  Due to the lack of a large collection of high-quality labeled sentence pairs
with textual similarity scores, existing approaches for Semantic Textual
Similarity (STS) mostly rely on unsupervised techniques or training signals
that are only partially correlated with textual similarity, e.g., NLI-based
datasets. To tackle this issue, in this paper, we propose the strategy of
measuring text similarity via GPT annotated data (Sim-GPT for short). The core
idea of Sim-GPT is to generate data with STS labels using GPT-4, based on which
an STS model is trained. Sim-GPT framework utilizes LLMs to provide a
substantial amount of reliable annotated data filling the gap of the lack of
training signals for STS. Sim-GPT is trained on a one-time generated dataset
using BERT or RoBERTa as the backbone, which offers long-term savings in cost
and speed compared to repeatedly invoking LLMs for each sentence pair. Trained
on the examples from GPT-4 (371K), Sim-GPT yields SOTA performances on the
widely-used seven STS benchmarks: +0.99 over supervised-SimCSE, and +0.42 over
the current SOTA PromCSE model. To encourage further advancements of the field,
we release both models and the 371K annotated examples from GPT-4. Code, models
and annotated data are available at: https://github.com/ShuheWang1998/Sim-GPT.
