Representation Without Control: Testing the Realization Effect in Language Models
📰 ArXiv cs.AI
Learn how to test the realization effect in language models to understand when their outputs reflect human-like cognitive mechanisms
Action Steps
- Evaluate LLM behavior at multiple levels: prompt-only, realized gains and losses, and risk-taking
- Test the realization effect in language models using behavioral economics frameworks
- Analyze the outputs of LLMs to determine when they reflect human-like cognitive mechanisms rather than surface patterns
- Apply the findings to improve the development of more human-like language models
- Compare the results with human behavioral data to validate the effectiveness of the approach
Who Needs to Know This
NLP researchers and AI engineers can benefit from this knowledge to improve the development of more human-like language models
Key Insight
💡 The realization effect can be used to evaluate when language models exhibit human-like cognitive mechanisms, rather than just prompt-sensitive surface patterns
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🤖 New research on testing the realization effect in language models to understand human-like cognitive mechanisms #LLMs #NLP
Key Takeaways
Learn how to test the realization effect in language models to understand when their outputs reflect human-like cognitive mechanisms
Full Article
Title: Representation Without Control: Testing the Realization Effect in Language Models
Abstract:
arXiv:2605.25151v1 Announce Type: new Abstract: Large language models are increasingly used as behavioral simulators, but it remains unclear when their outputs reflect human-like cognitive mechanisms rather than prompt-sensitive surface patterns. We study this question through the realization effect, a well-characterized finding in behavioral economics in which risk-taking differs systematically after paper versus realized gains and losses. We evaluate LLM behavior at three levels: prompt-only b
Abstract:
arXiv:2605.25151v1 Announce Type: new Abstract: Large language models are increasingly used as behavioral simulators, but it remains unclear when their outputs reflect human-like cognitive mechanisms rather than prompt-sensitive surface patterns. We study this question through the realization effect, a well-characterized finding in behavioral economics in which risk-taking differs systematically after paper versus realized gains and losses. We evaluate LLM behavior at three levels: prompt-only b
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