Unveiling Bias in NLU Benchmark Creation: The Impact of Instruction Patterns

Core Concepts

Bias in NLU benchmarks originates from instruction patterns, impacting model performance and generalization.

Abstract

In recent years, NLU progress relies on crowdsourced benchmarks. Annotators are influenced by instruction patterns, leading to biased data. This bias affects model performance and generalization. Recommendations include diverse instructions and monitoring biases.
Introduction

Progress driven by NLU benchmarks.
Crowdsourcing for dataset creation.
Previous studies on biases in crowdsourced data.
Instruction Bias Analysis

Hypothesis on instruction bias.
Study of 14 NLU benchmarks.
Patterns in instruction examples.
Propagation of bias to collected data.
Effect on Model Learning

Overestimation of model performance due to bias.
Models struggle to generalize beyond patterns.
Large models less sensitive to bias.
Conclusions and Recommendations

Instruction bias exists widely in NLU benchmarks.
Recommendations for future benchmark creation.
Implications for model training using instructions.

Stats

"In this work, we hypothesize that annotators pick up on patterns in the crowdsourcing instructions, which bias them to write many similar examples that are then over-represented in the collected data."
"We find that instruction bias is evident in most of these datasets, showing that ∼ 73% of instruction examples on average share a few clear patterns."
"Moreover, we observe that a higher frequency of instruction patterns in the training set often increases the model performance gap on pattern and non-pattern examples."

Quotes

"Annotators pick up on patterns in the crowdsourcing instructions."
"Instruction bias is evident in most datasets."
"Models often fail to generalize beyond instruction patterns."

Key Insights Distilled From

Don't Blame the Annotator

by Mihir Parmar... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2205.00415.pdf

Deeper Inquiries

How can diverse crowdsourcing instructions mitigate biases?

Diverse crowdsourcing instructions can help mitigate biases by providing a broader range of examples and patterns for annotators to follow. By including a variety of prompts, tasks, and scenarios in the instructions, dataset creators can encourage crowdworkers to think creatively and avoid falling into repetitive patterns. This diversity can stimulate different perspectives and approaches from annotators, leading to a more varied and representative dataset. Additionally, rotating through different sets of examples or periodically sampling from a pool of diverse examples during the collection process can prevent annotators from fixating on specific patterns.

What are the implications of overestimated model performance due to instruction bias?

Overestimation of model performance due to instruction bias can have significant consequences on the reliability and generalizability of NLU models. When models are trained predominantly on data instances with instruction patterns, they may struggle to generalize beyond those specific patterns when faced with new or unseen examples. This limitation hinders the model's ability to perform well on real-world tasks that do not align with the biased patterns present in the training data. As a result, there is a risk of inflated performance metrics that do not accurately reflect the true capabilities of the model.

How can biases originating from annotation instructions be addressed effectively?

Biases originating from annotation instructions can be effectively addressed through several strategies:

Diverse Instruction Examples: Providing a wide range of instructive examples in varying formats and styles.
Regular Analysis: Monitoring pattern distributions in collected data during data collection.
Correlation Checks: Analyzing correlations between model performance and input patterns during evaluation.
Training Strategies: Training neural models using learning strategies that make them more robust against biases.
Expert Assessments: Incorporating expert assessments or feedback loops into crowdsourcing protocols for quality control.
Linguistic Diversity Encouragement: Encouraging linguistic diversity among crowdworkers by avoiding recurring word patterns in instructions.

By implementing these measures proactively throughout the dataset creation process, it is possible to minimize biases stemming from annotation instructions and enhance both dataset quality and model generalization capabilities significantly