Summary of Improving the Capabilities Of Large Language Model Based Marketing Analytics Copilots with Semantic Search and Fine-tuning, by Yilin Gao et al.
Improving the Capabilities of Large Language Model Based Marketing Analytics Copilots With Semantic Search And Fine-Tuning
by Yilin Gao, Sai Kumar Arava, Yancheng Li, James W. Snyder Jr
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 research paper explores the potential of large language models (LLMs), such as GPT-4 and Llama-2-70b, in solving problems related to marketing attribution and budget optimization. The authors highlight the challenges in deploying these complex AI models without extensive implementation teams. To overcome these limitations, they propose a combination of semantic search, prompt engineering, and fine-tuning techniques to improve the accuracy of LLMs in executing tasks like domain-specific question-answering, SQL generation, and tabular analysis. The paper compares proprietary and open-source models, as well as various embedding methods, on sample use cases specific to marketing mix modeling and attribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how artificial intelligence (AI) can help with marketing decisions. AI models like GPT-4 can analyze large amounts of data to provide insights quickly. However, these models are complex, so it’s hard for people without technical expertise to understand what they’re doing or why the answers make sense. The authors want to find ways to simplify the use of AI models and improve their accuracy in tasks like answering specific questions or generating SQL code. They test different approaches on marketing-related problems and show that combining certain techniques can lead to better results. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Gpt » Llama » Optimization » Prompt » Question answering
