Artificial intelligence has become increasingly prevalent in the legal domain. However, LegalAI systems often struggle with vague user queries that lack essential legal details, leading to suboptimal performance in practical applications. To address this challenge, we propose FactFiller, a novel approach that dynamically generates questionnaires to help users refine their input queries. Our method leverages an iterative training process that collects valuable questionnaires, eliminating the need for human annotation. Additionally, we introduce a "case-law-quiz” cascading retrieval process, ensuring that the generated questions and answer options are directly linked to specific legal provisions. Through the user study and the downstream task experiments, we demonstrate that FactFiller, while remaining easy for non-experts to understand, not only improves the completeness of queries but also ensures the performance of various domain-specific models in downstream legal tasks.

The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of multi-modal large language models ((M)LLMs), this dream is closer to reality, as (M)LLM-based Agents using computers, mobile phones and web browsers by operating within the environments and interfaces (e.g., Graphical User Interface (GUI) and Command Line Interface (CLI)) provided by operating systems (OS) to automate tasks have significantly advanced. This paper presents a comprehensive survey on these advanced agents, designated as OS Agents. We begin by elucidating the fundamentals of OS Agents, exploring their key components and capabilities. We then examine methodologies for constructing OS Agents, focusing on domain-specific foundation models and agent frameworks. A detailed review of evaluation metrics and benchmarks highlights how OS Agents are assessed across diverse platforms and tasks. Finally, we discuss current challenges and identify promising directions for future research. An open-source GitHub repository is maintained as a dynamic resource to foster further innovation in this field.