Month in 4 Papers (May 2024)

Author(s): Ala Falaki, PhD

Originally published on Towards AI.

This month, we explore optimizing model merging, fine-tuning supervision, autonomous coding, and selective token relevance in NLP.

This series of posts is designed to bring you the newest findings and developments in the NLP field. I’ll delve into four significant research papers each month, offering a comprehensive summary. Be sure to visit my blog regularly or subscribe to my newsletter for monthly updates. Let’s dive in!

📝 Evolutionary Optimization of Model Merging Recipes [paper] [code] [demonstration]

Model merging has recently gained significant attention within the LLM community. The team at SakanaAI introduced an evolutionary approach that emphasizes both manipulating the parameters and the order of layers. The proposed approach, a hybrid of two methods, showed impressive performance on models fine-tuned from the same foundational pre-trained model. Moreover, their primary focus was cross-domain merging. We will see two examples of this at the end.

This method merges the parameter space (PS) — for example, by averaging the weights of two models — and the data flow space (DFS), which involves identifying the optimal sequence for mixing and matching the layers’ order. They suggested a fusion of these two approaches. The PS procedure involves selecting various data points from the search space and iteratively seeking the… Read the full blog for free on Medium.

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Published via Towards AI