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20202026
most citedBenchmarking Large Language Models in Retrieval-Augmented Generation

53 citations · 282 across the 117 of their papers we have counts for

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Showing 2021Show all

15 papers · 1 filter

cs.CL2021

Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion

Lingyong Yan, Xianpei Han, Le Sun

Bootstrapping has become the mainstream method for entity set expansion. Conventional bootstrapping methods mostly define the expansion boundary using seed-based distance metrics,…

cs.CL2021

Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention

Jiawei Chen, Hongyu Lin, Xianpei Han +1

Event detection has long been troubled by the \emph{trigger curse}: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection perfo…

cs.CL2021

Fine-grained Entity Typing via Label Reasoning

Qing Liu, Hongyu Lin, Xinyan Xiao +3

Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity…

cs.CL2021★ 4 cited

Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision

Ning Bian, Xianpei Han, Bo Chen +3

Despite recent success in machine reading comprehension (MRC), learning high-quality MRC models still requires large-scale labeled training data, even using strong pre-trained lang…

cs.CL2021★ 1 cited

Element Intervention for Open Relation Extraction

Fangchao Liu, Lingyong Yan, Hongyu Lin +2

Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction. Current OpenRE mode…

cs.CL2021

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Wenkai Zhang, Hongyu Lin, Xianpei Han +4

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which i…