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DataWorks 推出 ChatBI 能力，让业务分析师无需编写 SQL 或 Python，仅通过自然语言提问（如“上月销售额最高的区域是哪里？”），即可自动解析意图、生成查询逻辑并执行相应的python 或者 SQL任务。系统基于阿里云千问大模型，结合智能可视化引擎，自动生成图表与洞察，大幅降低数据分析门槛，让 Excel 用户也能轻松完成数据探索与决策支持。
，详情可参考同城约会
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Афганистан начал военную операцию против пакистанских военных вдоль всей непризнанной Кабулом границы между двумя государствами. Она стала ответом на бомбардировки ВВС Пакистана афганской территории.
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.