6 citations · 15 across the 9 of their papers we have counts for
6 papers · 1 filter
Toward Optimal Search and Retrieval for RAG
Alexandria Leto, Cecilia Aguerrebere, Ishwar Bhati +3
Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems f…
Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks
Mathis Pink, Vy A. Vo, Qinyuan Wu +7
Current LLM benchmarks focus on evaluating models' memory of facts and semantic relations, primarily assessing semantic aspects of long-term memory. However, in humans, long-term m…
OMPar: Automatic Parallelization with AI-Driven Source-to-Source Compilation
Tal Kadosh, Niranjan Hasabnis, Prema Soundararajan +5
Manual parallelization of code remains a significant challenge due to the complexities of modern software systems and the widespread adoption of multi-core architectures. This pape…
Scope is all you need: Transforming LLMs for HPC Code
Tal Kadosh, Niranjan Hasabnis, Vy A. Vo +9
With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to addres…
Memory in humans and deep language models: Linking hypotheses for model augmentation
Omri Raccah, Phoebe Chen, Ted L. Willke +2
The computational complexity of the self-attention mechanism in Transformer models significantly limits their ability to generalize over long temporal durations. Memory-augmentatio…
Multi-timescale Representation Learning in LSTM Language Models
Shivangi Mahto, Vy A. Vo, Javier S. Turek +1
Language models must capture statistical dependencies between words at timescales ranging from very short to very long. Earlier work has demonstrated that dependencies in natural l…