Summary of Large Language Models Are Overparameterized Text Encoders, by Thennal D K et al.
Large Language Models Are Overparameterized Text Encoders
by Thennal D K, Tim Fischer, Chris Biemann
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 language models (LLMs) excel as text embedding models when fine-tuned with supervised contrastive training. However, their large size poses challenges in terms of inference time and memory requirements. This paper introduces a pruning method that reduces the last p% layers of an LLM before supervised training for 1000 steps, achieving proportional reductions in memory and inference time. The authors evaluate four state-of-the-art LLMs on text embedding tasks, demonstrating that their method can prune up to 30% of layers with negligible performance impact or up to 80% with a modest drop. Their proposed ^3 strategy provides two optimal pruning configurations: a large variant for minimal performance loss and a small variant for resource-constrained settings. The results demonstrate that LLMs are overparameterized for text embedding tasks, supporting the feasibility of pruning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about big language models (LLMs) that can be used to understand text. These models are really good at their job, but they’re also very large and take a long time to process information. The researchers found a way to make these models smaller while still keeping them effective. They tested four different LLMs and showed that they could reduce the size of each model by 30% without sacrificing performance or 80% with only a slight drop. This is important because it means we can use these models more efficiently, making it easier for computers to understand text. |
Keywords
» Artificial intelligence » Embedding » Inference » Pruning » Supervised
