2023-04-10
The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT
Original. The accidental leak of "Llama," an LLM model, has sparked an open-source alternative movement to models like GPT-4 and Cohere that are only available via APIs. Since the leak, Alpaca, Vicuna, Koala, ColossalChat, and ChatLLama, among other models, have been released, all built on top of Llama. The open-source alternatives to these models have not shown the same performance level until the Llama release. The leak of Llama has turned out to be one of the biggest sparks of innovation in the open-source LLM space, where a war between open-source and API-based distribution is looming. The LLama effect has proven that open source is a viable distribution mechanism for foundational models, and there are some interesting sources of innovation in the LLM space.
Discussion Service. 'The LLama Effect' leak has sparked open-source alternatives to ChatGPT, gaining attention from tech experts. GPT-4 demonstrates more significant intelligence, offering reasoning and generalizing abilities from its predecessor, GPT-3. AI text-only models grasp spatial reasoning and can understand puzzles, but manual fine-tuning is necessary. There is debate around language models' actual level of learning happening, with the hope of an accessible and democratized AI future. LLaMA leak has led to open-source optimization on all platforms; however, experts criticize software piracy and AI companies' regulation. OpenAI may face legal challenges for using models to train commercial outcomes. ChatGPT accuracy is divergent, with some considering it useless, while others claim GPT-4 improves in some aspects. Bing/Sydney and ChatGPT have different personalities. The post does not provide new technology facts but discusses the nature of text compression.
From deep to long learning?
Original. Stanford researchers from Hazy Research lab are improving sequence length in machine learning foundation models, with focus on creating nearly linear time models in sequence length that can lead to context lengths of millions or even billions. The Hyena model has scalability in sequence length up to 2k using a small neural network which parametrizes the convolutional filters implicitly via another small neural network with an implementation time of O(NlogN). Researchers are exploring learning matrices and their connection to language applications. N/A.
Discussion Service. Stanford researchers explore cost reduction for self-attention in long sequences. Optimizing computation for GPUs and co-processors can optimize LLMs. Skepticism around longer-context models, coupling LLMs with other systems may create new solutions. GPT-4 release leads to new research on next-token prediction, potential breakthroughs in associative long-term memory. Understanding K,Q,V representation is crucial while RNNs and transformers have implications for democratizing AI. Longer context lengths may be considered a new form of search.