Summary of The Remarkable Robustness Of Llms: Stages Of Inference?, by Vedang Lad et al.
The Remarkable Robustness of LLMs: Stages of Inference?
by Vedang Lad, Wes Gurnee, Max Tegmark
First submitted to arxiv on: 27 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 This research paper investigates the robustness of Large Language Models (LLMs) by deleting and swapping adjacent layers. The study finds that these interventions retain a significant portion of the original model’s accuracy without fine-tuning, with models having more layers exhibiting greater robustness. The researchers propose four universal stages of inference across eight different LLMs: detokenization, feature engineering, prediction ensembling, and residual sharpening. These stages are characterized by distinct patterns of information integration, refinement, and alignment. The paper suggests that understanding these stages can lead to improvements in LLM performance and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how Large Language Models (LLMs) stay strong even when parts are removed or changed. The researchers did this by deleting and swapping adjacent layers in the models. They found that most of the original model’s accuracy is still there, without needing any extra training. The more layers a model has, the more robust it becomes. The team also identified four stages that LLMs go through to make predictions: detokenization, feature engineering, prediction ensembling, and residual sharpening. This could help us build better language models in the future. |
Keywords
» Artificial intelligence » Alignment » Feature engineering » Fine tuning » Inference
