Papers with FinQA
DocFinQA: A Long-Context Financial Reasoning Dataset (2024.acl-short)
Copied to clipboard
| 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. |
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to generate reasoning programs that ignore the differences between facts treated all facts equally, leading to wrong punishment of programs that differed from the ground truth. |
| Approach: | They propose an optimized training framework for long-form numerical reasoning that incorporates a number-aware negative sampling strategy and consistency-based reinforcement learning to increase execution accuracy. |
| Outcome: | The proposed method improves the performance of long-form numerical reasoning on the FinQA and ConvFinQA leaderboards. |
Evaluating LLMs’ Mathematical Reasoning in Financial Document Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models excel in natural language understanding, but their capability for complex mathematical reasoning with a hybrid of structured tables and unstructured text remain uncertain. |
| Approach: | They propose a prompting technique tailored to semi-structured documents that matches or outperforms baselines performance while providing a nuanced understanding of LLMs' abilities. |
| Outcome: | The proposed prompting technique outperforms baseline prompting techniques while providing a nuanced understanding of LLMs' abilities. |
FinQA: A Dataset of Numerical Reasoning over Financial Data (2021.emnlp-main)
Copied to clipboard
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. |
FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning (2026.acl-long)
Copied to clipboard
Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Nenkov Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, Preslav Nakov
| Challenge: | Existing benchmarks emphasize final numerical answers while neglecting intermediate reasoning steps. |
| Approach: | They propose a symbolic benchmark for verifiable Chain-of-Thought evaluation in finance . FINCHAIN spans 58 topics across 12 financial domains and three difficulty levels . |
| Outcome: | The proposed benchmark aims to bridge symbolic reasoning and factual verification. |
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA (2023.emnlp-main)
Copied to clipboard
| Challenge: | In-Context Learning with Large Language Models (LLMs) has shown great performance on reasoning tasks. |
| Approach: | They propose a method for selecting a set of exemplars that is representative and diverse. |
| Outcome: | The proposed method outperforms existing methods on FinQA and TAT-QA on hybrid questions. |
Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval (2025.emnlp-main)
Copied to clipboard
| Challenge: | Numerical reasoning remains a challenging area for large language models (LLMs). |
| Approach: | They propose a two-step framework to enhance LLM's capabilities in financial numerical reasoning by using a generative retriever and context-aware program of thought prompting. |
| Outcome: | The proposed model surpasses previous benchmarks with execution accuracy improvements of 5.98% and 4.05%, respectively. |