KG-QAGen: A Knowledge-Graph-Based Framework for Systematic Question Generation and Long-Context LLM Evaluation

The increasing context length of modern language models has created a need for evaluating their ability to retrieve and process information across extensive documents. While existing benchmarks test long-context capabilities, they often lack a structured way to systematically vary question complexity. We introduce KG-QAGen (Knowledge-Graph-based Question-Answer Generation), a framework that (1) extracts QA pairs at multiple complexity levels (2) by leveraging structured representations of financial agreements (3) along three key dimensions -- multi-hop retrieval, set operations, and answer plurality -- enabling fine-grained assessment of model performance across controlled difficulty levels. Using this framework, we construct a dataset of 20,139 QA pairs (the largest number among the long-context benchmarks) and open-source a part of it. We evaluate 13 proprietary and open-source LLMs and observe that even the best-performing models are struggling with set-based comparisons and multi-hop logical inference. Our analysis reveals systematic failure modes tied to semantic misinterpretation and inability to handle implicit relations.

Language Modeling for the Future of Finance: A Quantitative Survey into Metrics, Tasks, and Data Opportunities

Recent advances in language modeling have led to growing interest in applying Natural Language Processing (NLP) techniques to financial problems, enabling new approaches to analysis and decision-making. To systematically examine this trend, we review 374 NLP research papers published between 2017 and 2024 across 38 conferences and workshops, with a focused analysis of 221 papers that directly address finance-related tasks. We evaluate these papers across 11 qualitative and quantitative dimensions, identifying key trends such as the increasing use of general-purpose language models, steady progress in sentiment analysis and information extraction, and emerging efforts around explainability and privacy-preserving methods. We also discuss the use of evaluation metrics, highlighting the importance of domain-specific ones to complement standard machine learning metrics. Our findings emphasize the need for more accessible, adaptive datasets and highlight the significance of incorporating financial crisis periods to strengthen model robustness under real-world conditions. This survey provides a structured overview of NLP research applied to finance and offers practical insights for researchers and practitioners working at this intersection.