This 'position bias' means that, if a lawyer is using an LLM-powered virtual assistant to retrieve a certain phrase in a 30-page affidavit, the LLM is more likely to find the right text if it is on the initial or final pages. They created a theoretical framework to study how information flows through the machine-learning architecture that forms the backbone of LLMs. They found that certain design choices which control how the model processes input data can cause position bias. Their experiments revealed that model architectures, particularly those affecting how information is spread across input words within the model, can give rise to or intensify position bias, and that training data also contribute to the problem. This could lead to more reliable chatbots that stay on topic during long conversations, medical AI systems that reason more fairly when handling a trove of patient data, and code assistants that pay closer attention to all parts of a program. 'These models are black...
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