Summary of Memory-augmented Agent Training For Business Document Understanding, by Jiale Liu et al.
Memory-Augmented Agent Training for Business Document Understanding
by Jiale Liu, Yifan Zeng, Malte Højmark-Bertelsen, Marie Normann Gadeberg, Huazheng Wang, Qingyun Wu
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Matrix, a novel paradigm for Large Language Models (LLMs) to build domain expertise in specialized business domains like logistics. Matrix enables LLM agents to progressively refine their memory and learn from experience, improving their performance on tasks like extracting transport references from invoices. The authors collaborate with one of the world’s largest logistics companies to create a dataset of Universal Business Language format invoice documents. Experiments demonstrate that Matrix outperforms both prompting a single LLM and vanilla LLM agents by significant margins (30.3% and 35.2%, respectively). The optimized systems also require fewer API calls, costs, and can analyze longer documents on average. This approach transforms general-purpose LLMs into specialized business tools through systematic memory enhancement in document processing tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand and extract important information from a large stack of business documents by hand. That’s a big problem for companies that deal with lots of invoices and other papers. This paper talks about how to use special computer models called Large Language Models (LLMs) to help solve this problem. They created a new way to teach these models to become experts in specific areas, like logistics, so they can quickly and accurately extract important information from documents. The results show that their new method works much better than the old way of using LLMs, and it’s more efficient too! |
Keywords
» Artificial intelligence » Prompting
