116 citations · 496 across the 48 of their papers we have counts for
7 papers · 1 filter
Sim-GPT: Text Similarity via GPT Annotated Data
Shuhe Wang, Beiming Cao, Shengyu Zhang +5
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 o…
MART: Learning Hierarchical Music Audio Representations with Part-Whole Transformer
Dong Yao, Jieming Zhu, Jiahao Xun +6
Recent research in self-supervised contrastive learning of music representations has demonstrated remarkable results across diverse downstream tasks. However, a prevailing trend in…
Sentiment Analysis through LLM Negotiations
Xiaofei Sun, Xiaoya Li, Shengyu Zhang +5
A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning. This framework suffers…
Instruction Tuning for Large Language Models: A Survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li +8
This paper surveys research works in the quickly advancing field of instruction tuning (IT), which can also be referred to as supervised fine-tuning (SFT)\footnote{In this paper, u…
DisCover: Disentangled Music Representation Learning for Cover Song Identification
Jiahao Xun, Shengyu Zhang, Yanting Yang +7
In the field of music information retrieval (MIR), cover song identification (CSI) is a challenging task that aims to identify cover versions of a query song from a massive collect…
Denoising Multi-modal Sequential Recommenders with Contrastive Learning
Dong Yao, Shengyu Zhang, Zhou Zhao +5
There is a rapidly-growing research interest in engaging users with multi-modal data for accurate user modeling on recommender systems. Existing multimedia recommenders have achiev…
Intelligent Model Update Strategy for Sequential Recommendation
Zheqi Lv, Wenqiao Zhang, Zhengyu Chen +2
Modern online platforms are increasingly employing recommendation systems to address information overload and improve user engagement. There is an evolving paradigm in this researc…