Automatic Inter-document Multi-hop Scientific QA Generation
AIM-SciQA employs large language models to automatically generate 13,672 multi-hop scientific QA pairs across documents, enhancing cross-document reasoning.
Key Findings
Methodology
The AIM-SciQA framework integrates large language models (e.g., Qwen-7B-Instruct) with embedding-based semantic alignment and citation information to automate multi-document scientific question answering data generation. It involves selecting relevant papers from PubMed Central, extracting single-hop QA pairs via instruction-tuned LLMs, and constructing cross-document relations using MedEmbedlarge-v0.1 embeddings. Relations are built based on semantic similarity of questions and citation links, enabling the automatic creation of complex multi-hop questions. The process includes filtering, relation validation, and multi-step reasoning chain formation, resulting in a high-quality dataset of 13,672 multi-hop QA pairs.
Key Results
- Applied to 8,211 biomedical papers, the method generated 411,409 single-hop and 13,672 multi-hop QA pairs, demonstrating scalability and diversity in scientific domains.
- Human and automatic evaluations confirmed high factual consistency (94% perfect scores) and reasoning quality, with models effectively distinguishing reasoning stages.
- The citation-guided variant CIM-SciQA achieved performance comparable to the oracle setting, validating the importance of citation relations in multi-hop reasoning.
Significance
This work addresses the scarcity of large-scale, high-quality multi-hop scientific QA datasets, enabling robust training and evaluation of models capable of complex reasoning across scientific literature. It advances AI’s ability to synthesize knowledge from multiple sources, supporting scientific discovery, literature review automation, and intelligent research assistants, thus significantly impacting academia and industry.
Technical Contribution
The paper introduces a novel automated pipeline combining semantic embedding alignment and citation information for multi-document relation construction, enabling large-scale generation of multi-hop QA pairs. It also proposes a new dataset, IM-SciQA, and a citation-guided variant, CIM-SciQA, setting new standards for scientific QA data resources and evaluation benchmarks.
Novelty
This is the first work to fully automate the creation of large-scale, multi-hop scientific QA datasets spanning multiple documents using embedding-based semantic matching and citation relations. It moves beyond previous single-document or template-based approaches, enabling more realistic and complex reasoning scenarios.
Limitations
- The approach relies heavily on pre-trained embeddings and citation networks, which may not generalize well to emerging or less-connected scientific fields, potentially limiting coverage.
- Generated QA pairs, despite validation, can still contain semantic inaccuracies or factual errors, requiring manual review for critical applications.
- The current focus on biomedical literature limits cross-domain applicability; extending to other scientific disciplines remains a challenge.
Future Work
Future directions include integrating multi-modal data (images, tables), expanding to other scientific domains, improving factual verification mechanisms, and developing interactive systems that incorporate real-time literature updates to support dynamic scientific inquiry.
AI Executive Summary
In the rapidly evolving landscape of scientific research, the ability to perform complex reasoning across multiple documents remains a significant challenge. Existing datasets predominantly focus on single-document factoid questions, which fall short of capturing the intricate reasoning processes scientists undertake when synthesizing knowledge from diverse sources. To bridge this gap, this study introduces AIM-SciQA, an innovative framework leveraging large language models (LLMs) and semantic embedding techniques to automatically generate large-scale multi-hop scientific question-answering datasets.
The core of AIM-SciQA involves selecting relevant biomedical literature from PubMed Central, extracting single-hop QA pairs using instruction-tuned LLMs like Qwen-7B-Instruct, and constructing cross-document relations based on semantic similarity of questions and citation links. The embedding-based relation construction employs MedEmbedlarge-v0.1, which encodes question semantics to identify related sections across papers. Citation information further guides the relation building, enabling the automatic assembly of complex multi-hop questions that require integrating evidence from multiple sources.
Applying this pipeline to over 8,000 biomedical papers, the authors generated a dataset comprising 411,409 single-hop and 13,672 multi-hop QA pairs. Human and automated evaluations confirmed the high factual consistency and reasoning quality of these pairs. The introduction of a citation-guided variant, CIM-SciQA, demonstrated that leveraging citation networks can achieve performance comparable to oracle-based methods, underscoring the importance of citation relations in scientific reasoning.
This dataset offers a valuable resource for training and benchmarking AI systems capable of multi-document scientific reasoning. It addresses a critical bottleneck in AI-driven scientific discovery, literature synthesis, and intelligent research assistants. Despite its strengths, the approach depends on the quality of embeddings and citation data, which may limit coverage in less-connected fields. Future work aims to incorporate multi-modal data, expand to other disciplines, and enhance factual verification, pushing the boundaries of automated scientific reasoning.
Deep Analysis
Background
科学问答技术经历了从单文档理解到跨文档推理的演变。早期如PubMedQA和BioASQ主要关注医学文献的事实提取，近年来出现多跳和跨文档场景，但数据规模有限，难以支撑复杂推理模型。现有数据集多依赖人工标注，成本高昂，自动化生成技术逐渐兴起，但多跳科学问答仍缺乏大规模、自动化、高质量的数据资源。这限制了模型在真实科研场景中的应用，也阻碍了多跳推理能力的提升。
Core Problem
当前科学问答研究面临数据匮乏、跨文档推理复杂、自动生成质量难控等难题。尤其是在多跳场景下，如何自动构建具有真实性和多样性的大规模数据集成为瓶颈。这限制了模型在实际科研中的应用潜力，也影响了多跳推理能力的提升。人工标注成本高、效率低，难以满足快速发展的科研需求，自动化生成的问答质量参差不齐，亟需一种高效、可靠的方法解决这一难题。
Innovation
本文提出结合大模型和语义嵌入技术的自动多跳问答生成框架，创新点包括：1）利用大模型（如Qwen-7B-Instruct）自动提取每篇论文的单跳问答；2）引入MedEmbedlarge-v0.1编码器进行语义匹配，构建跨文档关系；3）结合引文信息，自动生成多跳问答。该方法突破了人工标注的限制，显著提升数据规模和多样性，推动科学推理研究的边界。
Methodology
- �� 论文筛选：从PubMed Central筛选2019-2024年发表的医学论文，确保完整元数据和引文信息。
- �� 单跳问答提取：利用指令调优的LLMs（如Qwen-7B-Instruct）自动从每篇论文中提取关键问答对，确保事实一致。
- �� 关系构建：通过语义嵌入匹配（MedEmbedlarge-v0.1）和引文关系，识别不同论文间的潜在联系。
- �� 多跳问答生成：结合单跳问答，设计多步骤推理链，自动生成跨文档的复杂问答。
- �� 关系验证：人工和自动筛查确保问答的质量和合理性。
- �� 数据集构建：最终生成13,672个多跳问答，覆盖医学领域的复杂推理场景。
Experiments
采用PubMed Central文献作为数据源，评估生成问答的质量和多样性。比较不同的检索模型（如LlamaIndex、Faiss）在文献匹配中的表现，使用Hit@1、MRR等指标衡量检索效果。模型在自动问答任务中的表现通过人工评估和自动指标（如BERTScore）验证，验证其在复杂推理中的应用潜力。还进行了引文关系的有效性分析和多跳问答的质量评估。
Results
数据集规模达13,672个多跳问答，覆盖医学文献中的复杂推理场景。自动生成的问答在人工验证中表现出高事实一致性（达94%的满意度），模型在检索和推理任务中表现优异，显著优于基线方法。引文引导的CIM-SciQA在多跳推理中表现接近理想状态，验证了引文关系的有效性。整体结果表明，该方法能有效提升科学问答系统的推理能力。
Applications
该数据集可用于训练和评估科学问答模型，推动跨文献推理、知识图谱构建和科研辅助工具的发展。适合科研机构、AI公司开发智能文献检索和推理系统，也可用于教育和科研培训，提升科研效率和知识整合能力。
Limitations & Outlook
模型在特定领域（如新兴学科）表现仍有限，自动生成的问答可能存在语义偏差和事实错误。依赖预训练模型和引文关系，可能在跨学科或多模态场景中效果不足。未来需结合知识图谱、多模态信息和人工校验，提升数据质量和模型鲁棒性。
Plain Language Accessible to non-experts
想象你在图书馆里查找信息。每本书代表一篇科学论文，里面有许多事实和结论。你需要从不同的书中找到相关内容，然后把它们结合起来，回答一个复杂的问题。比如，你想知道两本书中关于某个疾病的研究结果，单靠一本书可能不够。你得看多本书，把它们的内容联系起来，才能得到完整答案。这个过程就像科学家们在不同论文中寻找线索，最后拼出一个完整的故事。AIM-SciQA就像一个聪明的助手，能自动帮你找到相关的书，理解它们的内容，然后帮你解答复杂的问题。它用强大的AI模型，自动阅读、理解、连接不同的科学文献，就像你用放大镜仔细观察每本书的细节，然后把所有线索拼成一个完整的答案。这大大节省了科学家们的时间，也让科研变得更高效、更智能。
ELI14 Explained like you're 14
想象你在学校图书馆找资料。你有一个很难的问题，比如“为什么某个药物对某种疾病有效？”单看一本书可能还不够，你得看很多书，把它们的内容结合起来，才能找到答案。这个过程很复杂，因为每本书都讲不同的细节，你要理解它们之间的关系，然后拼凑出完整的答案。AIM-SciQA就像一个超级聪明的助手，它可以自动帮你找相关的书，理解里面的内容，然后帮你回答这个复杂的问题。它用最先进的AI技术，就像你用放大镜仔细观察每个细节，然后把所有信息拼在一起，得到一个完整的答案。这让科学家们不用费那么多时间去翻阅大量文献，就能快速得到答案，科研变得更简单、更快了！
Abstract
Existing automatic scientific question generation studies mainly focus on single-document factoid QA, overlooking the inter-document reasoning crucial for scientific understanding. We present AIM-SciQA, an automated framework for generating multi-document, multi-hop scientific QA datasets. AIM-SciQA extracts single-hop QAs using large language models (LLMs) with machine reading comprehension and constructs cross-document relations based on embedding-based semantic alignment while selectively leveraging citation information. Applied to 8,211 PubMed Central papers, it produced 411,409 single-hop and 13,672 multi-hop QAs, forming the IM-SciQA dataset. Human and automatic validation confirmed high factual consistency, and experimental results demonstrate that IM-SciQA effectively differentiates reasoning capabilities across retrieval and QA stages, providing a realistic and interpretable benchmark for retrieval-augmented scientific reasoning. We further extend this framework to construct CIM-SciQA, a citation-guided variant achieving comparable performance to the Oracle setting, reinforcing the dataset's validity and generality.