Summary of Dynamic Adaptive Rank Space Exploration For Efficient Sentiment Analysis with Large Language Models, by Hongcheng Ding et al.
Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models
by Hongcheng Ding, Fuzhen Hu, Xuanze Zhao, Zixiao Jiang, Shamsul Nahar Abdullah, Deshinta Arrova Dewi
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper proposes a novel Dynamic Adaptive Rank Space Exploration (DARSE) framework for efficient and effective sentiment analysis using large language models (LLMs). Sentiment analysis is crucial for assessing public opinion and informing decision-making. However, adapting LLMs to domain-specific tasks remains challenging due to computational constraints and the need for optimal fine-tuning. DARSE consists of a coarse-grained greedy algorithm, a fine-grained exploration algorithm, and a dynamic rank allocation method to determine the optimal rank combination for each LLM layer. The framework significantly improves sentiment analysis accuracy, achieving a 15.1% improvement in mean squared error (MSE) and a 4.3% improvement in accuracy compared to previous work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer models called large language models (LLMs) to understand how people feel about things. These models are really good at understanding what we say, but they need some help to focus on the right parts of our words. The researchers came up with a new way to make these models work better by finding the best combination of words and meanings. They tested it and found that their method worked much better than before, which is important for things like figuring out what people really think about politics or products. |
Keywords
» Artificial intelligence » Fine tuning » Mse
