title: Position of Uncertainty: A Cross-Linguistic Study of Positional Bias in
    Large Language Models
summary: 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.
paper_authors._id: 6834833c672ae46abcdc244d
paper_authors.name: Menschikov Mikhail
paper_authors._id: 6834833c672ae46abcdc244e
paper_authors.name: Alexander Kharitonov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:53:48.385Z
paper_authors._id: 65265ffb193ab5e06b5c3bc1
paper_authors.avatarUrl: /avatars/d02fd8bd4ea27be2c4f7699697d922bd.svg
paper_authors.fullname: Alexander Kharitonov
paper_authors.type: user
paper_authors.user: mathamateur
paper_authors._id: 6834833c672ae46abcdc244f
paper_authors.name: Maiia Kotyga
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:53:50.495Z
paper_authors._id: 634eb2f279980a15ac66646c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634eb2f279980a15ac66646c/hGOrxySznCWkZCEAof41n.jpeg
paper_authors.fullname: Maiia Kotyga
paper_authors.type: user
paper_authors.user: kotmayyaka
paper_authors._id: 6834833c672ae46abcdc2450
paper_authors.name: Vadim Porvatov
paper_authors._id: 6834833c672ae46abcdc2451
paper_authors.name: Anna Zhukovskaya
paper_authors._id: 6834833c672ae46abcdc2452
paper_authors.name: David Kagramanyan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:53:45.724Z
paper_authors._id: 6359b0b6548e5c7e6e69398c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1668424684378-6359b0b6548e5c7e6e69398c.jpeg
paper_authors.fullname: David Kagramanyan
paper_authors.type: user
paper_authors.user: dkagramanyan
paper_authors._id: 6834833c672ae46abcdc2453
paper_authors.name: Egor Shvetsov
paper_authors._id: 6834833c672ae46abcdc2454
paper_authors.name: Evgeny Burnaev
paper_title: Position of Uncertainty: A Cross-Linguistic Study of Positional Bias in
    Large Language Models
paper_summary: 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.
paper_ai_summary: LLMs display positional bias across different languages, which affects model uncertainty, syntax, and prompting, revealing that explicit positional guidance can reduce accuracy.
paper_ai_keywords: positional bias model uncertainty syntax prompting positional guidance entropy dominant word order free-word-order languages