Summary of Does Learning the Right Latent Variables Necessarily Improve In-context Learning?, by Sarthak Mittal et al.
Does learning the right latent variables necessarily improve in-context learning?
by Sarthak Mittal, Eric Elmoznino, Leo Gagnon, Sangnie Bhardwaj, Dhanya Sridhar, Guillaume Lajoie
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large autoregressive models like Transformers have shown promise in solving tasks through in-context learning (ICL) without requiring new weights. This has sparked interest in efficiently solving new tasks. For many tasks, such as linear regression, data factorizes: examples are independent given a task latent that generates the data. The optimal predictor leverages this factorization by inferring task latents. However, it is unclear whether Transformers implicitly do so or exploit heuristics and statistical shortcuts enabled by attention layers. Our paper systematically investigates the effect of explicitly inferring task latents in Transformers. We modified the architecture with a bottleneck designed to prevent shortcuts and compare performance against standard Transformers across various ICL tasks. Surprisingly, we found little difference between the two; biasing towards task-relevant latent variables does not lead to better out-of-distribution performance. Instead, we found that while the bottleneck learns to extract latent task variables from context, downstream processing struggles to utilize them for robust prediction. Our study highlights the limitations of Transformers in achieving structured ICL solutions that generalize and shows that inferring the right latents aids interpretability but is not sufficient to alleviate this problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how a type of artificial intelligence called Transformers can learn new things without needing to learn everything from scratch. The authors want to know if these models are really using a clever trick to solve problems, or just finding shortcuts. They tested the model by adding something that would stop it from taking shortcuts and comparing it to the normal way the model works. Surprisingly, they found that making this change didn’t make the model any better at solving new problems. Instead, they discovered that while the model can learn to identify the key things needed to solve a problem, it struggles to use that information to actually solve the problem well. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Linear regression
