ReCogLab: a framework testing relational reasoning & cognitive hypotheses on LLMs
Andrew Liu Henry Prior Gargi Balasubramaniam Rivka Moroshko Amir Zait Ilia Labzovsky Danny Karmon Ishita Dasgupta Kim Stachenfeld Kenneth Marino
A fundamental part of human cognition is the ability to not only recall previous memories, but also reason across them to draw conclusions. In cognitive science and psychology, this is termed relational reasoning and a number of effects and biases have been observed in human cognition. Designing experiments to measure these reasoning effects is effortful and does not transfer easily to analyzing language model reasoning patterns. To make exploring language models on relational reasoning easier, we introduce ReCogLab – a generative framework for constructing reasoning examples. Unlike static datasets, our framework has a number of benefits that help us in our goal of flexible evaluation of LLMs. First, our framework allows us to control the difficulty and context-length of the problem, allowing us to scale with model capability and evaluate LLMs at a variety of scales. Second, the ability to change the configuration of a dataset dynamically allows us to probe models on different aspects and capabilities. Finally, the flexibility of our approach enables the recreation of classic cognitive science experiments and the systematic study of relational reasoning biases in language models. We demonstrate several such experiments and present our findings on a wide variety of open and closed-source language models. We release all data and code at https://github.com/google-deepmind/recoglab.
Congitive Science, Large Language Models, Datasets, Evaluation, Relational Reasoning
We build a flexible framework generator for evaluating long-context relation reasoning in LLMs to probe different cognitive effects and biases.