Text Encoders Lack Knowledge: Leveraging Generative LLMs for
  Domain-Specific Semantic Textual Similarity
Joseph Gatto Omar Sharif Parker Seegmiller Philip Bohlman Sarah Masud Preum
Amidst the sharp rise in the evaluation of large language models (LLMs) on
various tasks, we find that semantic textual similarity (STS) has been
under-explored. In this study, we show that STS can be cast as a text
generation problem while maintaining strong performance on multiple STS
benchmarks. Additionally, we show generative LLMs significantly outperform
existing encoder-based STS models when characterizing the semantic similarity
between two texts with complex semantic relationships dependent on world
knowledge. We validate this claim by evaluating both generative LLMs and
existing encoder-based STS models on three newly collected STS challenge sets
which require world knowledge in the domains of Health, Politics, and Sports.
All newly collected data is sourced from social media content posted after May
2023 to ensure the performance of closed-source models like ChatGPT cannot be
credited to memorization. Our results show that, on average, generative LLMs
outperform the best encoder-only baselines by an average of 22.3% on STS tasks
requiring world knowledge. Our results suggest generative language models with
STS-specific prompting strategies achieve state-of-the-art performance in
complex, domain-specific STS tasks.