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20202025
most citedZero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model

2 citations · 7 across the 10 of their papers we have counts for

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Showing 2023 · cs.CLShow all

5 papers · 2 filters

cs.CL2023

Context Compression for Auto-regressive Transformers with Sentinel Tokens

Siyu Ren, Qi Jia, Kenny Q. Zhu

The quadratic complexity of the attention module makes it gradually become the bulk of compute in Transformer-based LLMs during generation. Moreover, the excessive key-value cache…

cs.CL2023★ 2 cited

Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model

Qi Jia, Siyu Ren, Yizhu Liu +1

Despite tremendous improvements in natural language generation, summarization models still suffer from the unfaithfulness issue. Previous work evaluates faithfulness either using m…

cs.CL2023★ 1 cited

EMO: Earth Mover Distance Optimization for Auto-Regressive Language Modeling

Siyu Ren, Zhiyong Wu, Kenny Q. Zhu

Neural language models are probabilistic models of human text. They are predominantly trained using maximum likelihood estimation (MLE), which is equivalent to minimizing the forwa…

cs.CL2023★ 2 cited

Low-Rank Prune-And-Factorize for Language Model Compression

Siyu Ren, Kenny Q. Zhu

The components underpinning PLMs -- large weight matrices -- were shown to bear considerable redundancy. Matrix factorization, a well-established technique from matrix theory, has…

cs.CL2023

Pruning Pre-trained Language Models with Principled Importance and Self-regularization

Siyu Ren, Kenny Q. Zhu

Iterative pruning is one of the most effective compression methods for pre-trained language models. We discovered that finding the optimal pruning decision is an equality-constrain…