Detrimental Contexts in Open-Domain Question Answering
Philhoon Oh James Thorne
For knowledge intensive NLP tasks, it has been widely accepted that accessing
more information is a contributing factor to improvements in the model's
end-to-end performance. However, counter-intuitively, too much context can have
a negative impact on the model when evaluated on common question answering (QA)
datasets. In this paper, we analyze how passages can have a detrimental effect
on retrieve-then-read architectures used in question answering. Our empirical
evidence indicates that the current read architecture does not fully leverage
the retrieved passages and significantly degrades its performance when using
the whole passages compared to utilizing subsets of them. Our findings
demonstrate that model accuracy can be improved by 10% on two popular QA
datasets by filtering out detrimental passages. Additionally, these outcomes
are attained by utilizing existing retrieval methods without further training
or data. We further highlight the challenges associated with identifying the
detrimental passages. First, even with the correct context, the model can make
an incorrect prediction, posing a challenge in determining which passages are
most influential. Second, evaluation typically considers lexical matching,
which is not robust to variations of correct answers. Despite these
limitations, our experimental results underscore the pivotal role of
identifying and removing these detrimental passages for the context-efficient
retrieve-then-read pipeline. Code and data are available at
https://github.com/xfactlab/emnlp2023-damaging-retrieval