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AfriSyCo: Measuring Assertive Framing, Verification, and Wording Sensitivity Around African-Language Content

arXiv 2609.17853 ↗First posted 15 Sep 2026cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

AfriSyCo studies answer switching around African-language factual content with two complementary layers: native-language follow-ups and a controlled cross-language factorial whose question, options, and target remain in the African language while the follow-up framing is English. We analyze 1,415 turn-1-correct model-language-item observations derived from 100 source questions across seven open-weight checkpoints and six languages; turn-1-correct denotes observed first-response accuracy, not demonstrated knowledge. Under native prompts, assertive endorsement produces 29.3 percentage points more any-turn false-target selection than mention-plus-verification (M+V), with a 19.0-point immediate T2 contrast. In the precommitted 2 x 2 factorial, averaged over three tested prompt families, assertive framing increases target selection by 30.4 points (95% CI [28.4, 32.3]); verification decreases it by 17.4 points, while the assertive effect rises from 20.5 points without verification to 40.2 with it (interaction +19.7). The effect remains 34.8 points among 611 observations correct after option reordering. Magnitude varies sharply by wording and checkpoint: prompt-family effects span 20.1-42.5 points, a Twi/Qwen3 paraphrase shifts target selection from 70.8% to 4.2%, and checkpoint effects span 9.2-47.0 points. Prompt realization is therefore part of the measurement problem.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 15 Sep 2026.

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References · 14

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  3. 2024Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontiercited by 121
  4. 2025Measuring Sycophancy of Language Models in Multi-turn Dialoguescited by 91
  5. 2024IrokoBench: A New Benchmark for African Languages in the Age of Large Language Modelscited by 43
  6. 2025Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMscited by 23
  7. 2026Sycophancy Hides Linearly in the Attention Headscited by 21
  8. 2025Challenging the Evaluator: LLM Sycophancy Under User Rebuttalcited by 18
  9. 2025Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PDcited by 13
  10. 2026AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languagescited by 12
  11. 2026What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Constructcited by 12
  12. 2026Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancycited by 7
2 more
  1. 2026SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancycited by 6
  2. 2026Why LLMs Give In: Conversational Factors and Reasoning Behind Medical Sycophancycited by 2

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