# Economy Watchers Survey Provides Datasets and Tasks for Japanese Financial Domain

arXiv: https://arxiv.org/abs/2407.14727 · HTML: https://mlsanity.com/paper/2407.14727/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://mlsanity.com/paper/2308.11462/) (2023)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain](https://mlsanity.com/paper/2211.00083/) (2022)
- [Impact of News on the Commodity Market: Dataset and Results](https://mlsanity.com/paper/2009.04202/) (2020)
- [Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis](https://mlsanity.com/paper/2305.07972/) (2023)
- [Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks](https://mlsanity.com/paper/2305.05862/) (2023)
- [Construction of a Japanese Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2403.15062/) (2024)
- [Large Language Models are legal but they are not: Making the case for a powerful LegalLLM](https://mlsanity.com/paper/2311.08890/) (2023)
- [News-based Business Sentiment and its Properties as an Economic Index](https://mlsanity.com/paper/2110.10340/) (2021)

## Built on by (most cited first, 2 of 2)

- [Refined and Segmented Price Sentiment Indices from Survey Comments](https://mlsanity.com/paper/2411.09937/) (2024)
- [JFinTEB: Japanese Financial Text Embedding Benchmark](https://mlsanity.com/paper/2604.15882/) (2026)

## Abstract (arXiv)

Natural language processing (NLP) tasks in English and general domains are widely available and are often used to evaluate pre-trained language models. In contrast, fewer tasks are available for languages other than English and in the financial domain. Particularly, tasks in the Japanese and financial domains are limited. We develop two large datasets using data published by a Japanese central government agency. The datasets provide three Japanese financial NLP tasks, including 3- and 12-class classifications for categorizing sentences, along with a 5-class classification task for sentiment analysis. Our datasets are designed to be comprehensive and updated by leveraging an automatic update framework that ensures that the latest task datasets are publicly always available.