Inducing brain-relevant bias in natural language processing models
Authors: Dan Schwartz, Mariya Toneva, Leila Wehbe
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that a version of BERT... can improve the prediction of brain activity after fine-tuning. We evaluate the quality of brain predictions made by a particular model by using the brain prediction in a classification task on held-out data... |
| Researcher Affiliation | Academia | Dan Schwartz Carnegie Mellon University drschwar@cs.cmu.edu Mariya Toneva Carnegie Mellon University mariya@cmu.edu Leila Wehbe Carnegie Mellon University lwehbe@cs.cmu.edu |
| Pseudocode | No | The paper includes diagrams (e.g., Figure 1) describing the model architecture but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/danrsc/bert_brain_neurips_2019 |
| Open Datasets | Yes | In this analysis, we use magnetoencephalography (MEG) and functional magnetic resonance imaging (f MRI) data recorded from people as they read a chapter from Harry Potter and the Sorcerer s Stone Rowling (1999). The MEG and f MRI experiments were shared respectively by the authors of Wehbe et al. (2014a) at our request and Wehbe et al. (2014b) online1. 1http://www.cs.cmu.edu/~fmri/plosone/ |
| Dataset Splits | Yes | The f MRI data were recorded in four separate runs in the scanner for each participant... We cross-validate over the f MRI runs. For each f MRI run, we train the model using the examples from the other three runs and use the fourth run to evaluate the model. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'the Py Torch version of the BERT code provided by Hugging Face' but does not specify version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | In all of our models, we use a base learning rate of 5 10 5. The learning rate increases linearly from 0 to 5 10 5 during the first 10% of the training epochs and then decreases linearly back to 0 during the remaining epochs. We use mean squared error as our loss function in all models. |