迁移到LLM这一块,对于pre-training的时候语料的处理,可以做ranking,包括fine-tune的时候做continual learning、active learning等等,理论上可做的事以及相应的效果应该也是相近的。 关于data augmentation方面,之前看过一篇在LLM训练过程中往intermediate feature加高斯噪声的工作,证明了能够带来性能提升。结论其实是比较有趣...
multi-attribute regression reward model:相对pairwise ranking models在基模型结构/训练目标上有区别(比如Nemotron-4-340B-Reward单独做一套结构,或者换个loss比如用带得分的数据做回归训练),直接输出score;回归模型更擅长预测细粒度奖励,Nemotron-4-340B-Reward在Nemotron-4-340B-Base模型基础上构建,通过用一个新...
Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令攻防 指令诱导 (诱导模型输出目标答案,from SuperCLUE) * 有害指令注入 (将真实有害意图注入到...
Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令攻防 指令诱导 (诱导模型输出目标答案,from SuperCLUE) 有害指令注入 (将真实有害意图注入...
DPO 可以执行 token 级信用分配的研究,参阅论文《From r to Q∗: Your language model is secretly a Q-function》,报道《这就是 OpenAI 神秘的 Q*?斯坦福:语言模型就是 Q 函数》。 TDPO,token 级 DPO,参阅论文《Token-level direct preference...
LiPO，逐列表偏好优化，参阅论文《LIPO: Listwise preference optimization through learning-to-rank》。RRHF，参阅论文《RRHF: Rank responses to align language models with human feedback without tears》。PRO，偏好排名优化，参阅论文《Preference ranking optimization for human alignment》。负偏好优化 这些研究有...
Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令 指令诱导 (诱导模型输出目标答案,from SuperCLUE) 有害指令注入 (将真实有害意图注入到prom...
Building on the solid foundation of its predecessors, Llama 4 introduces groundbreaking features that set it apart in terms of performance, efficiency, and versatility. Let’s break down what makes this model a true game-changer. Evolution from Llama 2 and Llama 3 ...
Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令攻防 指令诱导 (诱导模型输出目标答案,from SuperCLUE) ...
prompt -> Add few shot -> Add simple retrieval -> Fine-tune model -> Add HyDE retrieval + fact-checking step -> Add RAG content to training examples. 翻译翻译: prompt 工程 -> 进阶 prompt 工程 -> 简单 RAG -> 微调模型 -> 进阶 RAG -> 带着 RAG 样本微调模型 ...