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arXiv Machine Learning By Ingo Marquardt, Anthilia Alchanat, Priyanka Jain

Deep LLM layers expose passage-level context in EEG during silent reading

Read the original on arXiv Machine Learning →

The study investigates how information about words and passage context is represented across the layers of the Llama‑3.1‑8B language model during silent reading, using EEG recordings. Decoders trained on each layer show that word‑level decodability peaks in early layers and diminishes toward the final layer, while a context‑tracking component increases with depth. Even a single 0.5‑second EEG window can capture passage‑level information, and control experiments confirm that this context tracking is driven by the text rather than by word decoding or session artifacts.

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