RLHF可能真的算是整个LLM比价难和吃纯技术的一个方向,相比于pretrain更多的来源于对混沌未知的洞见(除了scaling law以外,可能还没有特别有指导性的工作),SFT来源于精细化处理和更多的人工干预,那么RLHF则是需要多年的技术积累和丰富的理论保证。个人从16年开始做RL,包括理论的,学术的,业务线的RL都做过很多,之前...
为了满足这两种条件,InteRecAgent使用SQL工具处理硬条件,从项目数据库中找到候选项目;对于软条件,采用项目到项目工具,基于潜在嵌入匹配相似项目。 项目排序(Item Ranking): 在对话中,通过分析用户的历史数据和对话中表达的偏好,排名工具对用户进行个性化推荐。排名模块设计为分析用户的历史和对话中提到的具体兴趣,将这些信...
Beyond ranking, MMLU checks if a model can transfer knowledge between areas, crucial for adaptable AI. Its challenging tasks push developers to create smarter systems, ensuring models are not just impressive on paper but also ready to tackle real-world problems where knowledge and reasoning matter....
Preference datasets: These datasets typically contain several answers with some kind of ranking, which makes them more difficult to produce than instruction datasets. Proximal Policy Optimization: This algorithm leverages a reward model that predicts whether a given text is highly ranked by humans. This...
Master of Laws (LLM) in Shipping Law University of Cape Town - Faculty of Law - Department of Commercial Law, ranked n°10 at Eduniversal Bests Masters Ranking
Skill Distillation: We delve into the enhancement of specific cognitive abilities, such as context following, alignment, agent, NLP task specialization, and multi-modality. Verticalization Distillation: We explore the practical implications of KD across diverse fields, including law, medical & healthcare...
Document Ranking with a Pretrained Sequence-to-Sequence Model 将T5类型的Seq2Seq模型用于检索重排, 这就是这篇论文最大的贡献.方法: 对于任务, 序列输入为: Query: {q} Document: {d} Relevant:, 其中{q}为查询文本占位符, {d}为文档占位符. 序列的输出/标签为:label \in [true,false]\. 由此继续模...
Preference datasets: These datasets typically contain several answers with some kind of ranking, which makes them more difficult to produce than instruction datasets. Proximal Policy Optimization: This algorithm leverages a reward model that predicts whether a given text is highly ranked by humans. This...
Among the potential outputs, we can ask human annotators for a quality ranking (i.e., which output is the “best”). Using this dataset of ranked model outputs, we can train a smaller LLM (6 billion parameters) that has undergone supervised fine-tuning to output a scalar reward giv...
在最后筛选保留Top-K洞察的部分,论文还加入了Ranking环节,说是排序但看实现上,更像是消重+相似度过滤+打散。 首先洞察之间两两消重,如果A洞察包含B洞察的内容,则删除B洞察 其次是相似度过滤,会过滤和用户提问关联较低的洞察。不过这里其实有些存疑,因为洞察存在维度下钻和多维度对比,似乎感觉相似度不太合适作为过滤...