We introduce and make publicly available the NIFTY Financial News Headlines
dataset, designed to facilitate and advance research in financial market
forecasting using large language models (LLMs). This dataset comprises two
distinct versions tailored for different modeling approaches: (i) NIFTY-LM,
which targets supervised fine-tuning (SFT) of LLMs with an auto-regressive,
causal language-modeling objective, and (ii) NIFTY-RL, formatted specifically
for alignment methods (like reinforcement learning from human feedback (RLHF))
to align LLMs via rejection sampling and reward modeling. Each dataset version
provides curated, high-quality data incorporating comprehensive metadata,
market indices, and deduplicated financial news headlines systematically
filtered and ranked to suit modern LLM frameworks. We also include experiments
demonstrating some applications of the dataset in tasks like stock price
movement and the role of LLM embeddings in information acquisition/richness.
The NIFTY dataset along with utilities (like truncating prompt's context length
systematically) are available on Hugging Face at
this https URL

介绍并公开提供了 NIFTY 金融新闻头条数据集，旨在促进和推进利用大型语言模型（LLMs）进行金融市场预测的研究。该数据集包含两个针对不同建模方法的版本：NIFTY-LM 和 NIFTY-RL。每个版本都提供了经过筛选和排名、附带全面元数据、市场指数的高质量数据，以适应现代 LLM 框架。同时还包括了一些应用实例，如股价走势预测以及 LLM 嵌入在信息获取与丰富性中的角色。