✨Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization

📝 Summary:
This paper introduces adaptive text anonymization, a framework that uses prompt optimization to automatically adjust anonymization strategies for language models. It adapts to varying privacy-utility requirements across diverse domains, achieving a better trade-off than baselines. It is efficient...

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20743
• PDF: https://arxiv.org/pdf/2602.20743

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#TextAnonymization #Privacy #PromptOptimization #LLM #NLP

🔥 Self-Supervised Prompt Optimization

💡 The paper proposes a self supervised framework called Self Supervised Prompt Optimization that optimizes prompts for large language models without requiring external references. The problem addressed is that manually designed prompts require expertise and iterative experimentation, while existing prompt optimization methods rely heavily on external references such as ground truth or human evaluation, which can be costly to obtain. The proposed method derives evaluation and optimization signals purely from output comparisons, where a large language model evaluator selects superior prompts through pairwise output comparisons, and a large language model optimizer aligns outputs with task requirements. The results show that the proposed method outperforms state of the art prompt optimization methods, achieving comparable or superior results with significantly lower costs and fewer samples, demonstrating its effectiveness and efficiency. The method can optimize prompts for both closed and open ended tasks, and can be applied in real world scenarios where external references are unavailable or costly to obtain.

📅 Published on Feb 7, 2025

🔗 Links:
• arXiv: https://arxiv.org/abs/2502.06855
• PDF: https://arxiv.org/pdf/2502.06855
• GitHub: https://github.com/geekan/metagpt ⭐ 67.7k

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/XiangJinYu/SPO
• https://huggingface.co/spaces/tang-x/SPO
• https://huggingface.co/spaces/ositamiles/SPO

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📢 By: https://xn--r1a.website/PaperNexus

#SelfSupervisedLearning #PromptOptimization #LargeLanguageModels #NaturalLanguageProcessing #LanguageModelEvaluation

🔥 GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

💡 The paper introduces GEPA, a prompt optimizer that uses natural language reflection to learn high level rules from trial and error, outperforming reinforcement learning methods. The problem addressed is that current reinforcement learning methods, such as Group Relative Policy Optimization, require thousands of rollouts to learn new tasks, which can be time consuming and inefficient. The authors argue that the interpretable nature of language can provide a richer learning medium for large language models compared to policy gradients derived from sparse scalar rewards.

The method used is GEPA, a Genetic-Pareto prompt optimizer that incorporates natural language reflection to learn high level rules from trial and error. GEPA samples system level trajectories, reflects on them in natural language to diagnose problems, proposes and tests prompt updates, and combines complementary lessons from its own attempts. This approach allows GEPA to turn even a few rollouts into a large quality gain.

The results show that GEPA outperforms Group Relative Policy Optimization by 10 percent on average and by up to 20 percent, while using up to 35 times fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10 percent across two large language models. Additionally, GEPA demonstrates promising results as an inference time search strategy for code optimization. Overall, the paper contributes a new approach to prompt optimization that can efficiently learn high level rules from trial and error, outperforming current reinforcement learning methods.

📅 Published on Jul 25, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2507.19457
• PDF: https://arxiv.org/pdf/2507.19457
• Project Page: https://gepa-ai.github.io/gepa/

🤖 Models citing this paper:
• https://huggingface.co/pirola/local-ai-coding-stack-research

📊 Datasets citing this paper:
• https://huggingface.co/datasets/zhongweixie/A-Survey-on-AI-Agent-Harness

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📢 By: https://xn--r1a.website/PaperNexus

#NaturalLanguageReflection #PromptOptimization #ReinforcementLearningAlternatives #GeneticParetoOptimization #LanguageModelLearning