3 citations · 6 across the 5 of their papers we have counts for
5 papers
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process
Peng Wang, Xiaobin Wang, Chao Lou +3
In-context learning (ICL) is a few-shot learning paradigm that involves learning mappings through input-output pairs and appropriately applying them to new instances. Despite the r…
Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers
Chao Lou, Zixia Jia, Zilong Zheng +1
Accommodating long sequences efficiently in autoregressive Transformers, especially within an extended context window, poses significant challenges due to the quadratic computation…
AMR Parsing with Causal Hierarchical Attention and Pointers
Chao Lou, Kewei Tu
Translation-based AMR parsers have recently gained popularity due to their simplicity and effectiveness. They predict linearized graphs as free texts, avoiding explicit structure m…
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
Tianyu Yu, Chengyue Jiang, Chao Lou +12
Large language models (LLMs) have shown impressive ability for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which…
Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints
Chao Lou, Kewei Tu
Neural QCFG is a grammar-based sequence-tosequence (seq2seq) model with strong inductive biases on hierarchical structures. It excels in interpretability and generalization but suf…