most citedTowards Robustness Against Natural Language Word Substitutions

64 citations · 100 across the 3 of their papers we have counts for

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cs.CL2024

SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA

Siyue Zhang, Anh Tuan Luu, Chen Zhao

Text-to-SQL parsing and end-to-end question answering (E2E TQA) are two main approaches for Table-based Question Answering task. Despite success on multiple benchmarks, they have y…

cs.CL2024

Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels

Chaoqun Liu, Qin Chao, Wenxuan Zhang +4

Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. However, this paradigm is limited by…

cs.CL2024

LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models

Zhiyuan Hu, Yuliang Liu, Jinman Zhao +8

Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts t…

cs.CL20223 cited

Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning

Xiaobao Wu, Anh Tuan Luu, Xinshuai Dong

To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data characteristic of short texts to introduce more wo…

cs.CL2021

Contrastive Learning for Neural Topic Model

Thong Nguyen, Anh Tuan Luu

Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by differentiating a document with another dissimilar s…

cs.CL202164 cited

Towards Robustness Against Natural Language Word Substitutions

Xinshuai Dong, Anh Tuan Luu, Rongrong Ji +1

Robustness against word substitutions has a well-defined and widely acceptable form, i.e., using semantically similar words as substitutions, and thus it is considered as a fundame…