8 papers
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…
Cartridges: Lightweight and general-purpose long context representations via self-study
Sabri Eyuboglu, Ryan Ehrlich, Simran Arora +8
Large language models are often used to answer queries grounded in large text corpora (e.g. codebases, legal documents, or chat histories) by placing the entire corpus in the conte…
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…
Simple linear attention language models balance the recall-throughput tradeoff
Simran Arora, Sabri Eyuboglu, Michael Zhang +6
Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of atten…
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes
Simran Arora, Brandon Yang, Sabri Eyuboglu +4
A long standing goal of the data management community is to develop general, automated systems that ingest semi-structured documents and output queryable tables without human effor…