  One goal of technical online communities is to help developers find the right
answer in one place. A single question can be asked in different ways with
different wordings, leading to the existence of duplicate posts on technical
forums. The question of how to discover and link duplicate posts has garnered
the attention of both developer communities and researchers. For example, Stack
Overflow adopts a voting-based mechanism to mark and close duplicate posts.
However, addressing these constantly emerging duplicate posts in a timely
manner continues to pose challenges. Therefore, various approaches have been
proposed to detect duplicate posts on technical forum posts automatically. The
existing methods suffer from limitations either due to their reliance on
handcrafted similarity metrics which can not sufficiently capture the semantics
of posts, or their lack of supervision to improve the performance.
Additionally, the efficiency of these methods is hindered by their dependence
on pair-wise feature generation, which can be impractical for large amount of
data. In this work, we attempt to employ and refine the GPT-3 embeddings for
the duplicate detection task. We assume that the GPT-3 embeddings can
accurately represent the semantics of the posts. In addition, by training a
Siamese-based network based on the GPT-3 embeddings, we obtain a latent
embedding that accurately captures the duplicate relation in technical forum
posts. Our experiment on a benchmark dataset confirms the effectiveness of our
approach and demonstrates superior performance compared to baseline methods.
When applied to the dataset we constructed with a recent Stack Overflow dump,
our approach attains a Top-1, Top-5, and Top-30 accuracy of 23.1%, 43.9%, and
68.9%, respectively. With a manual study, we confirm our approach's potential
of finding unlabelled duplicates on technical forums.
