| Challenge: | Existing work on automating financial numerical reasoning focuses on unrealistically specific document snippets, failing to reflect the broader and more realistic scenarios faced by analysts. |
| Approach: | They propose a long-document financial QA task that augments 7,437 questions from existing FinQA dataset with full-document context, extending the average context length from under 700 words in FinQA to 123k words in DocFinQA. |
| Outcome: | The proposed task extends the average context length from under 700 words in FinQA to 123k words in DocFinQA. |
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FinQA: A Dataset of Numerical Reasoning over Financial Data (2021.emnlp-main)
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Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, William Yang Wang
| Challenge: | Popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge. |
| Approach: | They propose a large-scale dataset with Question-Answering pairs over financial reports written by financial experts to facilitate analytical progress. |
| Outcome: | The proposed dataset is the first of its kind and is available on github. |
Systematic Evaluation of Long-Context LLMs on Financial Concepts (2024.emnlp-industry)
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| Challenge: | Long-context large language models (LC LLMs) are promising for tasks with long context windows . however, their ability to reliably utilize their growing context windows remains under investigation . |
| Approach: | They evaluate the performance of long-context large language models using a real-world financial news dataset. |
| Outcome: | The proposed models exhibit brittleness at longer context lengths even for simple tasks, the authors show . they advocate for more rigorous evaluation of LC LLMs by employing holistic metrics such as F1 (rather than recall) |
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. |
FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on simple attribution that retrieves textual evidence as references. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to generate reliable attributions. |
| Outcome: | The proposed benchmark evaluates the ability of LLMs to generate long-form answers with reliable and nuanced attributions. |
LLMs Cannot (Yet) Match the Specificity and Simplicity of Online Communities in Long Form Question Answering (2024.findings-emnlp)
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| Challenge: | Recent years have positioned Large Language Models (LLMs) as powerful question answering (QA) tools, shifting users away from interacting in communities towards discourse with AI-driven conversational interfaces. |
| Approach: | They propose to use a QA preference dataset to fine-tune and align Large Language Models (LLMs) from more than 7.4 million submissions and 82 million comments from 2008 to 2022 in Reddit’s 15 largest finance communities. |
| Outcome: | The proposed framework improves on the social quality of the data, and the proposed framework is more accurate and more specific. |
Large Language Model Agents in Finance: A Survey Bridging Research, Practice, and Real-World Deployment (2025.findings-emnlp)
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Yifei Dong, Fengyi Wu, Kunlin Zhang, Yilong Dai, Sanjian Zhang, Wanghao Ye, Sihan Chen, Zhi-Qi Cheng
| Challenge: | a systematic review of large language models (LLMs) is conducted to better align their capabilities with real-world demands. |
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Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. |
| Approach: | They propose a synthetic dataset in the financial domain that integrates Chain-of-Thought reasoning into LLMs in a supervised manner to facilitate effective long-context understanding. |
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LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA (2025.emnlp-main)
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| Challenge: | Existing Question Answering systems are limited by noisy documents and flawed QA pairs. |
| Approach: | They propose a high-quality subset of NarrativeQA focused on literary works . they identify and correct low-quality QA samples while removing extraneous text . |
| Outcome: | The proposed subset of NarrativeQA is based on literary works. |
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. |
Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are expensive to develop and maintain and require extensive feature engineering to perform. |
| Approach: | They propose a two-stage approach that serializes a compact subset of numeric/categorical attributes into natural language and performs retrieval-augmented in-context learning over label-aware, instance-level exemplars. |
| Outcome: | The proposed approach significantly improves F1/MCC over direct prompting and is competitive with strong tabular baselines in several settings. |