Summary of Beyond Text: Optimizing Rag with Multimodal Inputs For Industrial Applications, by Monica Riedler et al.
Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications
by Monica Riedler, Stefan Langer
First submitted to arxiv on: 29 Oct 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 investigates how to integrate multimodal models into Retrieval Augmented Generation (RAG) systems for the industrial domain. The goal is to determine whether including images alongside text from documents improves RAG performance and find the optimal configuration for this type of system. Two approaches are explored: multimodal embeddings and generating textual summaries from images. The study uses two Large Language Models (LLMs), GPT4-Vision and LLaVA, and evaluates results using an LLM-as-a-Judge approach. Findings show that multimodal RAG outperforms single-modality settings, although image retrieval poses a greater challenge than text retrieval. Textual summaries from images present a more promising approach compared to multimodal embeddings, offering opportunities for future advancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to better understand and work with pictures and words together. Right now, machines can answer questions and do tasks, but they’re not very good at understanding specific topics like the industrial domain. The researchers are trying to make things better by combining two types of models: ones that process text (like what you read) and ones that process images (like what you see). They want to know if adding pictures to text helps machines do their jobs better, and they’re testing different ways of doing this. Two special computer programs called GPT4-Vision and LLaVA are being used to help the research along. The results show that combining words and pictures can be helpful, but it’s harder for computers to understand images than words. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
