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Delight: Deep and light-weight transformer.arXiv preprint arXiv:2008.00623,

Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer, Luke Zettlemoyer, Hannaneh Hajishirzi · 2008 · arXiv 2008.00623

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
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cs.CL 1 cs.LG 1

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2026 2

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CONDITIONAL 1 UNVERDICTED 1

representative citing papers

Tapered Language Models

cs.LG · 2026-06-22 · unverdicted · novelty 7.0

Tapered Language Models monotonically decrease MLP width across depth with a cosine schedule, yielding better perplexity and downstream performance than uniform-width baselines across multiple architectures and scales at no extra cost.

Variable-Width Transformers

cs.CL · 2026-06-16 · conditional · novelty 6.0

×-shaped variable-width transformers outperform parameter-matched uniform baselines on language modeling loss with 22% fewer FLOPs and 15% smaller KV cache.

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Showing 2 of 2 citing papers.

  • Tapered Language Models cs.LG · 2026-06-22 · unverdicted · none · ref 19

    Tapered Language Models monotonically decrease MLP width across depth with a cosine schedule, yielding better perplexity and downstream performance than uniform-width baselines across multiple architectures and scales at no extra cost.

  • Variable-Width Transformers cs.CL · 2026-06-16 · conditional · none · ref 24

    ×-shaped variable-width transformers outperform parameter-matched uniform baselines on language modeling loss with 22% fewer FLOPs and 15% smaller KV cache.