Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
Comments EMNLP 2024 Main
AI 大模型
大模型对齐、安全、越狱、红队、提示注入和可信评测。
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
Comments EMNLP 2024 Main
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.AI
专题命中 偏好对齐 :alignment(title,abstract);safety(abstract);分类 cs.CL
Comments EMNLP 2024 Main Conference camera-ready version (fixed small typos). This article supersedes arXiv:2312.10665
专题命中 偏好对齐 :alignment(title,abstract);harmlessness(abstract);分类 cs.LG
专题命中 偏好对齐 :RLHF(title,abstract);alignment(abstract);分类 cs.LG
专题命中 偏好对齐 :DPO(title,abstract);RLHF(abstract);分类 cs.CL
Comments 20 pages, 12 figures, Accepted to COLM 2024
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
Comments ACL 2024 Main
专题命中 偏好对齐 :DPO(title,abstract);alignment(abstract);分类 cs.CL
专题命中 偏好对齐 :alignment(title,abstract);DPO(abstract);分类 cs.CL
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
Comments Accepted by ICML2024, I'm still preparing a better vision
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.LG
专题命中 偏好对齐 :alignment(title,abstract);分类 cs.CL、cs.AI、cs.CY
Comments The 2nd Workshop on Cross-Cultural Considerations in NLP (C3NLP) at ACL 2024
专题命中 偏好对齐 :alignment(title,abstract);DPO(abstract);分类 cs.CL
专题命中 偏好对齐 :alignment(title,abstract);DPO(abstract);分类 cs.CL
Comments 24 pages, 9 figures
Journal ref Forty-first International Conference on Machine Learning (ICML 2024)
专题命中 偏好对齐 :alignment(title);safety(abstract);分类 cs.CL、cs.AI、cs.CY
Comments 15 pages, 4 figures
专题命中 偏好对齐 :alignment(title,abstract);DPO(abstract);分类 cs.CL
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
专题命中 偏好对齐 :RLHF(title,abstract);alignment(abstract);分类 cs.CL
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.AI
专题命中 偏好对齐 :alignment(title);RLHF(abstract);safety(abstract);分类 cs.LG
Comments 11 pages, 5 figures
专题命中 偏好对齐 :alignment(title,abstract);safety(abstract);分类 cs.AI
Comments arXiv admin note: text overlap with arXiv:2110.09240 by other authors
Journal ref NeurIPS 2023 MP2 Workshop
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
Comments Published at NeurIPS 2023
专题命中 偏好对齐 :alignment(title,abstract);RLHF(abstract);分类 cs.CL
Comments Preprint
专题命中 偏好对齐 :RLHF(title,abstract);alignment(abstract);分类 cs.CL
Comments Preprint
专题命中 偏好对齐 :DPO(title,abstract);分类 cs.CL、cs.AI、cs.LG
Comments Code, data, and models are available at https://github.com/dvlab-research/Step-DPO
专题命中 偏好对齐 :alignment(title,abstract);分类 cs.CL、cs.AI、cs.LG
Comments Accepted at ICML 2024 Workshop on Models of Human Feedback for AI Alignment, Vienna, Austria
专题命中 偏好对齐 :alignment(title,comments);RLHF(abstract);分类 cs.CL、cs.AI、cs.LG
Comments Previous Title: SALMON: Self-Alignment with Principle-Following Reward Models. Accepted to ICLR 2024. Project page: https://github.com/IBM/SALMON
如何使用你的专家预算:蛋白质结构预测模型的实用指南
机构 * InstaDeep Ltd(InstaDeep公司) ; University of Oxford(牛津大学) ; Lancaster University(兰卡斯特大学)
专题命中 偏好对齐 :DPO(summary_cn,abstract);分类 cs.AI、cs.LG
AI总结 本研究针对蛋白质结构预测模型的专家预算约束,通过基准测试FK-steering、DPO、Best K-of-N采样及O3方法,明确不同预算下的最优方法并给出实用选择建议。
Comments Proceedings of the ICML 2026 Workshop on Structured Probabilistic Inference & Generative Modeling (SPIGM)