Summary of Algorithmic Language Models with Neurally Compiled Libraries, by Lucas Saldyt et al.
Algorithmic Language Models with Neurally Compiled Libraries
by Lucas Saldyt, Subbarao Kambhampati
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Programming Languages (cs.PL)
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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 approach aims to enhance Large Language Models (LLMs) by integrating a library of fundamental operations and sophisticated differentiable programs. This is achieved by augmenting the transformer architecture with memory, registers, basic operations, and adaptive recurrence. The method also involves directly compiling algorithms into a differentiable starting library, which can be used for optimization. The feasibility of this approach is explored through fine-tuning small transformers on simple algorithmic tasks with variable computational depth. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are limited in their ability to reason and plan due to the limitations of neural network optimization algorithms and architectural inexpressivity. To overcome these limitations, a library of fundamental operations and sophisticated differentiable programs can be added to LLMs. This allows common algorithms to be used without needing to be learned from scratch. The proposed approach is an innovative way to improve LLMs and enhance their ability to reason and plan. |
Keywords
» Artificial intelligence » Fine tuning » Neural network » Optimization » Transformer
