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Found 10 papers, 3 papers with code
Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks
To address the gap in evaluating memory in LLMs, we introduce Sequence Order Recall Tasks (SORT), which we adapt from tasks used to study episodic memory in cognitive psychology.
OMPar: Automatic Parallelization with AI-Driven Source-to-Source Compilation
Manual parallelization of code remains a significant challenge due to the complexities of modern software systems and the widespread adoption of multi-core architectures.
MPIrigen: MPI Code Generation through Domain-Specific Language Models
This study first investigates the performance of state-of-the-art language models in generating MPI-based parallel programs.
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks
Specifically, we start with HPC as a domain and build an HPC-specific LM, named MonoCoder, which is orders of magnitude smaller than existing LMs but delivers better performance on non-HPC and HPC codes.
Scope is all you need: Transforming LLMs for HPC Code
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 address a variety of programming tasks.
Memory in humans and deep language models: Linking hypotheses for model augmentation
The computational complexity of the self-attention mechanism in Transformer models significantly limits their ability to generalize over long temporal durations.
Memory-Augmented Graph Neural Networks: A Brain-Inspired Review
We provide a comprehensive review of the existing literature on memory-augmented GNNs.
Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay
Altogether, we found that LSTM with the proposed forget gate can learn long-term dependencies, outperforming other recurrent networks in multiple domains; such gating mechanism can be integrated into other architectures for improving the learning of long timescale information in recurrent neural networks.
Multi-timescale Representation Learning in LSTM Language Models
Earlier work has demonstrated that dependencies in natural language tend to decay with distance between words according to a power law.
