Summary of Lightweight Safety Classification Using Pruned Language Models, by Mason Sawtell et al.
Lightweight Safety Classification Using Pruned Language Models
by Mason Sawtell, Tula Masterman, Sandi Besen, Jim Brown
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper presents a novel technique for classifying content safety and detecting prompt injections in Large Language Models (LLMs). The approach, called Layer Enhanced Classification (LEC), uses Penalized Logistic Regression (PLR) to classify the hidden state of an LLM’s optimal intermediate transformer layer. By combining the efficiency of PLR with the language understanding capabilities of LLMs, LEC achieves superior performance compared to GPT-4o and special-purpose models fine-tuned for specific tasks. The study finds that small general-purpose models (e.g., Qwen 2.5 sizes 0.5B, 1.5B, and 3B) and transformer-based architectures like DeBERTa v3 can be used as robust feature extractors, allowing simple classifiers to be trained on fewer than 100 high-quality examples. The results indicate that a single general-purpose LLM can classify content safety, detect prompt injections, and generate output tokens simultaneously. Alternatively, these small LLMs can be pruned to the optimal intermediate layer and used exclusively as robust feature extractors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding what we say. It introduces a new way to teach computers to tell good from bad language. The new method uses special computer models called Large Language Models (LLMs) and works really well. These LLMs can also detect when someone is trying to trick them into saying something they don’t mean to say. The study shows that even small versions of these models can be very helpful in doing this job. This means we might not need many different types of computer models to do all sorts of language tasks. |
Keywords
» Artificial intelligence » Classification » Gpt » Language understanding » Logistic regression » Prompt » Transformer
