# Look at the First Sentence: Position Bias in Question Answering

Source: [Look at the First Sentence: Position Bias in Question Answering](<https://www.sota2.com/research/paper/68fa696e1e209131cea22013>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:59:51.007Z

Authors: Miyoung Ko, Jinhyuk Lee, Hyunjae Kim, Gangwoo Kim, Jaewoo Kang

Published: 2020-04-30T06:25:16.000Z

## Abstract

Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48% to 81.64% when trained on a biased SQuAD dataset.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.14602>)

[PDF](<https://arxiv.org/pdf/2004.14602.pdf>)