Rationale before or after Classification in Prompting

Which of the following would be more accurate in producing the correct output? Is there a difference?

Rationalize before classification

Based on this description, classify this product into one of the following category of VALUE:
LOW: Covers a niche industry, and a small segment of the processes within those industries
MEDIUM: Covers a large number of industries, or a large segment of processes within those industries
HIGH: Covers a large number of industries and a large segment of processes within those industries.
UNDETERMINED: The given information doesnt give enough information for you to classify.
Do this step-by-step to reach your classification.

Classify and then rationalize

Based on this description, classify this product into one of the following category of VALUE:
LOW: Covers a niche industry, and a small segment of the processes within those industries
MEDIUM: Covers a large number of industries, or a large segment of processes within those industries
HIGH: Covers a large number of industries and a large segment of processes within those industries.
UNDETERMINED: The given information doesnt give enough information for you to classify.
Give your classification and your reasons

Thus spoke GPT-4…

The difference between “Rationalize before classification” and “Classify and then rationalize” lies in the sequence and emphasis on the reasoning process in decision-making.

  1. Rationalize before classification: This approach involves a thorough analysis and understanding of the given information before making a classification decision. You first dissect the details, weigh different aspects, and consider various factors. Only after this comprehensive analysis do you place the product into one of the categories (LOW, MEDIUM, HIGH, UNDETERMINED). This method is systematic and puts emphasis on understanding and reasoning before reaching a conclusion. It can be particularly effective in complex scenarios where multiple factors need to be considered and balanced.
  2. Classify and then rationalize: In this method, you make an initial classification based on your first impression or understanding of the description, and then you justify your decision. This approach can be quicker, as it involves making a prompt decision and then reflecting on why that decision was made. It might be more suitable in situations where you have prior experience or intuition about the subject matter. However, the risk is that the initial classification might be biased or less informed, as it precedes a detailed analysis.

In terms of accuracy and thoroughness, “Rationalize before classification” generally leads to more considered and possibly more accurate outcomes, as it ensures that the decision is based on a comprehensive understanding of the information. However, in situations where quick decisions are needed, or where the individual has significant expertise, “Classify and then rationalize” could be more efficient.

The best approach depends on the context and the nature of the decision to be made. For complex and critical decisions where accuracy is paramount, rationalizing before classifying would be more appropriate. For more straightforward decisions or when working under time constraints, classifying and then rationalizing might be more practical.

Personally, I’d run both and compare results of 10+ runs of each maybe.

Welcome to the forum. Hope you stick around.

The thing is, which of this method fits the way LLMs works? In the context of token generation, would reason before classification yield a better result? Rather than classify and then justify (or even hallucinate) your reason for that classification.

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In the context of how Large Language Models (LLMs) like GPT-4 work, the process is more akin to “Classify and then rationalize.” LLMs generate responses based on patterns they’ve learned from the vast amount of text data they’ve been trained on. They do not “reason” in the human sense; instead, they use statistical correlations to generate what they predict to be the most likely continuation of the given input.

When you ask an LLM to classify something and then provide reasoning, it essentially makes a prediction based on the patterns it has seen in its training data, and then generates text that aligns with or supports that prediction. This process can sometimes lead to what is known as “hallucination,” where the model confidently asserts information that may be incorrect or not based on the input.

The “Rationalize before classification” method implies a deliberate, sequential reasoning process, which is something LLMs don’t inherently do. They don’t deliberate or ponder over data before generating a response. They respond based on the statistical likelihood of a sequence of words, given the input and the training they’ve received.

Therefore, in terms of fitting the way LLMs work, “Classify and then rationalize” is closer to their operational model. However, it’s important to note that the quality of both the classification and the subsequent rationalization from an LLM can vary greatly, depending on the specificity of the input, the complexity of the task, and the training and limitations of the model.

I’d still test both methods and compare results…