Summary of Enhancing Long-term Memory Using Hierarchical Aggregate Tree For Retrieval Augmented Generation, by Aadharsh Aadhithya a et al.
Enhancing Long-Term Memory using Hierarchical Aggregate Tree for Retrieval Augmented Generation
by Aadharsh Aadhithya A, Sachin Kumar S, Soman K.P
First submitted to arxiv on: 10 Jun 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 The proposed Hierarchical Aggregate Tree (HAT) memory structure addresses limitations in large language models’ context capacity, enabling them to reason over longer conversations. By recursively aggregating relevant dialogue context through conditional tree traversals, HAT encapsulates information from children nodes, allowing for broad coverage with depth control. The optimal tree traversal formulation is used to find the best context. Experimental results demonstrate the effectiveness of HAT in improving dialog coherence and summary quality compared to baseline contexts, making it suitable for multi-turn reasoning without exponential parameter growth. This memory augmentation enables more consistent, grounded long-form conversations from LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can’t have very long conversations because they can only remember a little bit of what’s been said. The Hierarchical Aggregate Tree (HAT) is a new way to store and use context in these conversations. It works by looking at smaller pieces of the conversation, then combining them to create a bigger picture. This helps the model understand more of what’s being talked about without getting too complicated or using up too much memory. The results show that this new approach makes conversations clearer and more helpful than usual. |
