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In the LLM context, selection bias specifically refers to a model's content-irrelevant preference regarding the presentation position, order, or format of options in multiple-choice tasks, with common manifestations including primacy bias, recency bias, or a systematic preference for particular letter labels.
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10/27/2026
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UnverifiedThe phenomenon where classification models are sensitive to the order of candidate answers is called position bias, where candidates appearing near the end of a sequence sometimes receive higher weights in unidirectional autoregressive models.78% similarUnverifiedIn the context of LLMs, bias refers to model outputs that systematically favor a particular group, stance, or conclusion, originating from uneven distributions in training data.70% similarUnverifiedLLM-as-a-Judge has three known biases: a tendency to prefer longer answers, greater tolerance for the outputs of its own model series, and sensitivity to position (options appearing earlier are more likely to be selected).70% similarUnverifiedModel selection must be weighed by scenario: fine-tuned models still have an advantage when there are strict reference template requirements for text style, while prompted LLMs are more reliable when factual faithfulness and robustness on large-scale graphs matter more.70% similarUnverifiedWhen an LLM call is constrained to choosing an answer from a fixed set of options, it is essentially a classifier rather than a generation task.68% similar
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