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We characterize and study zero-shot abstractive summarization in LargeLanguage Models (LLMs) by measuring position bias, which we propose as ageneral formulation of the more restrictive lead bias phenomenon studiedpreviously in the literature. Position bias captures the tendency of a modelunfairly prioritizing information from certain parts of the input text overothers, leading to undesirable behavior. Through numerous experiments on fourdiverse real-world datasets, we study position bias in multiple LLM models suchas GPT 3.5-Turbo, Llama-2, and Dolly-v2, as well as state-of-the-art pretrainedencoder-decoder abstractive summarization models such as Pegasus and BART. Ourfindings lead to novel insights and discussion on performance and position biasof models for zero-shot summarization tasks.

Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias