大型语言模型是位置一致的序数分类器吗？一项系统性评估

Computer Science > Computation and Language arXiv:2608.08869 (cs) [Submitted on 9 Aug 2026 (v1), last revised 9 Sep 2026 (this version, v2)] Title:Are LLMs Positionally Consistent Ordinal Classifiers? A Systematic Evaluation Authors:Yu Wang, Zhe Zhou, Menglin Liu, Ge Shi View a PDF of the paper titled Are LLMs Positionally Consistent Ordinal Classifiers? A Systematic Evaluation, by Yu Wang and 3 other authors View PDF HTML (experimental) Abstract:Large language models are increasingly used for ordinal classification, yet semantically equivalent changes to prompt organization can alter their predictions. We conduct systematic experiments to characterize positional bias from label order, demonstration order, and demonstration placement. First, we apply the three probes to ten frontier LLMs on a common ordinal-classification task; every model is sensitive to all three positional sources, showing that the problem is pervasive. Second, we vary eight prompt-, task-, and model-level factors across five datasets; accuracy and stability are often misaligned, and only lower scale cardinality consistently improves both. Third, we compare pointwise, pairwise, and listwise inference, alternative aggregation and debiasing methods, and joint configurations; the tested corrections do not provide a reliable remedy, while a comparison-based listwise formulation offers the best balance but transfers unevenly across models and bias sources. These findings show that positional robustness depends on the full system configuration rather than the model alone. Ordinal-classification systems should therefore be selected jointly for predictive performance and stability. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.08869 [cs.CL] (or arXiv:2608.08869v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.08869 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ge Shi [view email] [v1] Sun, 9 Aug 2026 19:19:08 UTC (356 KB) [v2] Wed, 9 Sep 2026 15:33:17 UTC (356 KB) Full-text links: Access Paper: View a PDF of the paper titled Are LLMs Positionally Consistent Ordinal Classifiers? A Systematic Evaluation, by Yu Wang and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)