D-SCoRE: Document-Centric Segmentation and CoT Reasoning with Structured Export for QA-CoT Data Generation

• URL: http://arxiv.org/abs/2508.01309v1
• Date: Sat, 02 Aug 2025 10:45:05 GMT
• Title: D-SCoRE: Document-Centric Segmentation and CoT Reasoning with Structured Export for QA-CoT Data Generation
• Authors: Weibo Zhou, Lingbo Li, Shangsong Liang,
• Abstract summary: D-SCoRE is a training-free pipeline that produces high-quality QA datasets from arbitrary textual sources.<n>D-SCoRE generates six QA-CoT pairs with four-option counterfactual materials per 100-200-word text in 90 seconds.<n>Its simplicity and scalability enable efficient QA generation and high-performance fine-tuning across domains.
• Score: 12.271220269415878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The scarcity and high cost of high-quality question-answering (QA) datasets hinder supervised fine-tuning (SFT) for domain-specific large language models (LLMs). To address this, we introduce D-SCoRE, a training-free pipeline that utilizes LLMs and prompt engineering to produce diverse, high-quality QA datasets from arbitrary textual sources. D-SCoRE integrates $\textbf{D}$ocument-centric processing, $\textbf{S}$egmentation, $\textbf{Co}$T $\textbf{R}$easoning, and structured $\textbf{E}$xport to generate QA-COT datasets tailored for domain-aware SFT. Multi-dimensional control mechanisms, such as semantic role transformation, question type balancing, and counterfactual materials, enhance diversity and relevance, overcoming limitations of existing QA generation. LLMs fine-tuned on D-SCoRE-generated QA datasets, and human-annotated QA datasets (SQuAD, Covid-QA) are evaluated on SQuADShifts and Covid-QA test sets, with D-SCoRE outperforming across most domains. D-SCoRE generates six QA-CoT pairs with four-option counterfactual materials per 100-200-word text in 90 seconds using an 8B LLM on consumer-grade hardware. Its simplicity and scalability enable efficient QA generation and high-performance fine-tuning across domains.

Related papers

• SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting [16.86139440201837]
  We introduce SustainableQA, a novel dataset and a scalable pipeline for generating a comprehensive QA datasets from corporate sustainability reports and annual reports.<n>With over 195,000 diverse factoid and non-factoid QA pairs, SustainableQA is an effective resource for developing and benchmarking advanced knowledge assistants.
  arXiv  Detail & Related papers  (2025-08-05T02:03:59Z)
• Benchmarking Multimodal Understanding and Complex Reasoning for ESG Tasks [56.350173737493215]
  Environmental, Social, and Governance (ESG) reports are essential for evaluating sustainability practices, ensuring regulatory compliance, and promoting financial transparency.<n>MMESGBench is a first-of-its-kind benchmark dataset to evaluate multimodal understanding and complex reasoning across structurally diverse and multi-source ESG documents.<n>MMESGBench comprises 933 validated QA pairs derived from 45 ESG documents, spanning across seven distinct document types and three major ESG source categories.
  arXiv  Detail & Related papers  (2025-07-25T03:58:07Z)
• TQA-Bench: Evaluating LLMs for Multi-Table Question Answering with Scalable Context and Symbolic Extension [8.489816179329832]
  We present TQA-Bench, a new multi-table QA benchmark designed to evaluate the capabilities of large language models (LLMs) in tackling complex QA tasks over relational data.<n>Our benchmark incorporates diverse relational database instances sourced from real-world public datasets.<n>We systematically evaluate a range of LLMs, both open-source and closed-source, spanning model scales from 7 billion to 70 billion parameters.
  arXiv  Detail & Related papers  (2024-11-29T06:48:13Z)
• SEC-QA: A Systematic Evaluation Corpus for Financial QA [12.279234447220155]
  Existing datasets are often constrained by size, context, or relevance to practical applications. We propose SEC-QA, a continuous dataset generation framework with two key features. We introduce a QA system based on program-of-thought that improves the ability to perform complex information retrieval and quantitative reasoning pipelines.
  arXiv  Detail & Related papers  (2024-06-20T15:12:41Z)
• FinTextQA: A Dataset for Long-form Financial Question Answering [10.1084081290893]
  FinTextQA is a novel dataset for long-form question answering (LFQA) in finance. The most effective system configuration on our dataset involved setting the embedder, retriever, reranker, and generator as Ada2, Automated Merged Retrieval, Bge-Reranker-Base, and Baichuan2-7B.
  arXiv  Detail & Related papers  (2024-05-16T10:53:31Z)
• Automatic Question-Answer Generation for Long-Tail Knowledge [65.11554185687258]
  We propose an automatic approach to generate specialized QA datasets for tail entities. We conduct extensive experiments by employing pretrained LLMs on our newly generated long-tail QA datasets.
  arXiv  Detail & Related papers  (2024-03-03T03:06:31Z)
• QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation [67.27999343730224]
  We introduce an iterative bootstrapping framework for QA data augmentation (named QASnowball) QASnowball can iteratively generate large-scale high-quality QA data based on a seed set of supervised examples. We conduct experiments in the high-resource English scenario and the medium-resource Chinese scenario, and the experimental results show that the data generated by QASnowball can facilitate QA models.
  arXiv  Detail & Related papers  (2023-09-19T05:20:36Z)
• Long-Tailed Question Answering in an Open World [46.67715607552547]
  We define Open Long-Tailed QA (OLTQA) as learning from long-tailed distributed data. We propose an OLTQA model that encourages knowledge sharing between head, tail and unseen tasks. On a large-scale OLTQA dataset, our model consistently outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2023-05-11T04:28:58Z)
• Self-Prompting Large Language Models for Zero-Shot Open-Domain QA [67.08732962244301]
  Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models. We propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of Large Language Models.
  arXiv  Detail & Related papers  (2022-12-16T18:23:43Z)
• Parameter-Efficient Abstractive Question Answering over Tables or Text [60.86457030988444]
  A long-term ambition of information seeking QA systems is to reason over multi-modal contexts and generate natural answers to user queries. Memory intensive pre-trained language models are adapted to downstream tasks such as QA by fine-tuning the model on QA data in a specific modality like unstructured text or structured tables. To avoid training such memory-hungry models while utilizing a uniform architecture for each modality, parameter-efficient adapters add and train small task-specific bottle-neck layers between transformer layers.
  arXiv  Detail & Related papers  (2022-04-07T10:56:29Z)
• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs [62.71505254770827]
  We propose a conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts. Our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
  arXiv  Detail & Related papers  (2020-05-28T08:26:06Z)
• Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models [56.268862325167575]
  This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs) We leverage PLMs to address the strong token-to-token independence assumption made in the common objective, maximum likelihood estimation, for the CQR task. We evaluate fine-tuned PLMs on the recently-introduced CANARD dataset as an in-domain task and validate the models using data from the TREC 2019 CAsT Track as an out-domain task.
  arXiv  Detail & Related papers  (2020-04-04T11:07:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.