Summary of Pldr-llm: Large Language Model From Power Law Decoder Representations, by Burc Gokden
PLDR-LLM: Large Language Model from Power Law Decoder Representations
by Burc Gokden
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large Language Model from Power Law Decoder Representations (PLDR-LLM) is a novel language model that utilizes non-linear and linear transformations through the Power Law Graph Attention mechanism to generate well-defined deductive and inductive outputs. Pretrained with a small batch size of 32 and approximately 8 billion tokens from the RefinedWeb dataset, PLDR-LLMs of varying layer sizes demonstrate competitive performance in zero-shot and few-shot settings compared to scaled dot-product LLMs of similar model size reported in the literature. The deductive outputs of PLDR-LLMs can be leveraged to compare model characteristics or improve performance by introducing the Directed Acyclic Graph (DAG) loss as a metric and regularizer. Our findings indicate that the initial maximum learning rate and warm-up steps have a lasting impact on deductive outputs throughout pretraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The PLDR-LLM is a new kind of language model that helps computers understand and generate text. It uses a special way to look at words called Power Law Graph Attention, which makes it good at coming up with logical conclusions and making connections between ideas. The model was trained on a large dataset of text and showed that it can do just as well as other similar models in some situations. This is important because it could help computers get better at understanding natural language and generating text that’s helpful to humans. |
Keywords
» Artificial intelligence » Attention » Decoder » Dot product » Few shot » Language model » Large language model » Pretraining » Zero shot
