Summary of Do Language Models Plan Ahead For Future Tokens?, by Wilson Wu et al.
Do language models plan ahead for future tokens?
by Wilson Wu, John X. Morris, Lionel Levine
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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 A new study delves into the behavior of transformers during inference, examining whether they “think ahead” and prepare information for future steps. The authors propose two explanations: pre-caching, where off-diagonal gradient terms from training enable the model to compute features relevant for future steps; or breadcrumbs, where the most relevant features for a given step are also useful for future inferences. To test these hypotheses, the researchers employed myopic training, which prevents gradients from propagating to past timesteps. Their experiments on synthetic and autoregressive language modeling tasks suggest that pre-caching may be at play, particularly as model scale increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Do transformers have a special ability to prepare for future steps during inference? Researchers investigate this phenomenon, proposing two explanations: pre-caching and breadcrumbs. They test these ideas by training models without gradients flowing back in time. In synthetic data experiments, they find evidence of pre-caching, while in language modeling tasks, the results are more mixed. |
Keywords
* Artificial intelligence * Autoregressive * Inference * Synthetic data
