5 papers
Adaptive Retrieval helps Reasoning in LLMs -- but mostly if it's not used
Srijan Shakya, Anamaria-Roberta Hartl, Sepp Hochreiter +1
Large Language Models (LLMs) often falter in complex reasoning tasks due to their static, parametric knowledge, leading to hallucinations and poor performance in specialized domain…
Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels
Maximilian Beck, Korbinian Pöppel, Phillip Lippe +1
Linear RNNs with gating recently demonstrated competitive performance compared to Transformers in language modeling. Although their linear compute scaling in sequence length offers…
pLSTM: parallelizable Linear Source Transition Mark networks
Korbinian Pöppel, Richard Freinschlag, Thomas Schmied +2
Modern recurrent architectures, such as xLSTM and Mamba, have recently challenged the Transformer in language modeling. However, their structure constrains their applicability to s…
xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
Maximilian Beck, Korbinian Pöppel, Phillip Lippe +5
Recent breakthroughs in solving reasoning, math and coding problems with Large Language Models (LLMs) have been enabled by investing substantial computation budgets at inference ti…
FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware
Korbinian Pöppel, Maximilian Beck, Sepp Hochreiter
While Transformers and other sequence-parallelizable neural network architectures seem like the current state of the art in sequence modeling, they specifically lack state-tracking…