A nalo B ench: Benchmarking the Identification of Abstract and Long-context Analogies (2024)

Abstract: Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a benchmark to determine analogical reasoning ability in LMs. Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios. We collect a set of 340 high quality, human written analogies for use in our benchmark, which constitutes the largest such collection to date. We then test a broad collection of models consisting of 12 open source and 3 proprietary in various sizes and architectures. As in prior results, scaling up LMs results in some performance boosts. Surprisingly, scale offers minimal gains when, (i) analogies involve lengthy scenarios, or (ii) recalling relevant scenarios from a large pool of information, a process analogous to finding a needle in a haystack. We hope these observations encourage further research in this field.

## introduction analogy is the ability to think about relational patterns @xcite and forms an integral aspect of human communication @xcite . this cognitive ability helps humans understand new or difficult concepts by relating them to more familiar experiences @xcite . analogical thinking plays a critical role in some of the major breakthroughs in human history, such as the discovery of gravity or even einstein's theory of provided story. maria spent years caring for everyone else's needs, barely taking a moment for herself. one day, she collapsed from exhaustion, finally understanding you can't pour from an empty cup; it was high time she cared for herself too. goal: given the following story, find the analogous stories from the story bank. ## analobench: a benchmark for abstract and long-context analogies we discuss design considerations and challenges of benchmarking analogies ( §3.1), the construction of analobench ( §3.2), and tasks devised based on this dataset ( §3.3). our analogies follow the definition given by structure mapping theory @xcite , where common relational structures between two domains (i.e, stories, in our setting) define an analogy. succeeding on our tasks does not involve recalling the surface form of stories, but rather pin-pointing the shared relational structures. longer stories preserve the relational structures but are padded with "noise." when humans perform our task, we intend for them to come up with their own internal representation of salient features. our benchmark then focuses on the question of how llms fare when they are presented with the same task. ## main experiments we structure our experimental assessment around two primary tasks aimed at evaluating the analogical reasoning of lms. we discuss the experimental setting including the metrics, models and human evaluation ( §4.1), followed by the results. ## conclusion analogical reasoning is an important aspect of human cognition, with wide-ranging potential for future research. to benchmark this ability in lms, we define a general approach by scaling the length of stories and the context from which they need to be retrieved. our benchmark exposes the limitations of analogical reasoning in modern lms. we release analobench to motivate further research.