Summary of Designing Heterogeneous Llm Agents For Financial Sentiment Analysis, by Frank Xing
Designing Heterogeneous LLM Agents for Financial Sentiment Analysis
by Frank Xing
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); General Finance (q-fin.GN)
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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 This paper explores the potential of large language models (LLMs) in financial sentiment analysis (FSA), a task that has traditionally relied on massive data acquisition and fine-tuned model training. By leveraging LLMs without fine-tuning, the study proposes a design framework with heterogeneous agents rooted in Minsky’s theory of mind and emotions. The framework instantiates specialized agents using prior domain knowledge of FSA errors and reasons on aggregated agent discussions. Evaluation on FSA datasets shows that this approach yields better accuracies, particularly when discussions are substantial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses super smart computers to help predict how people feel about the stock market or companies. Right now, these computers need a lot of training data to be good at this task. But what if we could use these computers without all that extra training? That’s what this research is all about. It proposes a new way to make these computers work better for financial prediction by using different types of computer “agents” that can discuss and agree on the best predictions. The results show that this approach does a better job than other methods, especially when there are lots of conversations happening. |
Keywords
» Artificial intelligence » Fine tuning
