Summary of Towards Signal Processing in Large Language Models, by Prateek Verma et al.
Towards Signal Processing In Large Language Models
by Prateek Verma, Mert Pilanci
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method applies signal processing techniques to Large Language Models (LLMs) to improve their performance. By decomposing intermediate activation signals into time-frequency representations, similar to Fourier Transforms, the model learns to filter and reconstruct these signals to predict the next token given the previous context. This approach is shown to achieve faster convergence and improved performance for GPT-like architectures with only a small increase in parameters. The work aims to bridge the gap between signal processing and LLMs, opening up new possibilities for algorithms that explore signal processing inside neural architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper combines two fields, signal processing and large language models, by applying signal processing techniques to Large Language Models (LLMs). This helps improve their performance. The method works by breaking down the signals inside the model into different parts, like a special kind of filter. This helps the model make better predictions about what comes next. The results show that this approach can help LLMs learn faster and be more accurate with only a few extra bits of information. |
Keywords
» Artificial intelligence » Gpt » Signal processing » Token
