# Ensemble of Task-Specific Language Models for Brain Encoding

arXiv: https://arxiv.org/abs/2310.15720 · HTML: https://mlsanity.com/paper/2310.15720/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Linking artificial and human neural representations of language](https://mlsanity.com/paper/1910.01244/) (2019)
- [Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?](https://mlsanity.com/paper/2205.01404/) (2022)

## Abstract (arXiv)

Language models have been shown to be rich enough to encode fMRI activations of certain Regions of Interest in our Brains. Previous works have explored transfer learning from representations learned for popular natural language processing tasks for predicting brain responses. In our work, we improve the performance of such encoders by creating an ensemble model out of 10 popular Language Models (2 syntactic and 8 semantic). We beat the current baselines by 10% on average across all ROIs through our ensembling methods.