Decrease LM discrimination via careful prompt engineering
Use prompt engineering to significantly decrease positive and negative discrimination in LM outputs.
Employ careful prompt engineering techniques to significantly decrease both positive and negative discrimination. This involves crafting prompts that guide the model towards fairer and more equitable decision-making.
Positive and negative discrimination in language model outputs.
Unmitigated biases in the model or training data, leading to unfair or prejudiced outputs.
This fix focuses on using prompt engineering as a method to reduce discriminatory outputs from language models. By carefully designing prompts, developers can steer the model towards more equitable and unbiased responses.
Well-crafted prompts can set clearer expectations for the model, constrain its response generation, and explicitly guide it away from biased reasoning or output.
When prompt engineering alone is insufficient to address deeply ingrained model biases, requiring model retraining or fine-tuning.
- Evaluating and Mitigating Discrimination in Language Model Decisions ↗Anthropic Research · Anthropic Research · Aug 5, 2024 · quoted as shared
First seen on Subvolts Aug 5, 2024. Updated Sep 11, 2026. Spotted a problem? Tell us.
- Evaluating and Mitigating Discrimination in Language Model DecisionsAnthropic · Anthropic Research · Aug 5, 2024
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- Anthropic Research (Anthropic Research). Subvolts collected, normalized and credited it; the link above opens the original.