Datasets:
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: context
dtype: string
- name: answer_1
dtype: string
- name: answer_2
dtype: string
- name: subreddit
dtype: string
- name: num_comments
dtype: int64
- name: score
dtype: int64
- name: upvote_ratio
dtype: float64
- name: ups
dtype: float64
- name: downs
dtype: float64
- name: author
dtype: string
- name: created_utc
dtype: int64
- name: retrieved_on
dtype: float64
- name: retrieved_utc
dtype: float64
- name: id
dtype: string
- name: comment_is_submission
dtype: bool
- name: score_answer1
dtype: int64
- name: upvote_ratio_answer1
dtype: 'null'
- name: ups_answer1
dtype: float64
- name: downs_answer1
dtype: float64
- name: author_answer1
dtype: string
- name: name_answer1
dtype: string
- name: id_answer1
dtype: string
- name: created_utc_answer1
dtype: int64
- name: retrieved_on_answer1
dtype: float64
- name: retrieved_utc_answer1
dtype: float64
- name: score_answer2
dtype: int64
- name: upvote_ratio_answer2
dtype: 'null'
- name: ups_answer2
dtype: float64
- name: downs_answer2
dtype: float64
- name: author_answer2
dtype: string
- name: name_answer2
dtype: string
- name: id_answer2
dtype: string
- name: created_utc_answer2
dtype: int64
- name: retrieved_on_answer2
dtype: float64
- name: retrieved_utc_answer2
dtype: float64
splits:
- name: train
num_bytes: 116393440
num_examples: 53061
- name: test
num_bytes: 1076885
num_examples: 500
download_size: 73381121
dataset_size: 117470325
task_categories:
- question-answering
language:
- en
tags:
- finance
size_categories:
- 10K<n<100K
Dataset Card for Dataset Name
SocialFinanceQA is the first social-media preference dataset for fine-tuning and aligning LLMs for the finance domain.
Dataset Details
Dataset Description
- Curated by: Kris-Fillip Kahl, Tolga Buz, Russa Biswas, Gerard de Melo
- Language(s) (NLP): English
- License: MIT
Dataset Sources
- Repository: https://github.com/kris-fillip/SocialFinanceQA
Uses
This dataset is intended to be used for fine-tuning and aligning LLMs for the task of Long Form Question Answering (LFQA).
Direct Use
This dataset is suitable for supervised fine-tuning as well as preference alignment of LLMs.
Dataset Structure
The dataset consists of questions from submissions and corresponding answers from comments within the selected subreddits. It is a preference dataset constructed based on the scores (upvotes and downvotes) of the individual submissions and comments within their subreddits. All fields entailing answer_1 correspond to the preferred answer for a specific question, while the answer_2 fields correspond to the non-preferred answer.
Dataset Creation
Curation Rationale
Retail investing is on the rise, and a growing number of users are relying on online finance communities to educate themselves. However, recent years have positioned Large Language Models (LLMs) as powerful question-answering (QA) tools, shifting users away from interacting in communities toward discourse with AI-driven conversational interfaces. These AI tools are currently limited by the availability of labeled data containing domain-specific financial knowledge. Therefore, in this work, we curate a QA preference dataset SOCIALFINANCEQA for fine-tuning and aligning LLMs, extracted from over 7.4 million submissions and 82 million comments from 2008 to 2022 in Reddit’s 15 largest finance communities.
Source Data
The data originally stems from Reddit submissions and comments made to 15 of the largest financial subreddits (personalfinance, financialindependence, FinancialPlanning, investing, wallstreetbets, Wallstreetbetsnew, stocks, StockMarket, pennystocks, options, RealEstate, Economics, realestateinvesting, AskEconomics, explainlikeimfive) between 2008 and 2022.
Data Collection and Processing
Who are the source data producers?
Reddit users who contributed to the selected financial subreddits between 2008 and 2022.
Personal and Sensitive Information
The dataset contains the usernames of the authors of the texts. These user names are pseudonyms that do not contain information about the author's identity unless the author actively decides to share personal information in their Reddit profile. We have not obtained dedicated permission from the authors of the texts to include them in our dataset. However, this is mitigated by the users agreeing to Reddit’s terms, which allow data extraction services such as Pushshift, which is the source for our dataset and was accessed legitimately. Additionally, the texts may contain the names of individuals, usually public figures relevant to the stock market, whom the authors included in their questions or responses. We have decided to leave these names in the dataset, as they are usually critical for understanding the text (e.g. when a company’s CEO is mentioned in a text about how the same company performs).
Bias, Risks, and Limitations
Social media should never be the only source for investors planning to make financial decisions. We have chosen 15 communities covering various topics to provide a broad foundation. Nonetheless, we recommend retail investors consult other sources, such as relevant books, scientific literature, and trained professionals, before making significant investment decisions. Investing often bears the risk of losing money, so investors should be careful with money they cannot afford to lose. Inappropriate, toxic, or offensive language is always a risk when working with social media data. We mitigate this by filtering our dataset carefully, leveraging Reddit’s internal mechanisms and additional techniques, such as toxicity detection. Nonetheless, there is a possibility that our dataset still harbors remaining instances of text that may be considered offensive or may cause fine-tuned LLMs to generate such.
Dataset Card Authors
Kris-Fillip Kahl, Tolga Buz