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

• URL: http://arxiv.org/abs/2401.01989v3
• Date: Mon, 18 Mar 2024 20:09:01 GMT
• Title: Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias
• Authors: Anshuman Chhabra, Hadi Askari, Prasant Mohapatra,
• Abstract summary: We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias. Position bias captures the tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. Our findings lead to novel insights and discussion on performance and position bias of models for zero-shot summarization tasks.
• Score: 13.828653029379257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied previously in the literature. Position bias captures the tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. Through numerous experiments on four diverse real-world datasets, we study position bias in multiple LLM models such as GPT 3.5-Turbo, Llama-2, and Dolly-v2, as well as state-of-the-art pretrained encoder-decoder abstractive summarization models such as Pegasus and BART. Our findings lead to novel insights and discussion on performance and position bias of models for zero-shot summarization tasks.

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