Summary of Silc-efsa: Self-aware In-context Learning Correction For Entity-level Financial Sentiment Analysis, by Senbin Zhu et al.
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis
by Senbin Zhu, Chenyuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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 proposed Self-aware In-context Learning Correction (SILC) approach for fine-grained sentiment analysis in finance leverages a novel two-stage strategy to achieve state-of-the-art performance. The first stage involves fine-tuning a base large language model to generate pseudo-labeled data specific to the task, while the second stage trains a correction model using a GNN-based example retriever informed by the pseudo-labeled data. This approach has been demonstrated to advance the field of financial sentiment analysis, and is particularly useful for monitoring the cryptocurrency market. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers create large datasets for analyzing the sentiment of specific companies or assets in finance, which is important for making informed investment decisions. They then develop a new way to analyze this sentiment called SILC, which involves training two models that work together to improve their performance. The results show that SILC can achieve better accuracy than other approaches and has practical applications such as monitoring the cryptocurrency market. |
Keywords
» Artificial intelligence » Fine tuning » Gnn » Large language model
