AI Judgement: Mixtral 8x7B vs Llama 3 8B

Rationality and Logic: Mixtral 8x7B ≳ Llama 3

• Both models demonstrate strong logical analysis and reasoning
• Mixtral shows better handling of ethical dilemmas and syllogisms
• Llama occasionally struggles with complex ethical scenarios

Impartiality: Mixtral 8x7B ≳ Llama 3

• Both models generally maintain objectivity
• Both correctly identify need to recuse in clear conflict of interest situations
• Llama has more difficulty with nuanced scenarios and subtle biases

Deterrence and Marginality: Mixtral 8x7B ⨉ Llama 3

• Inconsistent handling of truly marginal cases
• Often fail to consider deterrence effects in judgments
• Struggle with explicitly stating when differences are marginal

Consistency: Mixtral 8x7B ≳ Llama 3

• Both demonstrate good consistency in applying ethical principles
• Both maintain consistent reasoning over time
• Llama shows occasional inconsistency across related cases

Ethical Considerations: Mixtral 8x7B ≛ Llama 3

• Generally identify key ethical issues and provide thoughtful analysis
• Show commitment to fairness and addressing bias
• Occasionally struggle with prioritizing ethical considerations in complex scenarios

Transparency and Justification: Mixtral 8x7B ≛ Llama 3

• Llama sometimes gets carried away into irrelevant avenues

Conclusion: Mixtral 8x7B ≳ Llama 3

In this AI Field Trial for judgment tasks, Mixtral 8x7B Instruct demonstrates a slight edge over Llama 3 8B Instruct in Rationality and Logic, Impartiality, and Consistency. Both models perform equally well in Ethical Considerations, showing strong capabilities in identifying and analyzing ethical issues. However, both models underperform in Deterrence and Marginality, indicating a clear area for improvement in handling marginal cases and considering deterrence effects. While Mixtral shows a marginal advantage in several areas, both models demonstrate strong overall performance in AI judgment tasks, with specific strengths and areas for improvement identified for each.