Summary of Dynamic Adaptive Optimization For Effective Sentiment Analysis Fine-tuning on Large Language Models, by Hongcheng Ding et al.
Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models
by Hongcheng Ding, Xuanze Zhao, Shamsul Nahar Abdullah, Deshinta Arrova Dewi, Zixiao Jiang, Xiangyu Shi
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel multi-task learning framework for sentiment analysis, which addresses the limitations of large language models (LLMs) in managing diverse task complexities. The proposed framework integrates a dynamic adaptive optimization (DAO) module that dynamically adjusts loss weights based on task importance and data characteristics during training. Experimental results show that the framework achieves superior performance on a standard financial text dataset, improving Mean Squared Error (MSE) by 15.58% and Accuracy (ACC) by 1.24% compared to previous work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make computers better at understanding how people feel about things. It’s like having a computer that can read what someone wrote online and figure out if they’re happy, sad, or mad. The computer uses special tools to help it learn from different tasks, but sometimes it gets confused. To fix this, the paper suggests a new way of making the computer learn, which is called dynamic adaptive optimization (DAO). This helps the computer adjust how much weight it gives to each task based on what’s important and what kind of data it’s looking at. The computer did better when it used this new way, especially when looking at financial texts. |
Keywords
» Artificial intelligence » Mse » Multi task » Optimization
