Improve classifier with noisy multi-annotator labels
Problem Description
You are presented with a text dataset for a binary classification task, where the target label is either 0 or 1. Each example in the dataset has been labeled by multiple human annotators, and these annotators frequently disagree, leading to conflicting labels for the same item.
Your primary objective is to improve offline metrics such as F1 score, AUC, or accuracy, given these noisy multi-annotator labels.
Assumptions
You may make the following assumptions, but please state them clearly:
- You have access to the raw text data, individual labels provided by each annotator, unique annotator IDs, and timestamps for each annotation.
- You have the capability to retrain models and modify the strategy for aggregating labels.
- You have limited or no ability to collect new labels.
Deliverables
Your solution should address the following points:
- Detail the specific analyses you would perform on the dataset and labels, and what insights you would seek from them.
- Explain how you would construct the train, validation, and test splits to ensure that offline metrics are not misleading.
- Describe your approach to converting the multi-annotator labels into suitable training targets for your model.
- Discuss the various model architectures, loss functions, thresholding strategies, and calibration techniques you would consider, providing justifications for your choices.
- Identify potential failure modes and edge cases where observed offline metric improvements might be deceptive or not generalize to real-world performance.
AI interview prep
Core dilemma
The central engineering conflict is how to derive a robust and reliable learning signal or 'ground truth' from inherently noisy and conflicting human annotations, without the ability to collect new, clean labels, while ensuring that offline metric improvements genuinely reflect real-world model performance and generalization.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Improve classifier with noisy multi-annotator labels
Problem Description
You are presented with a text dataset for a binary classification task, where the target label is either 0 or 1. Each example in the dataset has been labeled by multiple human annotators, and these annotators frequently disagree, leading to conflicting labels for the same item.
Your primary objective is to improve offline metrics such as F1 score, AUC, or accuracy, given these noisy multi-annotator labels.
Assumptions
You may make the following assumptions, but please state them clearly:
- You have access to the raw text data, individual labels provided by each annotator, unique annotator IDs, and timestamps for each annotation.
- You have the capability to retrain models and modify the strategy for aggregating labels.
- You have limited or no ability to collect new labels.
Deliverables
Your solution should address the following points:
- Detail the specific analyses you would perform on the dataset and labels, and what insights you would seek from them.
- Explain how you would construct the train, validation, and test splits to ensure that offline metrics are not misleading.
- Describe your approach to converting the multi-annotator labels into suitable training targets for your model.
- Discuss the various model architectures, loss functions, thresholding strategies, and calibration techniques you would consider, providing justifications for your choices.
- Identify potential failure modes and edge cases where observed offline metric improvements might be deceptive or not generalize to real-world performance.