39 citations · 44 across the 7 of their papers we have counts for
6 papers · 1 filter
MLPs are Hebbians: Constructing Efficient Fact-Storing MLPs for Transformers
Roberto Garcia, Jerry Liu, Ronny Junkins +3
Large language models (LLMs) store factual knowledge in their parameters. While recent work has shown that this knowledge resides in MLP layers, existing constructive and mechanist…
Constructing Efficient Fact-Storing MLPs for Transformers
Owen Dugan, Roberto Garcia, Ronny Junkins +5
The success of large language models (LLMs) can be attributed in part to their ability to efficiently store factual knowledge as key-value mappings within their MLP parameters. Rec…
Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA Adapters
Roberto Garcia, Jerry Liu, Daniel Sorvisto +1
Large Language Models (LLMs) are computationally intensive, particularly during inference. Neuron-adaptive techniques, which selectively activate neurons in Multi-Layer Perceptron…
Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models
Avanika Narayan, Dan Biderman, Sabri Eyuboglu +4
We investigate an emerging setup in which a small, on-device language model (LM) with access to local data communicates with a frontier, cloud-hosted LM to solve real-world tasks i…
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture
Daniel Y. Fu, Simran Arora, Jessica Grogan +7
Machine learning models are increasingly being scaled in both sequence length and model dimension to reach longer contexts and better performance. However, existing architectures s…
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Sabri Eyuboglu, Maya Varma, Khaled Saab +5
Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly…