Summary of An Analysis Of Embedding Layers and Similarity Scores Using Siamese Neural Networks, by Yash Bingi and Yiqiao Yin
An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks
by Yash Bingi, Yiqiao Yin
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 Large Language Models (LLMs) are revolutionizing various applications, including language understanding, writing, and application development. A crucial aspect for optimal functionality is embedding layers. Our research delves into the algorithms used by leading companies such as OpenAI, Google’s PaLM, and BERT to transform input text into high-dimensional vectors. Using medical data, we analyzed similarity scores of each algorithm, noting differences in performance. To enhance models, we implemented Siamese Neural Networks and measured the carbon footprint per epoch of training. The study highlights the importance of considering the environmental impact of LLMs, particularly their carbon footprint. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having super smart computers that can understand and generate human-like text. These Large Language Models (LLMs) are getting better and better at doing tasks like language understanding and writing code. But did you know that how they “understand” words is really important? Our research looked at how different companies, like OpenAI and Google, do this word-understanding thing. We even used medical data to see how well each method works. To make things even better, we added a special kind of neural network. The study also shows that these super smart computers can have a big impact on the environment, so we should think about that too. |
Keywords
* Artificial intelligence * Bert * Embedding * Language understanding * Neural network * Palm
