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What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at OpenAI.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at OpenAI during technical interviews.

Improve classifier with noisy multi-annotator labels

Q: Improve classifier with noisy multi-annotator labels

This question evaluates a candidate's ability to analyze noisy multi-annotator labels, design label-aggregation and dataset-splitting strategies, and choose modeling and evaluation approaches for binary text classification.

Problem

You are given a text dataset for a binary classification task (label in {0,1}). Each example has been labeled by multiple human annotators, and annotators often disagree (i.e., the same item can have conflicting labels).

You need to:

Perform a dataset/label analysis to understand the disagreement and likely label noise.
Propose a training and evaluation approach that improves offline metrics (e.g., F1 / AUC / accuracy), given the noisy multi-annotator labels.

Assumptions you may make (state them clearly)

You have access to: raw text, per-annotator labels, annotator IDs, and timestamps.
You can retrain models and change the labeling aggregation strategy, but you may have limited or no ability to collect new labels.

Deliverables

What analyses would you run and what would you look for?
How would you construct train/validation/test splits to avoid misleading offline metrics?
How would you convert multi-annotator labels into training targets?
What model/loss/thresholding/calibration choices would you try, and why?
What failure modes and edge cases could cause offline metric gains to be illusory?

Problem

You need to:

Perform a dataset/label analysis to understand the disagreement and likely label noise.

Propose a training and evaluation approach that improves offline metrics (e.g., F1 / AUC / accuracy), given the noisy multi-annotator labels.

Assumptions you may make (state them clearly)

You have access to: raw text, per-annotator labels, annotator IDs, and timestamps.

You can retrain models and change the labeling aggregation strategy, but you may have limited or no ability to collect new labels.

Deliverables

What analyses would you run and what would you look for?

How would you construct train/validation/test splits to avoid misleading offline metrics?

How would you convert multi-annotator labels into training targets?

What model/loss/thresholding/calibration choices would you try, and why?

What failure modes and edge cases could cause offline metric gains to be illusory?

Improve classifier with noisy multi-annotator labels

Quick Overview

Problem

Assumptions you may make (state them clearly)

Deliverables

Comments (0)

Improve classifier with noisy multi-annotator labels

Quick Overview

Problem

Assumptions you may make (state them clearly)

Deliverables

Comments (0)