ANALOBENCH: A Benchmark for Evaluating Analogical Reasoning in Large Language Models Using Stories of Varying Length and Complexity
Large language models (LLMs) struggle with analogical reasoning, particularly when dealing with longer and more complex scenarios, highlighting the need for further research to bridge the gap between human and machine analogical thinking.
Limitations of Evaluating Analogical Reasoning in Large Language Models Using Human-Centric Tests
The methods used in the original paper are insufficient to conclusively demonstrate the general, zero-shot reasoning capacity of large language models like GPT-3. Comparisons to human performance do not provide adequate evidence, and counterexamples show the brittleness of the assessment approach.
Large Language Models Demonstrate Emergent Analogical Reasoning Capabilities, Even on Counterfactual Tasks
Large language models like GPT-3 and GPT-4 exhibit an emergent capacity for analogical reasoning, which is demonstrated through their ability to solve a wide range of text-based analogy problems, including novel and counterfactual tasks.
Exploring the Limits of Large Language Models' Analogical Reasoning Capabilities
Large language models cannot always perform analogical reasoning effectively, and the accuracy of self-generated examples is the key factor determining their performance on mathematical reasoning tasks, rather than the relevance of the examples.
© 2024 by Linnk AI