Tag: Alignment

All the articles with the tag "Alignment".

• Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach

  Published: 7 May, 2025 |  at  09:32 AM

  

  74.12 🤔

  本文通过校准感知微调（CFT和RCFT）方法，结合可校准和不可校准区域的理论框架，显著改善了偏好对齐后大型语言模型的校准性能，同时维持或提升其语言能力。

• Reward-Augmented Data Enhances Direct Preference Alignment of LLMs

  Published: 7 May, 2025 |  at  12:16 AM

  

  70.15 🤔

  本文提出了一种奖励增强数据集方法，通过对偏好对进行重新标记使大型语言模型条件化于奖励值学习响应质量全谱，显著提升了直接偏好优化（DPO）的性能并缓解了其遗忘高质被拒响应和无差别学习低质选中响应的局限性。

• SIMPLEMIX: Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference Learning

  Published: 9 May, 2025 |  at  11:06 AM

  

  68.33 🤔

  This paper introduces SIMPLEMIX, a simple method to mix on- and off-policy data in language model preference optimization, demonstrating that their complementary strengths—on-policy for reasoning tasks and off-policy for open-ended tasks—lead to a 6.03% average improvement over single-source methods on Alpaca Eval 2.0.

• HAIR: Hardness-Aware Inverse Reinforcement Learning with Introspective Reasoning for LLM Alignment

  Published: 11 May, 2025 |  at  11:12 AM

  

  67.37 🤔

  HAIR introduces a novel LLM alignment method using hardness-aware inverse reinforcement learning and introspective reasoning, constructing a balanced safety dataset and training category-specific reward models with GRPO-S, achieving state-of-the-art harmlessness while preserving usefulness across multiple benchmarks.

• HyPerAlign: Hypotheses-driven Personalized Alignment

  Published: 4 May, 2025 |  at  04:32 PM

  

  65.01 🤔

  本文提出HyPerAlign方法，通过假设驱动的少样本学习实现LLM的个性化对齐，提高了模型对个体用户的适应性和安全性，同时减少了对微调的依赖。