AfriSyCo: Measuring Assertive Framing, Verification, and Wording Sensitivity Around African-Language Content
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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.
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References · 14
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2023Towards Understanding Sycophancy in Language Modelscited by 988
- 2024Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontiercited by 121
- 2025Measuring Sycophancy of Language Models in Multi-turn Dialoguescited by 91
- 2024IrokoBench: A New Benchmark for African Languages in the Age of Large Language Modelscited by 43
- 2025Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMscited by 23
- 2026Sycophancy Hides Linearly in the Attention Headscited by 21
- 2025Challenging the Evaluator: LLM Sycophancy Under User Rebuttalcited by 18
- 2025Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PDcited by 13
- 2026AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languagescited by 12
- 2026What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Constructcited by 12
- 2026Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancycited by 7
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