  Negation is a common everyday phenomena and has been a consistent area of
weakness for language models (LMs). Although the Information Retrieval (IR)
community has adopted LMs as the backbone of modern IR architectures, there has
been little to no research in understanding how negation impacts neural IR. We
therefore construct a straightforward benchmark on this theme: asking IR models
to rank two documents that differ only by negation. We show that the results
vary widely according to the type of IR architecture: cross-encoders perform
best, followed by late-interaction models, and in last place are bi-encoder and
sparse neural architectures. We find that most information retrieval models
(including SOTA ones) do not consider negation, performing the same or worse
than a random ranking. We show that although the obvious approach of continued
fine-tuning on a dataset of contrastive documents containing negations
increases performance (as does model size), there is still a large gap between
machine and human performance.
