most citedMitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models

2 citations · 2 across the 4 of their papers we have counts for

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6 papers

cs.CL20242 cited

Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models

Zhenyi Lu, Jie Tian, Wei Wei +4

Text classification is a crucial task encountered frequently in practical scenarios, yet it is still under-explored in the era of large language models (LLMs). This study shows tha…

cs.CL2024

Position Debiasing Fine-Tuning for Causal Perception in Long-Term Dialogue

Shixuan Fan, Wei Wei, Wendi Li +3

The core of the dialogue system is to generate relevant, informative, and human-like responses based on extensive dialogue history. Recently, dialogue generation domain has seen ma…

cs.CL2024

Reinforcement Learning with Token-level Feedback for Controllable Text Generation

Wendi Li, Wei Wei, Kaihe Xu +3

To meet the requirements of real-world applications, it is essential to control generations of large language models (LLMs). Prior research has tried to introduce reinforcement lea…

cs.CL2024

Joint Multi-Facts Reasoning Network For Complex Temporal Question Answering Over Knowledge Graph

Rikui Huang, Wei Wei, Xiaoye Qu +3

Temporal Knowledge Graph (TKG) is an extension of regular knowledge graph by attaching the time scope. Existing temporal knowledge graph question answering (TKGQA) models solely ap…

cs.CV2023

Detection-based Intermediate Supervision for Visual Question Answering

Yuhang Liu, Daowan Peng, Wei Wei +3

Recently, neural module networks (NMNs) have yielded ongoing success in answering compositional visual questions, especially those involving multi-hop visual and logical reasoning.…

cs.CL2023

Enhancing Low-Resource Relation Representations through Multi-View Decoupling

Chenghao Fan, Wei Wei, Xiaoye Qu +4

Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource sce…