slide window会把语义切割,而LLM本身已经学到了文本的结构信息,这种分割反而会导致语义信息的丢失。苏剑林提出一个解决方案,就是前几层做window attention,最后一层做full attention。zhuanlan.zhihu.com/p/63这篇文章也给出了类似结论,全部都是slide window attention肯定是不行的。 4.2.2 衰减会丢失衰减应该是连贯...
query_min_slide_window:查询词在某个字段上命中的分词词组个数与该词组在字段上最小窗口的比值 时效性 timeliness: 时效分,用于衡量文档的新旧程度,单位为秒 timeliness_ms: 时效分,用于衡量文档的新旧程度,单位为毫秒 功能性 tag_match: 用于对查询语句和文档做标签匹配,使用匹配结果对文档进行算分加权 first_pha...
(mask, diagonal=-sliding_window) # actually produces a large column of slide window size mask = torch.triu(mask, diagonal=-sliding_window+1) mask = torch.log(mask) print(mask) tensor([[0., -inf, -inf, -inf, -inf], [0., 0., -inf, -inf, -inf], [0., 0., 0., -inf, ...
如上图所示,我们在最前面的三层使用Full Cache机制,即存储所有的KV,三层以上的层则通过Sliding Window自动替换打分最低的KV,通过这种方法就可以灵活的指定各个层所需要的内存空间,即节约了空间,又最大化了稀疏算法的效果。 • 稀疏化计算阶段,此阶段往往发生在模型执行之后,通过计算得到的稀疏化打分去执行稀疏操作,...
🚀[Jan 2024] Introduce Modular RAG and RAG Flow. [Part Ⅰ] [Part II] 🚀[Dec 2023] Release RAG Survey. (last updated Mar 2024) Material: [Slide] If you find our survey useful for your research, please cite the following paper: @misc{gao2024retrievalaugmented, title={Retrieval-Augmente...
I recently gave a talk at a Microsoft-internal event on everything I learned (so far) about grounding LLMs with Retrieval Augmented Generation and other...
(ICL), which facilitate an increase in prompt length. In some instances, prompts now extend to tens of thousands of tokens, or units of text, and beyond. While longer prompts hold considerable potential, they also introduce a host of issues, such as the need to exceed...
https://docs.google.com/presentation/d/15jlAz0pOmybTxAzywzXklBcL1DLvQy50/edit#slide=id.p1 xLLM has been using knowledge graphs since the very beginning: taxonomies, related concepts (related links found in any large repository such as Wikipedia or corporate, that you can extract to enrich yo...
作者基于DIN-SQL的实践:LLM在中文Text2SQL的实践 二. 问题 针对上期优化的结果，我们做了进一步的数据...
title=”Mark Prepared from Unprepared”>Mark PreparedView 20/20 unprepared letters输出类似