Summary of Evidence From Fmri Supports a Two-phase Abstraction Process in Language Models, by Emily Cheng and Richard J. Antonello
Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models
by Emily Cheng, Richard J. Antonello
First submitted to arxiv on: 9 Sep 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 explores the representation properties of intermediate hidden states in large language models (LLMs) that enable predicting brain responses to natural language stimuli. Researchers have shown that these intermediate layers are more effective than output layers for this task, but the reasons behind this phenomenon remain unclear. The study reveals a two-phase abstraction process within LLMs, where early composition phases compress into fewer layers as training progresses. The paper also demonstrates a strong connection between layer-wise encoding performance and intrinsic dimensionality of representations from LLMs, suggesting that this correspondence stems primarily from the compositional properties of LLMs rather than their next-word prediction capabilities. To achieve this understanding, the authors employed manifold learning methods to analyze language encoding models in functional magnetic resonance imaging (fMRI) data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out why some parts of big language models are better at predicting how our brains respond to words and sentences. Scientists already know that these parts, called intermediate layers, do a great job, but they don’t know why it’s not the final layer that does this task. The researchers found out that there’s a two-stage process happening inside these language models as they learn. They also discovered that how well these layers work is connected to how simple or complex the ideas represented in the model are. |
Keywords
» Artificial intelligence » Manifold learning
