微信扫码
添加专属顾问
我要投稿
不同子方面数量的子集实验,RichRAG框架在所有子集上都优于所有基线,框架在多样化搜索场景中的鲁棒性
Your task is to adjust the results of query-facets mining. The query-facets are extensions of theoriginal query in various generic perspectives, rather than some specific facts. Given a query thatrequires information from multiple query-facets, you should return all query-facets of the queryto fully answer it query. Note that each query has at least two query-facets. I will give you thelong-form answer to the original query to help you explore query-facets based on the perspectivesof its answer. But refrain from using the additional information from the answer to generate thequery-facets. Then you should segment the original long-form answer into several sub-answersthat each are paired with a query-facet. Please return each query-facet of the original query and itscorresponding sub-answers. The query-facets and sub-answers should be one-to-one and returnedin JSON format. You need to follow the following rules:1. The answers are only used to help you determine the generic direction. You mustn’t generatequery-facets based on the contents of answers and the facets mustn’t contain the answers’additional information beyond the input query.2. Sub-answers are constructed by segmenting the original answer, you cannot generate or reorderthe original answer to create sub-answers.3. The sub-answers should be complete. You must ensure that when the sub-answers are joinedtogether in order, the complete original answer should be formed.4. The generated query-facets should be sufficiently generic and contain no specific informationabout the sub-answers.5. **You should ensure that generated query-facets cover all perspectives original answer.**6. **You should ensure that all sub-answers cover all contents of the original answer.**7. **The number of query surfaces must range from 2 to 7.**8. **You should ensure that each query-facet is sufficiently generic and can be easily derived fromthe original query.**9. **You should ensure each query-facet contains no information from the answer.**10. **You should rewrite or combine the query-facets to be more generic if some query-facets donot meet the above requirements.**11. The returned results should be in JSON format and contain the following key: results, whichis a list of JSON data. Each item of results should contain the following keys: query-facet, andsub-answer.12. I will give you some demonstrations, you should learn the pattern of them to mine query-facetsand split sub-answers.**Demonstration**{demonstrations}Query: {query}Answer: {answer}Results:对于RAG整个框架的更多技术,PaperAgent团队RAG专栏进行过详细的归纳总结:高级RAG之36技(术)。
高级RAG之36技试看私信获取:RAG专栏 RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generationhttps://arxiv.org/pdf/2406.12566
53AI,企业落地大模型首选服务商
产品:场景落地咨询+大模型应用平台+行业解决方案
承诺:免费POC验证,效果达标后再合作。零风险落地应用大模型,已交付160+中大型企业
2026-06-23
RAG之后,知识库开始自己长大
2026-06-23
AI 知识库开始分叉:LLM Wiki 和 GBrain 真正的差别
2026-06-23
谷歌发布OKF(Open Knowledge Format)规范,它与Karpathy的LLM-wiki是什么关系?
2026-06-23
RAG 的尽头,是 SQL?
2026-06-22
传统RAG已经落伍了?清华大神开源的这个 rag-skill,让知识库检索直接升维
2026-06-22
从个人知识库到企业级 RAG:我们最终选了 WeKnora
2026-06-22
RAG 不是先向量检索再回答:Metadata Filter 才是企业知识库的第一道门
2026-06-21
使用 LangSmith 进行 RAG 评估:构建生产级 RAG 系统的 AI 开发者指南
2026-04-06
2026-04-27
2026-04-02
2026-03-31
2026-04-23
2026-04-20
2026-04-09
2026-04-12
2026-04-22
2026-04-10
2026-06-23
2026-06-23
2026-06-15
2026-06-10
2026-06-10
2026-05-20
2026-05-18
2026-05-11