see that progress had been made, and declare the job done
这是大语言模型常见的“过度乐观”陷阱。模型倾向于迎合用户的完成预期,而非客观审视实际进度。通过强制读取结构化的feature list,是用外部状态锚定来对抗模型的内在偏见。
see that progress had been made, and declare the job done
这是大语言模型常见的“过度乐观”陷阱。模型倾向于迎合用户的完成预期,而非客观审视实际进度。通过强制读取结构化的feature list,是用外部状态锚定来对抗模型的内在偏见。
Rather than treating a complex document as a single monolithic task, Deep Extract deploys sub-agents to break it down and conquer each piece, which is what allows it to remain accurate even on documents with thousands of rows across hundreds of pages.
大多数人可能认为处理复杂文档的最佳方式是将其作为一个整体来处理,保持上下文完整性。但作者提出将复杂文档分解为多个子任务并由子代理分别处理的方法更有效,这一方法挑战了文档处理中'整体优于部分'的传统认知,暗示分解策略可能更适合处理超长文档。