Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·19h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA
TL;DR
Existing question answering (QA) systems consume excessive tokens and storage due to raw text and image evidence. Latent Memory is a new approach that uses a single high-dimensional latent token for each evidence item, reducing resource consumption.
✦ Why It Matters
Engineers can leverage Latent Memory to build more efficient QA systems that reduce resource consumption significantly.
Key Takeaways
- Explore Latent Memory for efficient QA in resource-limited environments.
- Utilize high-dimensional latent tokens to reduce storage needs.
- Evaluate performance on multimodal QA benchmarks for insights.
- Implement compression techniques to optimize evidence representation.
How It Works
Latent Memory compresses raw evidence into high-dimensional latent tokens using a small compressor model. This allows the system to retrieve relevant tokens based on the embedded query, facilitating efficient answer generation without the need for raw text or images.