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Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

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Language models, like humans, show content effects on reasoning tasks.

Andrew K Lampinen¹, Ishita Dasgupta¹, Stephanie C Y Chan¹

¹Google DeepMind, Mountain View, CA, 94043 USA.

|July 17, 2024

Summary

This summary is machine-generated.

Large language models (LMs) show human-like reasoning biases, performing better when problem content aligns with logic. However, models and humans differ on complex tasks like the Wason selection task.

Keywords:

cognitive science content effects language models logic reasoning

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Area of Science:

Cognitive Science
Artificial Intelligence
Computational Linguistics

Background:

Abstract reasoning is crucial for intelligent systems, yet both humans and large language models (LMs) exhibit imperfections.
Human reasoning is influenced by real-world knowledge and beliefs, leading to 'content effects' where semantic content aids logical inference.
Understanding these content effects in LMs is key to understanding their cognitive parallels with humans.

Purpose of the Study:

To investigate whether large language models (LMs) exhibit content effects in their reasoning, similar to humans.
To compare LM and human performance across diverse logical reasoning tasks.
To explore the implications of these findings for human cognition and LM development.

Main Methods:

Evaluated state-of-the-art LMs and human participants on three reasoning tasks: natural language inference, syllogistic validity judgment, and the Wason selection task.
Analyzed accuracy patterns and lower-level features, such as the relationship between LM confidence and human response times.
Compared qualitative patterns of reasoning between LMs and humans across tasks.

Main Results:

LMs, like humans, demonstrate improved accuracy when the semantic content of a reasoning task supports logical inferences.
Parallels were observed in accuracy and in the relationship between LM confidence and human response times.
Significant differences emerged on the Wason selection task, where humans performed substantially worse than LMs and showed distinct error patterns.

Conclusions:

Large language models (LMs) mirror human 'content effects' in logical reasoning, suggesting shared mechanisms or influences.
The findings provide insights into the cognitive underpinnings of human content effects and the factors shaping LM reasoning.
Differences in performance, particularly on the Wason task, highlight areas where LM and human reasoning diverge, warranting further investigation.