The Impact of Transformer Depth on Compositional Generalization in Language Models
Deeper transformer language models exhibit better compositional generalization than shallower models, even when controlling for total parameter count.
Exploring the Transferability of Interpretability Techniques from Transformer Language Models to Recurrent Neural Networks
Interpretability techniques originally designed for transformer language models, such as contrastive activation addition, the tuned lens, and probing for latent knowledge, can be effectively applied to state-of-the-art recurrent neural network architectures like Mamba and RWKV, with similar performance to transformers.
RWKV-5 (Eagle) and RWKV-6 (Finch): Efficient and Expressive Sequence Models with Matrix-Valued States and Dynamic Recurrence
The authors present two new sequence model architectures, Eagle (RWKV-5) and Finch (RWKV-6), that improve upon the RWKV-4 architecture by incorporating multi-headed matrix-valued states and dynamic recurrence mechanisms. These advancements enhance the models' expressivity while maintaining the efficient inference and training characteristics of RNNs.
Efficient Cross-Architecture Transfer Learning for Low-Cost Inference Transformer Models
Cross-Architecture Transfer Learning (XATL) can significantly reduce the training time and improve the performance of Low-Cost Inference (LCI) Transformer models by directly transferring compatible weights from pre-trained Transformer models, without the need to train the LCI models from scratch.
Mixture-of-Depths: Dynamically Allocating Compute in Transformer-based Language Models to Improve Efficiency
Transformer-based language models can learn to dynamically allocate compute resources across input sequences, optimizing the allocation along the sequence and across model depth. This allows for significant compute savings without sacrificing performance.
Unlocking Emergent Modularity in Large Language Models to Enhance Downstream Generalization
Emergent modularity spontaneously arises in pre-trained language models, and unlocking this modularity through fine-tuning as Emergent Mixture-of-Experts (EMoE) can improve downstream in-domain and out-of-domain generalization.
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