and scalability in AI systems.