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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description" content="Using Analogical Reasoning to Prompt LLMs
for their Intuitions of Abstract Spatial Schemas">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Using Analogical Reasoning to Prompt LLMs
for their Intuitions of Abstract Spatial Schemas</title>
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<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Using Analogical Reasoning to Prompt LLMs
for their Intuitions of Abstract Spatial Schemas</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://www.phil-wicke.com/">Philipp Wicke</a><sup>1,3</sup>,</span>
<span class="author-block">
<a href="https://github.com/hirlimann">Lea Hirlimann</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.cisuc.uc.pt/en/people/joao-m-cunha">João Miguel Cunha</a><sup>2</sup>,</span>
<span class="author-block">
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>LMU Munich</span>
<span class="author-block"><sup>2</sup>CISUC, University of Coimbra</span>
<span class="author-block"><sup>3</sup>Munich Center for Machine Learning (MCML)</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://analogy-angle.github.io/assets/Wicke.pdf" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<!-- Code Link. -->
<span class="link-block">
<a href="https://github.com/PhilWicke/Image-Schemas-in-LLMs" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Abstract notions are often comprehended through
analogies, wherein there exists correspondence or
partial similarity with more concrete concepts. A
fundamental aspect of human cognition involves
synthesising embodied experiences into spatial
schemas, which profoundly influence
conceptualisation and underlie language
acquisition. Recent studies have demonstrated
that Large Language Models (LLMs) exhibit
certain spatial intuitions akin to human
language. For instance, both humans and LLMs
tend to associate ↑ with 'hope' more readily
than with 'warn'. However, the nuanced
partial similarities between concrete (e.g.,
↑) and abstract (e.g., hope)
concepts, remain insufficiently explored.
Therefore, we propose a novel methodology
utilising analogical reasoning to elucidate
these associations and examine whether LLMs
adjust their associations in response to
analogical prompts. We find that analogy-prompting
is slightly increasing agreement with human
choices and the answers given by models
include valid explanations supported by
analogies, even when in disagreement with
human results.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Example Inference</h2>
<div class="publication-image">
<img src="static/images/models.png" alt="Example Inference" width="100%">
</div>
</div>
</div>
<!--/ Paper video. -->
</div>
</section>
<!-- Add this section to the existing HTML code -->
<section class="section has-text-centered" id="scientific-data-viewer">
<div class="container">
<h2 class="title is-3">Results</h2>
<div id="conditionButtons">
<button class="conditionButton text" onclick="selectCondition('TEXT')">TEXT</button>
<button class="conditionButton pseudo-visual" onclick="selectCondition('UNICODE')">UNICODE</button>
</div>
<div id="modelButtons" class="hidden">
<!-- Models will be dynamically added here based on the selected condition -->
</div>
<div id="imageContainer">
<img id="selectedImage" src="images/default.png" alt="Click on a model to select results">
</div>
</div>
<script>
const models = {
TEXT: ['GPT-3-instruct', "GPT-4"],
UNICODE: ['GPT-3-instruct', "GPT-4"],
};
function selectCondition(condition) {
var modelButtons = document.getElementById("modelButtons");
modelButtons.innerHTML = "";
// Add each model as a button
models[condition].forEach(model => {
var button = document.createElement("button");
button.className = "modelButton";
button.textContent = model;
button.onclick = function () {
selectModel(condition, model);
};
modelButtons.appendChild(button);
});
// Show the model buttons
modelButtons.classList.remove("hidden");
updateImage(condition, models[condition][0]);
}
function selectModel(condition, model) {
var modelButtons = document.getElementById("modelButtons");
// Toggle the selected state for the model buttons
modelButtons.childNodes.forEach(button => {
button.classList.remove("selectedModel");
if (button.textContent === model) {
button.classList.add("selectedModel");
}
});
updateImage(condition, model);
}
function updateImage(condition, model) {
var imagePath = `images/${condition}/${condition}_${model}_Choices.png`;
document.getElementById("selectedImage").src = imagePath;
}
</script>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{wicke2024using,
author = {Wicke, Philipp and Hirlimann, Lea and Cunha, Joao Miguel},
title = {Using Analogical Reasoning to Prompt LLMs for their Intuitions of Abstract Spatial Schemas},
journal = {Workshop Proceedings of the First Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-ANGLE) co-located at IJCAI},
year = {2024},
}</code></pre>
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