activity
20202026
most citedReducing Quantity Hallucinations in Abstractive Summarization

8 citations · 10 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CL2026

Cultivar: A Contrastive and Locale-Oriented Translation Benchmark for Investigating Contamination and Localisation Robustness

Pinzhen Chen, Koel Dutta Chowdhury, Xiaoya Xu +20

Multilingual translation benchmarks are typically sourced in English and translated into other languages, treating language pairs as the unit of evaluation---a design that is prone…

cs.CL2026

Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profiles

Shun Shao, Zheng Zhao, Anna Korhonen +2

Most fairness research in NLP assumes direct access to protected attributes such as gender, race, or nationality. In practice, however, such information is often unavailable due to…

cs.LG2026

Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability

Jialiang Yin, Zheng Zhao, Linsey Pang +3

Graph Neural Networks (GNNs) have demonstrated remarkable performance across a range of applications involving graph-structured data, particularly in high-stakes domains. However,…

cs.CL2026

Summarization is Not Dead Yet

Dongqi Liu, Chenxi Whitehouse, Zheng Zhao +3

The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summar…

cs.CL2024

Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding

Zheng Zhao, Emilio Monti, Jens Lehmann +1

Large language models (LLMs) tend to inadequately integrate input context during text generation, relying excessively on encoded prior knowledge in model parameters, potentially re…

cs.CL2024★ 1 cited

Spectral Editing of Activations for Large Language Model Alignment

Yifu Qiu, Zheng Zhao, Yftah Ziser +3

Large language models (LLMs) often exhibit undesirable behaviours, such as generating untruthful or biased content. Editing their internal representations has been shown to be effe…