Summary of Bayesian Example Selection Improves In-context Learning For Speech, Text, and Visual Modalities, by Siyin Wang et al.
Bayesian Example Selection Improves In-Context Learning for Speech, Text, and Visual Modalities
by Siyin Wang, Chao-Han Huck Yang, Ji Wu, Chao Zhang
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 paper proposes a novel Bayesian in-Context example Selection (ByCS) method for in-context learning (ICL), which adapts large language models to new tasks through dialogue history without updating model parameters. ByCS selects in-context examples based on their inverse inference results, focusing on the likelihood of accurate posterior probability. Experimental results demonstrate the efficacy and robustness of ByCS on various models, tasks, and modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way for large language models to learn new things by looking at just a few examples. This is useful because it means you don’t need to update the model’s settings every time you want it to do something new. The problem is that the quality of these examples matters a lot, so the researchers came up with a new way to choose good ones. They call it Bayesian in-Context example Selection (ByCS). ByCS works by looking at how likely an example is to be correct, and then choosing the best ones based on that. The results show that this method works well for different models, tasks, and types of data. |
Keywords
» Artificial intelligence » Inference » Likelihood » Probability
