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20232025
most citedOn the Worst Prompt Performance of Large Language Models

3 citations · 6 across the 10 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CL2023

ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

Xuxin Cheng, Bowen Cao, Qichen Ye +3

Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impa…

cs.SD2023

NADiffuSE: Noise-aware Diffusion-based Model for Speech Enhancement

Wen Wang, Dongchao Yang, Qichen Ye +2

The goal of speech enhancement (SE) is to eliminate the background interference from the noisy speech signal. Generative models such as diffusion models (DM) have been applied to t…

cs.CL2023

Alleviating Over-smoothing for Unsupervised Sentence Representation

Nuo Chen, Linjun Shou, Ming Gong +5

Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language mo…

cs.LG2023

FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning

Bowen Cao, Qichen Ye, Weiyuan Xu +1

Learning representations for graph-structured data is essential for graph analytical tasks. While remarkable progress has been made on static graphs, researches on temporal graphs…

cs.CL2023★ 2 cited

FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering

Qichen Ye, Bowen Cao, Nuo Chen +2

Knowledge-aware question answering (KAQA) requires the model to answer questions over a knowledge base, which is essential for both open-domain QA and domain-specific QA, especiall…