Update README.md

README.md

@@ -56,11 +56,18 @@ print(f"\n--- Model Output ---\n{full_text}")
 ## Citation
 If you use the FinBloom 7B LLM, please cite with the following BibTex entry:
 ```
-@article{sinha2025finbloom,
-  title={FinBloom: knowledge grounding large language model with real-time financial data},
-  author={Sinha, Ankur and Agarwal, Chaitanya and Malo, Pekka},
-  journal={arXiv preprint arXiv:2502.18471},
-  year={2025}
+@article{SINHA2026115559,
+title = {FinBloom: Knowledge-Grounding Large Language Model with Real-Time Financial Data},
+journal = {Knowledge-Based Systems},
+volume = {339},
+pages = {115559},
+year = {2026},
+issn = {0950-7051},
+doi = {https://doi.org/10.1016/j.knosys.2026.115559},
+url = {https://www.sciencedirect.com/science/article/pii/S0950705126003011},
+author = {Ankur Sinha and Chaitanya Agarwal and Pekka Malo},
+keywords = {Financial large language model, Generative pre-Trained transformer, Knowledge grounding, Natural language processing},
+abstract = {Large language models (LLMs) excel at generating human-like responses but often struggle with interactive tasks that require access to real-time information. This limitation poses challenges in finance, where models must access up-to-date information, such as recent news or price movements, to support decision-making. To address this, we introduce Financial Agent, a knowledge-grounding approach for LLMs to handle financial queries using real-time text and tabular data. Our contributions are threefold: First, we develop a Financial Context Dataset of over 50,000 financial queries paired with the required context. Second, we develop FinBloom 7B, a custom 7 billion parameter LLM, by fine-tuning Bloom 7B on 14 million financial news articles from Reuters and Deutsche Presse-Agentur (DPA), alongside a random sample of 25% from 12 million Securities and Exchange Commission (SEC) filings. Third, we fine-tune FinBloom 7B using the Financial Context Dataset to serve as a Financial Agent. This agent generates relevant financial context, enabling efficient real-time data retrieval to answer user queries. By reducing latency and eliminating the need for users to manually provide accurate data, our approach significantly enhances the capability of LLMs to handle dynamic financial tasks. Our proposed approach makes real-time financial decisions, algorithmic trading and other related tasks streamlined, and is valuable in contexts with high-velocity data flows.}
 }
 ```