Summary of Chatgpt Alternative Solutions: Large Language Models Survey, by Hanieh Alipour et al.
ChatGPT Alternative Solutions: Large Language Models Survey
by Hanieh Alipour, Nick Pendar, Kohinoor Roy
First submitted to arxiv on: 21 Mar 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 abstract discusses the advancements in Large Language Models (LLMs) and their applications across various domains. The development of LLMs has led to significant research contributions, including improvements in neural network architecture, context length enhancements, model alignment, training datasets, benchmarking, and efficiency improvements. The introduction of ChatGPT, a powerful AI chatbot grounded in LLMs, has garnered widespread attention. The evolving technology of LLMs is reshaping the landscape of the entire AI community, promising a revolutionary shift in creating and employing AI algorithms. The survey provides an up-to-the-minute review of the literature, examining multiple LLM models and charting a course that identifies existing challenges and potential future research trajectories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have become incredibly powerful tools for processing natural language. This has led to many exciting applications across various fields. Researchers are working hard to improve these models by tweaking their architecture, training them on larger datasets, and making them more efficient. One notable example is ChatGPT, a chatbot that uses LLMs to understand and respond to human language. The development of LLMs is changing the way we create and use AI algorithms. This survey will take you on a journey through the current state of LLM research, highlighting what’s been achieved so far and where things might go from here. |
Keywords
» Artificial intelligence » Alignment » Attention » Context length » Neural network
