Position of Uncertainty: A Cross-Linguistic Study of Positional Bias in Large Language Models

• URL: http://arxiv.org/abs/2505.16134v1
• Date: Thu, 22 May 2025 02:23:00 GMT
• Title: Position of Uncertainty: A Cross-Linguistic Study of Positional Bias in Large Language Models
• Authors: Menschikov Mikhail, Alexander Kharitonov, Maiia Kotyga, Vadim Porvatov, Anna Zhukovskaya, David Kagramanyan, Egor Shvetsov, Evgeny Burnaev,
• Abstract summary: We study how positional bias interacts with model uncertainty, syntax, and prompting.<n>We present a cross-linguistic study across five typologically distinct languages.
• Score: 49.46335932942725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models exhibit positional bias -- systematic neglect of information at specific context positions -- yet its interplay with linguistic diversity remains poorly understood. We present a cross-linguistic study across five typologically distinct languages (English, Russian, German, Hindi, Vietnamese), examining how positional bias interacts with model uncertainty, syntax, and prompting. Key findings: (1) Positional bias is model-driven, with language-specific variations -- Qwen2.5-7B favors late positions, challenging assumptions of early-token bias; (2) Explicit positional guidance (e.g., correct context is at position X) reduces accuracy across languages, undermining prompt-engineering practices; (3) Aligning context with positional bias increases entropy, yet minimal entropy does not predict accuracy. (4) We further uncover that LLMs differently impose dominant word order in free-word-order languages like Hindi.

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