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Analyzing Machine-Learned Representations: A Natural Language Case Study.

Ishita Dasgupta1, Demi Guo2, Samuel J Gershman3

  • 1Departments of Psychology and Computer Science, Princeton University.

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|December 19, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning models show human-like reasoning in language processing but also exhibit similar biases. Understanding these artificial intelligence (AI) systems offers insights into human cognition and AI development.

Keywords:
CompositionalityGeneralizationHeuristicNatural language inferenceRepresentation learningSentence embeddingsStrategiesTest datasets

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Modern deep networks approach human-like capabilities in specific domains.
  • Comparing AI and human representations and decision-making is crucial for understanding intelligence.

Purpose of the Study:

  • To investigate sentence representations in artificial intelligence (AI) for natural language processing.
  • To compare AI learning strategies and representations with human cognitive processes.

Main Methods:

  • Developed a diagnostic test dataset to assess abstract, composable structure.
  • Analyzed AI performance on diagnostic tests to identify representations and decision rules.
  • Investigated the impact of training distribution and data augmentation on AI learning.

Main Results:

  • AI systems exhibit a lack of systematicity and employ heuristic strategies.
  • Parallels were found between AI and human representations and generalization behaviors.
  • AI can learn abstract rules and generalize, similar to human zero-shot reasoning, but shows limitations akin to belief bias.

Conclusions:

  • Deep networks display both human-like generalization and cognitive biases.
  • Studying AI-human parallels can advance understanding of human psychology.
  • Findings inform strategies for developing AI with human-like language understanding.