Summary of Can Custom Models Learn In-context? An Exploration Of Hybrid Architecture Performance on In-context Learning Tasks, by Ryan Campbell et al.
Can Custom Models Learn In-Context? An Exploration of Hybrid Architecture Performance on In-Context Learning Tasks
by Ryan Campbell, Nelson Lojo, Kesava Viswanadha, Christoffer Grondal Tryggestad, Derrick Han Sun, Sriteja Vijapurapu, August Rolfsen, Anant Sahai
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper explores the phenomenon of In-Context Learning (ICL) in Multi-Headed Attention (MHA) models with absolute positional embedding. The study examines the implications of architectural differences between GPT-2, LLaMa, and Mamba sequence models on ICL accuracy and training efficiency. The researchers extend previous work by investigating hybrid models that combine parameters from different architectures, such as GPT-2/LLaMa and LLaMa/Mamba hybrids. They find that certain architectural changes can lead to suboptimal predictors or slower convergence, while others show improved performance. A new metric, the “ICL regression score”, is proposed to measure a model’s overall performance on a specific task. The study also highlights the importance of compute limitations and proposes a Python package for reproducible research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines learn from context without updating their parameters. It studies different types of sequence models, like GPT-2, LLaMa, and Mamba, to see how they affect learning. The researchers also test combining parts of these models together to create new hybrids. They find that some changes can make the model worse or slower, but others can improve its performance. A new way to measure a model’s performance is also introduced. This study shows how important it is to consider limitations when doing research and provides a way for other researchers to repeat their experiments. |
Keywords
» Artificial intelligence » Attention » Embedding » Gpt » Llama » Regression
