Summary of T-rag: Lessons From the Llm Trenches, by Masoomali Fatehkia et al.
T-RAG: Lessons from the LLM Trenches
by Masoomali Fatehkia, Ji Kim Lucas, Sanjay Chawla
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 integration of Large Language Models (LLMs) into applications across various domains, focusing on question answering over private enterprise documents. The authors highlight the challenges of deploying LLMs on-premises, with limited computational resources, and the need for robust applications that accurately respond to queries. They discuss Retrieval-Augmented Generation (RAG), a prominent framework for building LLM-based applications, but emphasize that customizing RAG for specific domains requires extensive expertise. The authors share their experiences building an LLM application for question answering over private organizational documents, combining RAG with a finetuned open-source LLM. They introduce Tree-RAG (T-RAG), which uses a tree structure to represent entity hierarchies within the organization and generates textual descriptions to augment context when responding to user queries. Evaluations demonstrate that this combination outperforms simple RAG or finetuning implementations. Finally, the authors share lessons learned from building an LLM application for real-world use, highlighting the importance of domain expertise and customization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer programs called Large Language Models to help answer questions based on private company documents. These programs are very good at understanding language but need to be used carefully because they can access sensitive information. The authors explain how they built one such program that can answer questions accurately, even when the answers are hard to find. They combined two ideas: Retrieval-Augmented Generation and a special way of organizing data called Tree-RAG. This combination worked better than just using one or the other. The authors also share what they learned from building this program for real companies. |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation
