Summary of Linking In-context Learning in Transformers to Human Episodic Memory, by Li Ji-an et al.
Linking In-context Learning in Transformers to Human Episodic Memory
by Li Ji-An, Corey Y. Zhou, Marcus K. Benna, Marcelo G. Mattar
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Transformers are artificial intelligence models that rely on self-attention mechanisms to process information. Despite their success in various natural language processing tasks, the connection between Transformer-based large language models (LLMs) and biological intelligent systems remains largely unexplored. This paper investigates the relationship between interacting attention heads in LLMs and human episodic memory. The researchers focus on induction heads, which play a crucial role in in-context learning within LLMs. They demonstrate that these induction heads share similarities with the contextual maintenance and retrieval (CMR) model of human episodic memory. By analyzing LLMs pre-trained on extensive text data, the authors show that CMR-like heads often emerge in intermediate and late layers, mirroring human memory biases. The ablation of these CMR-like heads suggests a causal role in in-context learning. This study’s findings provide valuable insights into both artificial intelligence research and human cognitive processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence models, like the ones that power language translation apps, can be compared to how our brains work. The researchers looked at something called “attention heads” in these AI models and found they are similar to how our brains remember things. They tested their idea by looking at how well language models performed when certain parts of them were removed. What they found was that the parts of the AI model that mimicked human memory were important for helping the model learn new things. This study can help us understand more about how our brains work and also improve artificial intelligence. |
Keywords
» Artificial intelligence » Attention » Natural language processing » Self attention » Transformer » Translation
