  Negative sampling stands as a pivotal technique in dense retrieval, essential
for training effective retrieval models and significantly impacting retrieval
performance. While existing negative sampling methods have made commendable
progress by leveraging hard negatives, a comprehensive guiding principle for
constructing negative candidates and designing negative sampling distributions
is still lacking. To bridge this gap, we embark on a theoretical analysis of
negative sampling in dense retrieval. This exploration culminates in the
unveiling of the quasi-triangular principle, a novel framework that elucidates
the triangular-like interplay between query, positive document, and negative
document. Fueled by this guiding principle, we introduce TriSampler, a
straightforward yet highly effective negative sampling method. The keypoint of
TriSampler lies in its ability to selectively sample more informative negatives
within a prescribed constrained region. Experimental evaluation show that
TriSampler consistently attains superior retrieval performance across a diverse
of representative retrieval models.
