Question answering over visually rich documents (VRDs) requires reasoning not only over isolated content but also over documents’ structural organization and cross-page dependencies. However, conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retrieve a fixed number of pages at inference, regardless of the specific demands of the question or context. This often results in incomplete evidence retrieval and degraded answer quality for multi-page reasoning tasks. To address these limitations, we propose LAD-RAG, a novel Layout-Aware Dynamic RAG framework. During ingestion, LAD-RAG constructs a symbolic document graph that captures layout structure and cross-page dependencies, adding it alongside standard neural embeddings to yield a more holistic representation of the document. During inference, an LLM agent dynamically interacts with the neural and symbolic indices to adaptively retrieve the necessary evidence based on the query. Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DocVQA demonstrate that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning, and outperforming baseline retrievers by up to 20% in recall at comparable noise levels, yielding higher QA accuracy with minimal latency.

Multi-hop question answering (MHQA) is a practical bottleneck in industry applications such as enterprise assistants, customer-support copilots, and compliance analysis, where systems must combine evidence across multiple documents before answering. Large language models (LLMs) remain brittle in this setting: iterative retrieval can commit too early to low-recall trajectories, while planning-only approaches can produce static query sets that fail to adapt when intermediate evidence changes. We propose Planned Active Retrieval and Reasoning RAG (PAR²-RAG), a training-free two-stage framework that separates coverage from commitment. PAR²-RAG first performs breadth-first anchoring to build a high-recall evidence frontier, then applies depth-first refinement with evidence sufficiency control in an iterative loop. This design targets deployment constraints by avoiding retraining cycles, reducing maintenance overhead under changing corpora, and improving scalability across domains. Across four MHQA benchmarks, PAR²-RAG consistently outperforms strong baselines: compared with IRCoT, it achieves up to 23.5% higher answer accuracy and up to 10.5% NDCG gains in retrieval quality.

“大语言模型问世以来,在自然语言处理诸多任务上都取得了惊人的表现。但其中可能存在的安全性和公平性问题也引起了人们的重视,特别是模型生成文本可能含有对特定职业、性别等群体的偏见和歧视。本文通过两种性别表征形式,构造了显性和隐性的”性别+职业“提示语,提示大语言模型生成开放性文本,并从情感极性、词汇丰富度和冒犯性程度三个维度对生成文本的偏见进行分析,评估并比较了传统模型与以ChatGPT为代表的大语言模型中的职业显性性别和隐性性别交叉偏见。结果表明,比起单维度的职业、性别身份信息,更复杂的职业性别交叉身份信息会减少ChatGPT生成文本中的偏见,具体表现为情感极性趋于中性,词汇丰富度提高;ChatGPT对于不同类型的职业性别身份展现出差异的态度,对研究型、艺术型等创造类的职业情感极性更高,对事务型、经管型等与人打交道的职业情感极性偏低;另外,ChatGPT相比之前的GPT-2模型在生成能力和消除偏见上有所进步,在多种组合身份提示下的生成文本更加积极、多样,冒犯性内容显著减少。”

“尽管悲观者认为,职场中永远不可能存在性别平等。但随着人们观念的转变,愈来愈多的人们相信,职业的选择应只与个人能力相匹配,而不应由个体的性别决定。目前已经发现自然语言处理的各个任务中都存在着职业性别偏见。但这些研究往往只针对特定的英文任务,缺乏针对中文的、综合多任务的职业性别偏见测量研究。本文基于霍兰德职业模型,从中文自然语言处理中常见的三个任务出发,测量了词向量、共指消解和文本生成中的职业性别偏见,发现不同任务中的职业性别偏见既有一定的共性,又存在着独特的差异性。总体来看,不同任务中的职业性别偏见反映了现实生活中人们对于不同性别所选择职业的刻板印象。此外,在设计不同任务的偏见测量指标时,还需要考虑如语体、词序等语言学要素的影响。”