Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond
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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.
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36 more
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Built on by
Citing papers per year: 2023: 5; 2024: 2.
7 papers.
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
- 2023PaniniQA: Enhancing Patient Education Through Interactive Question Answeringcited by 21
- 2024Automatic Metrics in Natural Language Generation: A Survey of Current Evaluation Practicescited by 19
- 2023UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?cited by 14
- 2023Improving Summarization with Human Editscited by 7
- 2023To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenderscited by 2
- 2024LargePiG: Your Large Language Model is Secretly a Pointer Generatorcited by 0
- 2024LargePiG: Your Large Language Model is Secretly a Pointer Generator15 Oct 2024
- 2024Automatic Metrics in Natural Language Generation: A Survey of Current Evaluation Practices17 Aug 2024
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions9 Nov 2023
- 2023To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders21 Oct 2023
- 2023Improving Summarization with Human Edits9 Oct 2023
- 2023PaniniQA: Enhancing Patient Education Through Interactive Question Answering7 Aug 2023
- 2023UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?29 Jun 2023