mlsanityData to 7 Oct 2026

Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond

arXiv 2305.12289 ↗First posted 20 May 2023cs.CLcited by 7

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Abstract (arXiv)

Is the output softmax layer, which is adopted by most language models (LMs), always the best way to compute the next word probability? Given so many attention layers in a modern transformer-based LM, are the pointer networks redundant nowadays? In this study, we discover that the answers to both questions are no. This is because the softmax bottleneck sometimes prevents the LMs from predicting the desired distribution and the pointer networks can be used to break the bottleneck efficiently. Based on the finding, we propose several softmax alternatives by simplifying the pointer networks and accelerating the word-by-word rerankers. In GPT-2, our proposals are significantly better and more efficient than mixture of softmax, a state-of-the-art softmax alternative. In summarization experiments, without significantly decreasing its training/testing speed, our best method based on T5-Small improves factCC score by 2 points in CNN/DM and XSUM dataset, and improves MAUVE scores by 30% in BookSum paragraph-level dataset.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 48 of its 67 references that are in the map. First posted on arXiv 20 May 2023.

Builds on

48 of 67 references.

References · 48

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36 more
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Citing papers per year: 2023: 5; 2024: 2.

7 papers.