Measuring the Inconsistency of Large Language Models in Preferential Ranking

• URL: http://arxiv.org/abs/2410.08851v1
• Date: Fri, 11 Oct 2024 14:27:18 GMT
• Title: Measuring the Inconsistency of Large Language Models in Preferential Ranking
• Abstract summary: This study investigates the capacity of large language models to provide consistent ordinal preferences. We introduce a formalization of consistency based on order theory, outlining criteria such as transitivity, asymmetry, reversibility, and independence from irrelevant alternatives. Our diagnostic experiments on selected state-of-the-art LLMs reveal their inability to meet these criteria, indicating a strong positional bias and poor transitivity.
• Score: 6.723531714964794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite large language models' (LLMs) recent advancements, their bias and hallucination issues persist, and their ability to offer consistent preferential rankings remains underexplored. This study investigates the capacity of LLMs to provide consistent ordinal preferences, a crucial aspect in scenarios with dense decision space or lacking absolute answers. We introduce a formalization of consistency based on order theory, outlining criteria such as transitivity, asymmetry, reversibility, and independence from irrelevant alternatives. Our diagnostic experiments on selected state-of-the-art LLMs reveal their inability to meet these criteria, indicating a strong positional bias and poor transitivity, with preferences easily swayed by irrelevant alternatives. These findings highlight a significant inconsistency in LLM-generated preferential rankings, underscoring the need for further research to address these limitations.

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