据此发布了论文LIMA: Less Is More for Alignment。如标题所示,核心思想就是对于一个强大的LLM,在它预训练的时候就已经学到了绝大部分知识,后续只需要一些精品数据微调就足以让模型产生高质量的内容。不需要搞RLHF哪些复杂的东西。所以:Less Is More! [2305.11206] LIMA: Less Is More for Alignment (arxiv.org...
LIMA: Less Is More for Alignment是facebook 和 CMU 在2023年的一篇论文,由于结论比较显眼包所以热度挺高。 我认为几乎是极端条件下的对比实验了,结论挺有意思。 提出了一个 Superficial Alignment Hypothesis 表面对齐假设 表面一致性假说,他们认为: 大模型中几乎所有知识都是在预训练中学习的,指令微调只是一个很...
LIMA: Less Is More for Alignment O网页链接ChatPaper:这篇文章说明了大型语言模型的训练可以分为两个阶段,即无监督预训练和大规模的指导微调和强化学习。通过训练一个只用1,000个提示和响应数据就能获得出色表现的LIMA模型,研究表明大多数知识都是在预训练时学习的,只需要有限的指导微调数据就可以教会模型产生高质量...
模型全称 Less Is More for Alignment 模型简称 LIMA 模型类型 基础大模型 发布日期 2023-05-22 预训练文件大小 未知 是否支持中文(中文优化) 否 最高支持的上下文长度 2K 模型参数数量(亿) 650.0 模型代码开源协议 预训练结果开源商用情况 - 模型GitHub链接 ...
Paul Christianodistinguishes two main approaches for dealing with the alignment tax.[1][2] The first is to have the will to pay the tax, i.e. the relevant actors (corporations, governments, etc.) would be willing to pay the extra costs to avoid deploying a system until it is aligned. ...
One such advance, minimizers, allows for reducing the quantity of data handled while maintaining some of its key properties. We provide a basic introduction to minimizers, cover recent methodological developments, and review the diverse applications of minimizers to analyze genomic data, including de...
Moreover, it is for its time a distinctive mode of punctuation, less concerned to mark grammatical gradations and more sparing in the use of the comma than was customary in the later seventeenth century, but, paradoxically, free in... R Creaser - 《Review of English Studies》 被引量: 4...
DeepMind Alignment Team (according to Rohin Shah): Let’s contribute to a variety of projects and let our individual researchers pursue their own directions rather than having a unified agenda (note that DeepMind is also controversial for advancing AI capabilities; morehere). ...
discourse. Interest in Aumann agreement has waned in recent years within the Rationalist community, perhaps out of a sense Aumann agreement cannot be practically achieved by humans – there is too much background information to be exchanged. Instead, people now focus on things more like Double-...
Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data. In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65%...