most citedPEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.SD2025

FCPE: A Fast Context-based Pitch Estimation Model

Yuxin Luo, Ruoyi Zhang, Lu-Chuan Liu +2

Pitch estimation (PE) in monophonic audio is crucial for MIDI transcription and singing voice conversion (SVC), but existing methods suffer significant performance degradation unde…

cs.CL2025

iShumei-Chinchunmei at SemEval-2025 Task 4: A balanced forgetting and retention multi-task framework using effective unlearning loss

Yujian Sun, Tian Li

As the Large Language Model (LLM) gains widespread adoption, increasing attention has been given to the challenge of making LLM forget non-compliant data memorized during its pre-t…

cs.CL2025

Chinchunmei at SemEval-2025 Task 11: Boosting the Large Language Model's Capability of Emotion Perception using Contrastive Learning

Tian Li, Yujian Sun, Huizhi Liang

The SemEval-2025 Task 11, Bridging the Gap in Text-Based Emotion Detection, introduces an emotion recognition challenge spanning over 28 languages. This competition encourages rese…

cs.CL2025

MTLM: Incorporating Bidirectional Text Information to Enhance Language Model Training in Speech Recognition Systems

Qingliang Meng, Pengju Ren, Tian Li +2

Automatic speech recognition (ASR) systems normally consist of an acoustic model (AM) and a language model (LM). The acoustic model estimates the probability distribution of text g…

cs.CL20241 cited

PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification

Tianfang Xie, Tianjing Li, Wei Zhu +2

Due to their substantial sizes, large language models (LLMs) are typically deployed within a single-backbone multi-tenant framework. In this setup, a single instance of an LLM back…