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Overview

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Generate Examples First

Analogical Prompting is a method that aims to get LLMs to generate examples that are relevant to the problem before starting to address the user’s query.

This takes advantage of the various forms of knowledge that the LLM has acquired during training and explicitly prompts them to recall the relevant problems and solutions. We can use Analogical Prompting using the following template

Analogical Prompting Prompt Template

  • Problem: [user prompt]
  • Relevant Problems: Recall [n] relevant and distinct problems.
  • For each problem, describe it and explain the solution

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Example

We can implement this using Instructor to solve the problem, as seen below with some slight modifications.

<?php



$loader = require 'vendor/autoload.php';

$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');



use Cognesy\Instructor\Instructor;



class Problem {

    public string $problemExplanation;

    public string $solution;

}



class Response {

    /** @var Problem[] */

    public array $relevantProblems;

    public Problem $problemSolution;

    public string $answer;

}



class SolvePerAnalogy {

    private int $n = 3;

    private string $prompt = <<<PROMPT

        <problem>

        {query}

        </problem>

        

        Relevant Problems: Recall {n} relevant and

        distinct problems. For each problem, describe

        it and explain the solution before solving

        the problem    

    PROMPT;



    public function __invoke(string $query) : Response {

        return (new Instructor)->respond(

            messages: str_replace(['{n}', '{query}'], [$this->n, $query], $this->prompt),

            responseModel: Response::class,

        );

    }

}



$solution = (new SolvePerAnalogy)('What is the area of the square with the four vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2)?');



dump($solution);

?>

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References

  1. Large Language Models As Analogical Reasoners
Generate In-Context ExamplesArbitrary properties