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Lightweight Safety Classification Using Pruned Language Models (arxiv.org)

19 points by sandijean90 ↗ HN
Layer Enhanced Classification (LEC) is a novel technique that outperforms current industry leaders like GPT-4o, LlamaGuards 1 and 8B, and deBERTa v3 Prompt Injection v2 for content safety and prompt injection tasks.

We prove that the intermediate hidden layers in transformers are robust feature extractors for text classification.

On content safety, LEC models achieved a 0.96 F1 score vs GPT-4o's 0.82 and Llama Guard 8B's 0.71.The LEC models were able to outperform the other models with only 15 training examples for binary classification and 50 examples for multi-class classification across 66 categories.

On prompt injection,LEC models achieved a 0.98 F1 score vs GPT-4o's 0.92 and deBERTa v3 Prompt Injection v2's 0.73. LEC models were able to outperform deBERTa with only 5 training examples and GPT-4o with only 55 training examples.

Read the full paper and our approach here: https://arxiv.org/abs/2412.13435

5 comments

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bberenberg
Are these models available for us to try out?
gdiamos
This is really easy to set up - and is much more accurate than asking the LLM to predict True/False.

Just feed the outputs of an embedding API into logistic regression, e.g. from sklearn.

  import voyageai
  
  vo = voyageai.Client()
  # This will automatically use the environment variable VOYAGE_API_KEY.
  # Alternatively, you can use vo = voyageai.Client(api_key="<your secret key>")

  texts = ["Sample text 1", "Sample text 2"]

  result = vo.embed(texts, model="voyage-2", input_type="document")
  print(result.embeddings[0])
https://scikit-learn.org/1.5/modules/generated/sklearn.linea...
flatpepsi17
I'd pay good money for a local LLM with no "content safety" at all.