Summary of Mixing It Up: the Cocktail Effect Of Multi-task Fine-tuning on Llm Performance — a Case Study in Finance, by Meni Brief et al.
Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance – A Case Study in Finance
by Meni Brief, Oded Ovadia, Gil Shenderovitz, Noga Ben Yoash, Rachel Lemberg, Eitam Sheetrit
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to fine-tuning large language models (LLMs) is presented, which demonstrates the effectiveness of multi-task fine-tuning in achieving state-of-the-art results on financial benchmarks. Specifically, it is shown that fine-tuning LLMs exclusively on a target task is not always the most effective strategy, and that training on a cocktail of related tasks can lead to significant performance improvements. The study uses several widely adopted LLMs as baselines, conducts over 200 training experiments, and empirically confirms the benefits of multi-task fine-tuning. Additionally, the use of general instruction data is explored as a form of regularization, which helps minimize performance degradation. Furthermore, the inclusion of mathematical data is found to improve numerical reasoning, which transfers effectively to financial tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being used more and more in different areas, including finance. To make these models better for specific tasks like predicting stock prices or analyzing financial reports, researchers have been fine-tuning them. In this study, scientists looked at how well this fine-tuning works when applied to financial tasks. They found that instead of just focusing on one task, it’s actually more effective to train the model on multiple related tasks at once. This approach allowed a small model to perform better than a larger model on some financial benchmarks. The researchers also experimented with adding general instruction data and mathematical data to the training process, which helped improve the model’s ability to understand numbers and make predictions. |
Keywords
» Artificial intelligence » Fine tuning » Multi task » Regularization
