LLMs default to responses more similar to opinions from the USA and some European and South American countries; prompting for a country shifts alignment but can introduce stereotypes, while translation does not reliably match language speakers.
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Reasoning models expend more tokens on association-incompatible tasks than compatible ones, indicating greater effort on counter-stereotypical information, except for Claude 3.7 Sonnet which shows the reverse pattern linked to its bias-focused reasoning.
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
A survey reviewing benchmark data contamination in LLMs, its impact on evaluation, and alternative assessment approaches.
citing papers explorer
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Towards Measuring the Representation of Subjective Global Opinions in Language Models
LLMs default to responses more similar to opinions from the USA and some European and South American countries; prompting for a country shifts alignment but can introduce stereotypes, while translation does not reliably match language speakers.
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Implicit Bias-Like Patterns in Reasoning Models
Reasoning models expend more tokens on association-incompatible tasks than compatible ones, indicating greater effort on counter-stereotypical information, except for Claude 3.7 Sonnet which shows the reverse pattern linked to its bias-focused reasoning.
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Ethical and social risks of harm from Language Models
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
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Benchmark Data Contamination of Large Language Models: A Survey
A survey reviewing benchmark data contamination in LLMs, its impact on evaluation, and alternative assessment approaches.