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20172023
most citedCross-Sentence N-ary Relation Extraction with Graph LSTMs

134 citations · 335 across the 52 of their papers we have counts for

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cs.CL2023

RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment

Kevin Yang, Dan Klein, Asli Celikyilmaz +2

We propose Reinforcement Learning from Contrastive Distillation (RLCD), a method for aligning language models to follow principles expressed in natural language (e.g., to be more h…

cs.CL2023

Open-Domain Text Evaluation via Contrastive Distribution Methods

Sidi Lu, Hongyi Liu, Asli Celikyilmaz +2

Recent advancements in open-domain text generation, driven by the power of large pre-trained language models (LLMs), have demonstrated remarkable performance. However, assessing th…

cs.CL2023

DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models

Sidi Lu, Wenbo Zhao, Chenyang Tao +4

NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models. It is designed to avoid catastrophic forgetting while achieving gua…

cs.CL2023

AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model

I-Hung Hsu, Zhiyu Xie, Kuan-Hao Huang +2

Event argument extraction (EAE) identifies event arguments and their specific roles for a given event. Recent advancement in generation-based EAE models has shown great performance…

cs.CL20231 cited

Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Tales

Paulina Toro Isaza, Guangxuan Xu, Akintoye Oloko +3

Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of humanities and social science literatur…

cs.CL2023

Identifying Informational Sources in News Articles

Alexander Spangher, Nanyun Peng, Jonathan May +1

News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand th…