Enhancing Legal Analysis with AI: The Benefits of Clamping to Behaviours

Enhancing Legal Analysis with AI: The Benefits of Clamping to Behaviours

Having read about breaking into the black box that is AI that Anthropic posted the other day, I decided to read the (slightly too technical for me) paper by Anthropic and managed to grasp enough to see the practical benefits for legal applications. Anthropic’s paper: Scaling Monosemanticity explores how clamping to specific behaviours within large language models (LLMs) like Claude 3 can significantly enhance their utility.

What is Clamping to Behaviours?

Clamping to behaviours involves manipulating specific neuron activations within an AI model to control its responses. This technique allows us to isolate and adjust particular features, so fine-tuning the model’s behaviour to meet specific requirements

This is different then just prompting by saying "You will respond like a lawyer" or "You will respond in a positive way" it's aligning to those behaviours at a much more fundamental level.

Bias Mitigation

Legal assessments require impartiality. By clamping features associated with biases, AI can provide fairer evaluations, avoiding prejudiced outcomes that might arise from skewed training data.

Consistency in Tasks

Legal tasks demand consistent application of standards. Clamping can ensure that the AI adheres uniformly to these standards, minimising discrepancies in analysis or recommendations.

Fraud Detection

Clamping could enhance the detection of fraudulent activities by focusing on suspicious patterns or deceptive language in legal documents, making the AI more effective in identifying irregularities.


It'll be interesting to see if (or can) this ability will be brought to the consumption level, where enterprises will be able to work with AI providers to define behaviours to created models clamped those needs.

We also need to think about the ethical considerations, the cat (at a research level) is out of the bag, soon models may well be released that at their core clamp to unethical behaviours and perform better at those tasks than just having an uncensored model with a good (bad) prompt.

Anthropic Paper: https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html