Summary of Needle in the Haystack For Memory Based Large Language Models, by Elliot Nelson et al.
Needle in the Haystack for Memory Based Large Language Models
by Elliot Nelson, Georgios Kollias, Payel Das, Subhajit Chaudhury, Soham Dan
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper investigates whether combining a dynamically adaptable external memory with large language models (LLMs) can improve their performance on simple fact retrieval tasks. The authors test the Larimar architecture, which uses an external associative memory, on long-context recall tasks such as passkey and needle-in-the-haystack tests. They demonstrate that Larimar’s external memory, which allows fast write and read of text samples, can be used at test time to handle contexts much longer than those seen during training. The latent readouts from the memory control the decoder towards generating correct outputs, with the memory stored off of the GPU. Compared to existing transformer-based LLM architectures for long-context recall tasks that use larger parameter counts or modified attention mechanisms, Larimar maintains strong performance without any task-specific training or training on longer contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how a special type of AI model called large language models (LLMs) do when asked simple questions. Right now, these models don’t do very well with this kind of thing. The researchers try to fix this by adding an extra “memory” part that can help the model remember more information. They test this new system on some special tasks and find that it works really well! This means that we might be able to make AI models better at understanding longer pieces of text without having to teach them everything beforehand. |
Keywords
* Artificial intelligence * Attention * Decoder * Recall * Transformer
