Code and Datasets

Crowd coachable recommendations / retrieval (CCR)

Zhuotong Chen, Yifei Ma, Branislav Kveton, Anoop Deoras

2023

Last updated March 28, 2024

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Contents

al_demo_prime_pantry.ipynb povides a notebook template to run oracle-labeled active learning experiments on a small-scale dataset.
al_demo_nq.ipynb provides oracle-labeled experiments on the larger-scale natural questions dataset.

The only change between the two notebooks is DATA_NAME="nq" in the configuration line. One may also change it to DATA_NAME="msmarco" for the larger-scale MS-MARCO oracle-labeled experiments.

Crowd-sourcing experiments are run by calling scripts/al_0_rank.py, scripts/al_1_em.py, scripts/al_2_ft.py sequentially for each batch of the labeling tasks. Human feedback is provided between al_0_rank and al_1_em by uploading request_perm.csv and downloading human_response.csv in the same data-step folder.

All experiments are conducted using NVidia A10G GPU machines with 4-GPU parallelization. Runtime is usually less than 3 hours per active learning step. Human tasks are usually completed under 45 minutes.

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