Punching Cards On Large Language Models - Season 2, Episode 1
Jan 9, 2025
Sepp Hochreiter, a pioneer behind the LSTM model, and Alan Akbik, an expert in NLP, dive into the fascinating world of large language models. They discuss the evolution of language models and the promising potential of XLSTMs in AI coding. The conversation highlights the advantages of LSTMs versus transformers and introduces XLCMs, emphasizing their role in generative AI. They also touch on the cultural barriers to applying AI innovations in Europe, and the balance needed between AI use and traditional academic practices.
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Transformers vs. LSTMs Capabilities
- Transformers train faster due to parallelization, handling many words simultaneously.
- LSTMs read sequentially and have limited memory, being better at abstraction than memorization.
LSTM Efficiency vs. Transformer Scale
- Transformers excel in scalable training but are expensive in text generation.
- LSTMs handle long sequences efficiently with linear costs, making them cheaper in use.
XLSTM's Key Innovations
- XLSTM innovates by exponential gating and a large memory structure.
- These improvements help it revise stored information and handle complex memory dynamics better than LSTM.