❓ Question-Answering Embeddings ❓

Crafting Interpretable Embeddings by Asking LLMs Questions, code for the QA-Emb paper.

QA-Embs builds an interpretable embeddings by asking a series of yes-no questions to a pre-trained autoregressive LLM.

Quickstart

If you just want to use QA-Emb in your own application, the easiest way is through the imodelsX package. To install, just run pip install imodelsx.

Then, you can generate your own interpretable embeddings by coming up with questions for your domain: ```python from imodelsx import QAEmb import pandas as pd

questions = [ 'Is the input related to food preparation?', 'Does the input mention laughter?', 'Is there an expression of surprise?', 'Is there a depiction of a routine or habit?', 'Does the sentence contain stuttering?', 'Does the input contain a first-person pronoun?', ] examples = [ 'i sliced some cucumbers and then moved on to what was next', 'the kids were giggling about the silly things they did', 'and i was like whoa that was unexpected', 'walked down the path like i always did', 'um no um then it was all clear', 'i was walking to school and then i saw a cat', ]

checkpoint = 'meta-llama/Meta-Llama-3-8B-Instruct'

embedder = QAEmb( questions=questions, checkpoint=checkpoint, use_cache=False) embeddings = embedder(examples)

df = pd.DataFrame(embeddings.astype(int), columns=[ q.split()[-1] for q in questions]) df.index = examples df.columns.name = 'Question (abbreviated)' display(df.style.background_gradient(axis=None)) --------DISPLAYS ANSWER FOR EACH QUESTION IN EMBEDDING-------- ```

Dataset set up

Directions for installing the datasets required for reproducing the fMRI experiments in the paper.

• download data with python experiments/00_load_dataset.py
  • create a data dir under wherever you run it and will use datalad to download the preprocessed data as well as feature spaces needed for fitting semantic encoding models
• set neuro1.config.root_dir to where you want to store the data
• to make flatmaps, need to set [pycortex filestore] to {root_dir}/ds003020/derivative/pycortex-db/
• to run eng1000, need to grab em_data directory from here and move its contents to {root_dir}/em_data
• loading responses
  • neuro1.data.response_utils function load_response
  • loads responses from at {root_dir}/ds003020/derivative/preprocessed_data/{subject}, hwere they are stored in an h5 file for each story, e.g. wheretheressmoke.h5
• loading stimulus
  • neuro1.features.stim_utils function load_story_wordseqs
  • loads textgrids from {root_dir}/ds003020/derivative/TextGrids", where each story has a TextGrid file, e.g.wheretheressmoke.TextGrid`
  • uses {root_dir}/ds003020/derivative/respdict.json to get the length of each story

Code install

Directions for installing the code here as a package for full development.

• from the repo directory, start with pip install -e . to locally install the neuro1 package
• python 01_fit_encoding.py --subject UTS03 --feature eng1000
  • The other optional parameters that encoding.py takes such as sessions, ndelays, single_alpha allow the user to change the amount of data and regularization aspects of the linear regression used.
  • This function will then save model performance metrics and model weights as numpy arrays.

# Citation r @misc{benara2024crafting, title={Crafting Interpretable Embeddings by Asking LLMs Questions}, author={Vinamra Benara and Chandan Singh and John X. Morris and Richard Antonello and Ion Stoica and Alexander G. Huth and Jianfeng Gao}, year={2024}, eprint={2405.16714}, archivePrefix={arXiv}, primaryClass={cs.CL} }