FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial
  Reasoning
Multi-step symbolic reasoning is critical for advancing downstream
performance on financial tasks. Yet, benchmarks for systematically evaluating
this capability are lacking. Existing datasets like FinQA and ConvFinQA
supervise only final numerical answers, without assessing intermediate
reasoning steps. To address this, we introduce FinChain, the first symbolic
benchmark designed for verifiable Chain-of- Thought (CoT) financial reasoning.
Spanning 54 topics across 12 financial domains, Fin- Chain offers five
parameterized templates per topic, each varying in reasoning complexity and
domain expertise required. Each dataset instance includes an executable Python
trace, enabling automatic generation of extensive training data and easy
adaptation to other domains. We also introduce ChainEval, a new metric for
automatic evaluation of both final answers and intermediate reasoning.
Benchmarking 30 LLMs on our dataset, we find that even state-of-the-art models
have considerable room for improvement in multi-step financial reasoning. All
templates and evaluation metrics for FinChain are available at https:
//github.com/mbzuai-nlp/finchain.
6841704275b84c68e75dd774
Zhuohan Xie
claimed_verified
2025-06-05T12:41:57.397Z
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/avatars/eea38e90c3595eab1d7e0fdb7418b0d5.svg
Zhuohan Xie
user
Zhuohan
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Dhruv Sahnan
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Debopriyo Banerjee
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Georgi Georgiev
6841704275b84c68e75dd778
Rushil Thareja
6841704275b84c68e75dd779
Hachem Madmoun
6841704275b84c68e75dd77a
Jinyan Su
6841704275b84c68e75dd77b
Aaryamonvikram Singh
claimed_verified
2025-09-12T16:24:57.612Z
670f8d8801f1ec4d87e3f1f2
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Aaryamonvikram Singh
user
aaryamonvikram
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Yuxia Wang
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Rui Xing
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Fajri Koto
6841704275b84c68e75dd77f
Haonan Li
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Ivan Koychev
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Tanmoy Chakraborty
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Salem Lahlou
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Veselin Stoyanov
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Preslav Nakov
claimed_verified
2025-07-09T15:10:33.641Z
647f7eb25e1bc4753746bf9f
/avatars/cc9c6210fdc822d8a106937e747dff41.svg
Preslav Nakov
user
preslavnakov
FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial
  Reasoning
Multi-step symbolic reasoning is critical for advancing downstream
performance on financial tasks. Yet, benchmarks for systematically evaluating
this capability are lacking. Existing datasets like FinQA and ConvFinQA
supervise only final numerical answers, without assessing intermediate
reasoning steps. To address this, we introduce FinChain, the first symbolic
benchmark designed for verifiable Chain-of- Thought (CoT) financial reasoning.
Spanning 54 topics across 12 financial domains, Fin- Chain offers five
parameterized templates per topic, each varying in reasoning complexity and
domain expertise required. Each dataset instance includes an executable Python
trace, enabling automatic generation of extensive training data and easy
adaptation to other domains. We also introduce ChainEval, a new metric for
automatic evaluation of both final answers and intermediate reasoning.
Benchmarking 30 LLMs on our dataset, we find that even state-of-the-art models
have considerable room for improvement in multi-step financial reasoning. All
templates and evaluation metrics for FinChain are available at https:
//github.com/mbzuai-nlp/finchain.
A new benchmark called FinChain evaluates multi-step symbolic reasoning in financial tasks with a focus on intermediate reasoning steps, introducing ChainEval as a metric for assessing both final answers and reasoning processes.
symbolic reasoning FinQA ConvFinQA Chain-of-Thought (CoT) FinChain multi-step financial reasoning parameterized templates reasoning complexity domain expertise executable Python trace ChainEval LLMs