---
title: Multi-Task Learning in Financial NLP
url: https://www.ml-quant.com/papers/ssrn/4640029/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4640029
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4640029
featured: 2023-11-29
citations: unknown
topic: LLMs & Text
---


# Multi-Task Learning in Financial NLP

Improvements in Financial NLP's Multitask Learning can be achieved by considering skill diversity, task relatedness, and aggregation size.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4640029
- Identifier: SSRN 4640029
- Released: 2023-05-25
- First featured: Quant Letter No. 27 (2023-11-29): https://www.ml-quant.com/issues/2023-11-29/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

## Related

- [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://www.ml-quant.com/papers/arxiv/2407.10930/): The article explores a method to enhance Natural Language Processing systems by simultaneously optimizing language model weights and prompting strategies, leading to significant improvements in tasks like multi-hop QA and mathematical reasoning.
- [Planning In Natural Language Improves LLM Search For Code Generation](https://www.ml-quant.com/papers/arxiv/2409.03733/): PLANSEARCH is a new search algorithm that creates diverse solutions for natural language problems, outperforming traditional methods in various benchmarks.
- [Narratives from GPT-derived networks of news and a link to financial markets dislocations](https://www.ml-quant.com/papers/arxiv/2311.14419/): The study uses natural language processing and network analysis to examine news content over time, linking the results to financial market dislocations.
- [Text mining arXiv: a look through quantitative finance papers](https://www.ml-quant.com/papers/arxiv/2401.01751/): The study uses text mining and natural language processing to examine quantitative finance papers from 1997 to 2022 on the arXiv preprint server. It identifies topic trends, most cited researchers and journals, and compares different topic modeling algorithms.
- [BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks](https://www.ml-quant.com/papers/arxiv/2401.11011/): LLMs for Financial Sentiment Analysis: BioFinBERT, a finetuned Large Language Model, is introduced for financial sentiment analysis of biotech press releases and financial texts, which greatly impact biotech stock prices.
- [Is Media Sentiment Associated with Future Conflict Events?](https://www.ml-quant.com/papers/ssrn/4573695/): Using machine learning and natural language processing, the research finds a significant link between conflictual sentiment in media reports and future conflict events, indicating sentiment analysis can improve our understanding of conflict dynamics.