Papers with financial question answering
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework (2024.lrec-main)
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| Challenge: | Currently, ML&DL methods fail to provide reasons for stock trend predictions, lacking interpretability and reasoning processes. large language models (LLMs) suffer from hallucinations and are unable to keep up with the latest information. |
| Approach: | They develop a method to train large language models to handle financial analysis tasks . they use AlphaFin datasets to compare performance with traditional methods . |
| Outcome: | The proposed method improves stock trend prediction and financial question answering tasks. |
David vs. Goliath: Cost-Efficient Financial QA via Cascaded Multi-Agent Reasoning (2025.findings-emnlp)
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| Challenge: | Large language models have demonstrated remarkable reasoning capabilities, but performance in FQA remains limited. |
| Approach: | They propose a low-cost yet effective framework that enables small LLMs to perform complex reasoning tasks without expensive models. |
| Outcome: | The proposed framework outperforms the best open-source model on BizBench by 10.46% and achieves competitive performance to GPT-3.5 using significantly fewer parameters. |
FinTextQA: A Dataset for Long-form Financial Question Answering (2024.acl-long)
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| Challenge: | Existing financial question answering datasets lack scope diversity and question complexity. |
| Approach: | They propose to use a dataset for long-form question answering in finance to evaluate QA systems. |
| Outcome: | The proposed dataset includes 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites. |
What Factors Affect LLMs and RLLMs in Financial Question Answering? (2026.findings-acl)
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| Challenge: | Recent studies have focused on large language models and reasoning large language model (RLLMs) however, there are few studies that explore what methods can fully unlock the performance of LLMs and RLLM in the financial domain. |
| Approach: | They examine the effects of prompting methods, agentic frameworks, and multilingual alignment methods on financial question-answering tasks. |
| Outcome: | The results show that prompting methods and agent frameworks improve LLMs' performance . the authors suggest that these frameworks can be used to enhance LLM performance if they are implemented in financial domains. |
SAHM: A Benchmark for Arabic Financial and Shari’ah-Compliant Reasoning (2026.acl-long)
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Rania Elbadry, Sarfraz Ahmad, Ahmed Heakl, Dani Bouch, Momina Ahsan, Muhra AlMahri, Marwa Elsaid Khalil, Yuxia Wang, Salem Lahlou, Sophia Ananiadou, Veselin Stoyanov, Jimin Huang, Xueqing Peng, Preslav Nakov, Zhuohan Xie
| Challenge: | English financial NLP has progressed rapidly through benchmarks for sentiment, document understanding, and financial question answering. |
| Approach: | They propose a document-grounded benchmark and instruction-tuning dataset for Arabic financial NLP and Shari’ah-compliant reasoning. |
| Outcome: | The proposed dataset contains 14,380 expert-verified instances spanning seven tasks . it includes financial sentiment analysis, extractive summarization, and event–cause reasoning . |