Summary of The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More, by Ouail Kitouni et al.
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
by Ouail Kitouni, Niklas Nolte, Diane Bouchacourt, Adina Williams, Mike Rabbat, Mark Ibrahim
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 investigates the limitations of current language models in retrieving information accurately. While these models excel in generating text, they often produce factually incorrect generations, known as hallucinations, which hinders their ability to retrieve reliable information seen during training. The authors reframe this issue as a factorization curse, where models struggle to learn the same joint distribution under different orders of tokens. Through controlled experiments on WikiReversal and other settings, they demonstrate that popular large language models inherently fail to overcome the factorization curse using existing techniques like scale, reversed tokens, or bidirectional attention training. Instead, the authors propose factorization-agnostic objectives as a promising solution to mitigate this issue and potentially improve knowledge storage and planning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well current language models can find information they were trained on. These models are great at generating text, but sometimes they make things up that didn’t happen. This makes it hard for them to remember the right information when asked about it. The problem is like trying to learn a new skill by following different instructions each time. Researchers found that these language models have trouble learning from information in different orders. They tried different ways to fix this, but none of them worked. Instead, they suggest doing things differently to help the models remember and understand better. |
Keywords
» Artificial intelligence » Attention
