Summary of Fistech: Financial Style Transfer to Enhance Creativity Without Hallucinations in Llms, by Sohini Roychowdhury et al.
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs
by Sohini Roychowdhury, Marko Krema, Brian Moore, Xingjian Lai, Dike Effedua, Bharat Jethwani
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 explores fine-tuning large language models (LLMs) for domain-specific applications like financial report generation. The goal is to create LLMs that can generate creative and accurate writing styles with minimal prompting. To achieve this, the authors propose a two-stage fine-tuning strategy that involves training on public domain financial reports in the first stage, allowing the LLM to hallucinate, and then correcting those hallucinations in the second stage. The finally trained LLM can generate specific financial report sections using minimal instructions and tabular data inputs while ensuring low fine-tuning costs. The authors evaluate their proposed framework by comparing its performance with base LLMs on various metrics such as perplexity, ROUGE, TER, BLEU, creativity, knowledge density, uncertainty, and cross entropy. The results show that the proposed framework boosts the accuracy of financial questions answering by two-folds while reducing hallucinations by over 50%. It also improves the model’s ability to generate text with higher creativity and knowledge density. The authors’ approach can be generalized to train creativity in LLMs for other applications, making it a significant contribution to the field of Generative AI. By fine-tuning LLMs for specific writing styles, the authors open up new possibilities for automation and chatbot-like applications that require creative and accurate text generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to make large language models (LLMs) better at generating financial reports. Right now, these models can generate text, but it’s not always very good or creative. The authors want to change this by fine-tuning the LLMs so they can write financial reports in different styles. They do this by letting the model generate its own writing and then correcting any mistakes. This makes the model better at generating reports that are both accurate and creative. The authors tested their method and found that it works really well. The model is now much better at answering questions about financial reports and it can generate text that is more creative and interesting. This could be very useful for people who need to write a lot of financial reports, like accountants or bankers. It’s an important step forward in making AI models more useful for everyday tasks. |
Keywords
» Artificial intelligence » Bleu » Cross entropy » Fine tuning » Perplexity » Prompting » Rouge » Text generation
