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language model alignment

166 papers

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reinforcement learning from human feedback (58) large language model (52) direct preference optimization (45) preference optimization (31) reinforcement learning (31) reward model (29) preference learning (29) model alignment (25) reward modeling (23) language model (19)

Papers

Noise Contrastive Alignment of Language Models with Explicit Rewards NeurIPS 2024 On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization EMNLP 2024 On the Relationship between Truth and Political Bias in Language Models EMNLP 2024 ORPO: Monolithic Preference Optimization without Reference Model EMNLP 2024 Perplexity-aware Correction for Robust Alignment with Noisy Preferences NeurIPS 2024 Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization NeurIPS 2024 Preference Learning Algorithms Do Not Learn Preference Rankings NeurIPS 2024 Privately Aligning Language Models with Reinforcement Learning ICLR 2024 Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer NeurIPS 2024 RAIN: Your Language Models Can Align Themselves without Finetuning ICLR 2024 Reformatted Alignment EMNLP 2024 Rethinking the Role of Proxy Rewards in Language Model Alignment EMNLP 2024 Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment EMNLP 2024 Reward Difference Optimization For Sample Reweighting In Offline RLHF EMNLP 2024 Reward Modeling Requires Automatic Adjustment Based on Data Quality EMNLP 2024 RLCD: Reinforcement Learning from Contrastive Distillation for LM Alignment ICLR 2024 Safer-Instruct: Aligning Language Models with Automated Preference Data NAACL 2024 SALMON: Self-Alignment with Instructable Reward Models ICLR 2024 Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels NeurIPS 2024 Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness EMNLP 2024 Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting EACL 2024 Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation EMNLP 2024 Statistical Rejection Sampling Improves Preference Optimization ICLR 2024 The Accuracy Paradox in RLHF: When Better Reward Models Don’t Yield Better Language Models EMNLP 2024 The Paradox of Preference: A Study on LLM Alignment Algorithms and Data Acquisition Methods NAACL 2024