A hybrid fine-tuning objective using KL divergence for token calibration and Kahneman-Tversky optimization for semantic binding enables LLMs to produce outputs that match desired attribute distributions across repeated prompts.
RLP rompt: Optimizing Discrete Text Prompts with Reinforcement Learning
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LLMs are highly sensitive to prompt formatting in few-shot settings, with accuracy varying by up to 76 points across formats; FormatSpread samples formats to report performance intervals without model weights.
iPOE derives and optimizes guidelines from explanations to create interpretable prompts, yielding up to 31% and 35% gains over standard and random-guideline prompts on four datasets.
citing papers explorer
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Controlling Distributional Bias in Multi-Round LLM Generation via KL-Optimized Fine-Tuning
A hybrid fine-tuning objective using KL divergence for token calibration and Kahneman-Tversky optimization for semantic binding enables LLMs to produce outputs that match desired attribute distributions across repeated prompts.
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Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
LLMs are highly sensitive to prompt formatting in few-shot settings, with accuracy varying by up to 76 points across formats; FormatSpread samples formats to report performance intervals without model weights.
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iPOE: Interpretable Prompt Optimization via Explanations
iPOE derives and optimizes guidelines from explanations to create interpretable prompts, yielding up to 31% and 35% gains over standard and random-guideline prompts on four datasets.