Summary of Enhancing Financial Question Answering with a Multi-agent Reflection Framework, by Sorouralsadat Fatemi et al.
Enhancing Financial Question Answering with a Multi-Agent Reflection Framework
by Sorouralsadat Fatemi, Yuheng Hu
First submitted to arxiv on: 29 Oct 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 proposed multi-agent framework incorporating a critic agent improves the performance of Large Language Models (LLMs) in financial question answering (QA) tasks by 15% on average, outperforming single-agent LLMs and offering a cost-effective alternative to larger models. The framework reflects on reasoning steps and final answers for each question, enhancing multi-step reasoning capabilities. This approach demonstrates the potential of LLM-based frameworks in tackling complex NLP tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are really good at answering questions about language, but they struggle when it comes to financial question answering, especially when numbers are involved. Researchers have been trying to improve their performance by using special teams of AI models working together. This new study proposes a team of AI models that checks its own work and makes sure the answers are correct. The results show that this approach is really effective, improving the performance of these AI models by 15%. It’s like having a second pair of eyes to make sure everything is accurate. |
Keywords
» Artificial intelligence » Nlp » Question answering
