A recurrent-depth architecture enables language models to improve reasoning performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
LPES uses per-layer scaling factors optimized by a genetic algorithm with Bézier curves to balance attention and improve long-context LLM performance by up to 11.2% on key-value retrieval.
Experimental study finds Recursive-Transformer for ASR encoders achieves comparable performance with 66% fewer parameters when limited recursion is applied in the latent space.
ART replaces uniform attention in shallow LLM layers with local attention patterns to reduce hallucinations across multiple model architectures.
citing papers explorer
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Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
A recurrent-depth architecture enables language models to improve reasoning performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.
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Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling
LPES uses per-layer scaling factors optimized by a genetic algorithm with Bézier curves to balance attention and improve long-context LLM performance by up to 11.2% on key-value retrieval.
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Rethinking Depth: A study of the Recursive-Transformer for Speech Recognition
Experimental study finds Recursive-Transformer for ASR encoders achieves comparable performance with 66% fewer parameters when limited recursion is applied in the latent space.
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ART: Attention Replacement Technique to Improve Factuality in LLMs
ART replaces uniform attention in shallow LLM layers with local attention patterns to reduce hallucinations across multiple model architectures.