No One Left Behind: Cross-Level Analysis for Sustainable Software Engineering
本文提出可持续性反模式概念，以解决软件工程中跨层级的可持续性问题，并通过识别、形式化、检测和缓解这些反模式来指导未来的研究。
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Selected work
Representative Papers
LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
This study addresses the cumbersome and resource-intensive manual review process for collateral eligibility in securities prospectuses, which is challenged by lengthy, semi-structured documents often mixing German and English. The work proposes a generative information extraction framework—novel in its application of large language models (LLMs) to central bank securities eligibility assessment—that decomposes the task into extraction, standardization, and explanation stages to effectively handle OCR noise and multilingual interference. It introduces an innovative value-based LLM-as-a-judge evaluation approach, overcoming limitations of conventional position-based metrics. The system achieves 91% precision at the document-level eligibility decision, demonstrating conservative and reliable performance with significantly reduced risk of erroneously accepting ineligible securities.
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Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
This study addresses cross-lingual causal relation extraction in English–Spanish financial texts through a question-answering framework to enable causal inference. It systematically evaluates the performance of multilingual large language models—including mBERT, mBART, Llama 3.1, and the GPT series—across encoder, encoder-decoder, and decoder-only architectures, offering the first comparative analysis of prompt engineering, few-shot learning, and supervised fine-tuning strategies for financial causal extraction. Experimental results demonstrate that supervised fine-tuning on combined English and Spanish data substantially enhances cross-lingual transfer performance. Notably, the fine-tuned GPT-4.1 Mini model achieves joint top rank on the English subtask (4.8140) and third place on the Spanish subtask (4.7753), underscoring the effectiveness of task-specific fine-tuning.
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A Survey on Spatio-Temporal Knowledge Graph Models
Current spatiotemporal knowledge graph (STKG) models suffer from fundamental limitations—including conceptual fragmentation, terminological inconsistency, poor reusability, and inadequate support for long-term knowledge preservation—arising from their disparate foundations in static, temporal, and spatial graph paradigms. To address these issues, this work introduces the first multidimensional analytical framework encompassing edge semantics, spatiotemporal annotation, and semantic modeling. Through a systematic literature review and cross-dimensional comparative analysis, we clarify the theoretical evolution of STKGs. We further propose a general-purpose modeling guideline explicitly designed for long-term knowledge preservation, establish standardized design principles, and distill six key open challenges. Our contributions provide both theoretical foundations and practical pathways for transitioning STKGs from application-specific solutions toward universal, sustainable knowledge infrastructure.
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Recent publications
Latest Papers
No One Left Behind: Cross-Level Analysis for Sustainable Software Engineering
本文提出可持续性反模式概念，以解决软件工程中跨层级的可持续性问题，并通过识别、形式化、检测和缓解这些反模式来指导未来的研究。
0 citationsRead paper
LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
This study addresses the cumbersome and resource-intensive manual review process for collateral eligibility in securities prospectuses, which is challenged by lengthy, semi-structured documents often mixing German and English. The work proposes a generative information extraction framework—novel in its application of large language models (LLMs) to central bank securities eligibility assessment—that decomposes the task into extraction, standardization, and explanation stages to effectively handle OCR noise and multilingual interference. It introduces an innovative value-based LLM-as-a-judge evaluation approach, overcoming limitations of conventional position-based metrics. The system achieves 91% precision at the document-level eligibility decision, demonstrating conservative and reliable performance with significantly reduced risk of erroneously accepting ineligible securities.
0 citationsRead paper
Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
This study addresses cross-lingual causal relation extraction in English–Spanish financial texts through a question-answering framework to enable causal inference. It systematically evaluates the performance of multilingual large language models—including mBERT, mBART, Llama 3.1, and the GPT series—across encoder, encoder-decoder, and decoder-only architectures, offering the first comparative analysis of prompt engineering, few-shot learning, and supervised fine-tuning strategies for financial causal extraction. Experimental results demonstrate that supervised fine-tuning on combined English and Spanish data substantially enhances cross-lingual transfer performance. Notably, the fine-tuned GPT-4.1 Mini model achieves joint top rank on the English subtask (4.8140) and third place on the Spanish subtask (4.7753), underscoring the effectiveness of task-specific fine-tuning.
0 citationsRead paper
A Survey on Spatio-Temporal Knowledge Graph Models
Current spatiotemporal knowledge graph (STKG) models suffer from fundamental limitations—including conceptual fragmentation, terminological inconsistency, poor reusability, and inadequate support for long-term knowledge preservation—arising from their disparate foundations in static, temporal, and spatial graph paradigms. To address these issues, this work introduces the first multidimensional analytical framework encompassing edge semantics, spatiotemporal annotation, and semantic modeling. Through a systematic literature review and cross-dimensional comparative analysis, we clarify the theoretical evolution of STKGs. We further propose a general-purpose modeling guideline explicitly designed for long-term knowledge preservation, establish standardized design principles, and distill six key open challenges. Our contributions provide both theoretical foundations and practical pathways for transitioning STKGs from application-specific solutions toward universal, sustainable knowledge infrastructure.
0 citationsRead paper