---
title: "ConvFinQA: Exploring the Chain of Numerical — Chen 2022"
description: "The paper asks whether current models can follow multi-step arithmetic that unfolds across a dialogue, rather than within one isolated question."
authors: ["Zhiyu Chen", "Shiyang Li", "Charese Smiley", "Zhiqiang Ma", "Sameena Shah", "William Yang Wang"]
year: 2022
venue: "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2022.emnlp-main.421/"
doi: "10.18653/v1/2022.emnlp-main.421"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2022-convfinqa-exploring-chain-numerical-reasoning/"
---
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# ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering

**Authors:** [Zhiyu Chen](https://md.research.elamur.ai/free/en/authors/zhiyu-chen/), [Shiyang Li](https://md.research.elamur.ai/free/en/authors/shiyang-li/), [Charese Smiley](https://md.research.elamur.ai/free/en/authors/charese-smiley/), Zhiqiang Ma, [Sameena Shah](https://md.research.elamur.ai/free/en/authors/sameena-shah/), [William Yang Wang](https://md.research.elamur.ai/free/en/authors/william-yang-wang/) · **Year:** 2022 · **Venue:** Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.emnlp-main.421/) · [PDF](https://aclanthology.org/2022.emnlp-main.421.pdf) · [DOI 10.18653/v1/2022.emnlp-main.421](https://doi.org/10.18653/v1/2022.emnlp-main.421) · [OpenAlex](https://openalex.org/W4385573185)

**Citations:** 70 citations (openalex-snapshot · 2026-10-14) · [101](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper asks whether current models can follow multi-step arithmetic that unfolds across a dialogue, rather than within one isolated question. The setting is finance: analysts often question an earnings report turn by turn, and later questions build on earlier answers. The authors introduce ConvFinQA, built from the existing FinQA dataset. They split FinQA's multi-hop reasoning programs into smaller steps. Sometimes they also join the two programs written for the same report. This produces conversation "skeletons", and expert annotators then wrote a natural question for each turn. Models must output a program in the FinQA domain-specific language that yields the answer to the final turn.

The authors test two families of systems: fine-tuned neural symbolic models and few-shot prompting of GPT-3. Both stay well below human experts. GPT-3 tends to copy the reasoning in its exemplars or fall back on generic arithmetic knowledge. The work shows that realistic, domain-specific reasoning chains remain difficult for large language models.

## Key points
- ConvFinQA has 3,892 conversations with 14,115 questions, drawn from financial reports with text and tables. The data and code are released at github.com/czyssrs/ConvFinQA.
- There are two conversation types:
  - Type I (simple) comes from decomposing a single FinQA question.
  - Type II (hybrid) concatenates the decompositions of two questions about the same report.
- Number-lookup turns are inserted into both types.
- Models are scored by execution accuracy and program accuracy. Every model scores below 70% execution accuracy, while human experts reach 89.4%.
- GPT-3 (175B) prompting scores below 50%. It does better with familiar calculation notation (Program-normal) than with the original DSL format. Chain-of-thought prompts performed worse than generating the program alone.
- GPT-3 performs worst on number-selection turns and on hybrid conversations, especially their second part. This suggests that resolving references to earlier turns is a central weakness.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

101 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 97 not yet.

- [Evaluating Large Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
- [When Tables Go Crazy: Evaluating Multimodal Models on French Financial Documents](https://md.research.elamur.ai/free/en/papers/mouilleron-2026-when-tables-go-crazy-evaluating/)
- [Merging Continual Pretraining Models for Domain-Specialized LLMs: A Case Study in Finance](https://md.research.elamur.ai/free/en/papers/ueda-2026-merging-continual-pretraining-models-domain/)
- [BloombergGPT: A Large Language Model for Finance](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{chen2022convfinqa,
  title = {{ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering}},
  author = {Chen, Zhiyu and Li, Shiyang and Smiley, Charese and Ma, Zhiqiang and Shah, Sameena and Wang, William Yang},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  year = {2022},
  doi = {10.18653/v1/2022.emnlp-main.421},
  url = {https://doi.org/10.18653/v1/2022.emnlp-main.421}
}
```

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