LLMs can make fair predictions on tabular data through optimized prompting and example selection
This paper introduces four effective approaches to improve fairness in LLM predictions on tabular data while maintaining high performance. The methods include fair prompt optimization, soft prompt tuning, strategic few-shot example selection, and self-refining predictions through chain-of-thought reasoning.
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https://arxiv.org/abs/2412.04642
🤔 Original Problem:
LLMs show bias when making predictions on tabular data, producing unfair outcomes across demographic groups. Traditional debiasing methods for natural language tasks don't work well for tabular data fairness.
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🛠️ Solution in this Paper:
→ The paper implements fair prompt optimization using meta-LLMs to generate fairness-specific instructions
→ It develops soft prompt tuning by optimizing tokens in embedding space with fairness penalties
→ It creates strategic few-shot examples by selecting nearest neighbors and manipulating positive/negative example ratios
→ It introduces self-refinement where LLMs analyze and adjust their own predictions to achieve demographic parity
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💡 Key Insights:
→ Fairness in tabular tasks differs from NLP fairness - it focuses on class label distributions across groups
→ Simple prompt engineering can significantly improve fairness without sacrificing accuracy
→ Strategic selection of in-context examples outperforms random selection for fairness
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📊 Results:
→ Fair prompt optimization achieved 0.94-0.99 demographic parity ratio across datasets
→ Strategic few-shot examples maintained 90%+ accuracy while improving fairness by 30-40%
→ Self-refinement improved demographic parity by 20-30% with minimal accuracy loss
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