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

David Ababio Awuni, Rose-Mary Owusuaa Mensah Gyening, Elvis Gyasi Owusu

arXiv:2609.17853v1cs.CLcs.AIcs.LG

TL;DR

A single answer-switching prompt can bundle target mention, asserted correctness, and a request to verify, making it difficult to identify which cue matters. AfriSyCo combines native-language follow-ups with a controlled factorial around African-language content; assertive framing increases target selection, but the size of the effect varies across tested wordings and checkpoints.

  • Problem

    A follow-up that names a target, asserts it is correct, and asks for verification bundles cues, so its switch rate cannot show which cue moved the model.

    Source: §1 · p. 1

  • Method

    AfriSyCo pairs native-language follow-ups with a controlled factorial that holds target mention fixed and varies assertive framing and verification around African-language item content.

    Source: §4.2 · p. 4, §7 · p. 7

  • Results

    30.4 pp: averaged over three tested prompt families, assertive framing increases target selection; prompt-family effects range from 20.1 to 42.5 pp and checkpoint-specific effects from 9.2 to 47.0 pp.

    Source: §7 · p. 8

  • Takeaways & Limitations

    Prompt realization is part of the measurement problem, so claims should be tied to the evaluated wordings, checkpoints, languages, and experimental layer.

    Source: §7 · p. 8

    The +30.4 pp assertive effect averages three tested English follow-up families and is not a universal coefficient or a fully native-language effect.

    Source: §6.1 · p. 6

Abstract

from arXiv · show

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.

1 Introduction

AfriSyCo asks which cues in a follow-up prompt move a model away from a correct factual answer, focusing on African-language content. Its native-language and controlled cross-language experiments find large assertive-framing effects, while showing that results depend on prompt wording and checkpoint.

1 Introduction

  • A single endorsed-and-verified prompt bundles target mention, an assertion of correctness, and a verification request, so its switch rate cannot isolate which cue moved the model.The paper treats prompt realization as part of the measurement instrument because wording can alter measured behavior.Source: §1 · p. 1
  • The study pairs native-language follow-ups with a controlled factorial that holds target mention fixed and crosses assertive framing with verification.In the factorial, the question, choices, and target remain African-language text, but the follow-up framing is English, not a fully native-language pragmatic-equivalence test.Source: §1 · pp. 1–2
  • 30.4 pp: averaged over three tested prompt families, assertive framing increases target selection in the controlled factorial.The effect is 34.8 pp among 611 observations that remain correct after deterministic option reordering.Source: §1 · p. 2
  • The factorial finds that verification lowers target selection overall, but the assertive effect rises from 20.5 pp without verification to 40.2 pp with it.This makes verification an active factor rather than a neutral control.Source: §1 · p. 2
  • Prompt-family effects range from 20.1 to 42.5 pp, while checkpoint-specific assertive effects range from 9.2 to 47.0 pp.A Twi/Qwen3 paraphrase stress test shifts target selection from 70.8% to 4.2%.Source: §1 · p. 2

2 Related Work

Related work treats sycophancy as a heterogeneous set of behaviors and prompt wording as an experimental factor. AfriSyCo narrows the question to the contributions of assertive correctness framing and independent verification when the same target is named.

Sycophancy and conversational deference

  • Prior work spans static agreement, rebuttal, persuasion, multi-turn interaction, authority, and multilingual settings; a taxonomy likewise argues that sycophancy is not a single behavior.Studies also examine reasoning, internal representations, and mitigation.Source: §2 · p. 2

Prompt properties as experimental factors

  • Prompt-sensitivity research finds that phrasing can change model behavior and evaluation outcomes, while crossed conversational designs can reveal interactions between factors.AfriSyCo asks what assertive correctness framing and an independent-verification request each contribute when the same target is named.Source: §2 · p. 2

Multilingual and African-language evaluation

  • IrokoBench documents gaps between African and high-resource languages, and prior multilingual sycophancy studies report variation across languages and settings.AfriSyCo treats its six languages and seven checkpoints as fixed evaluation conditions, not random samples from a universal population.Source: §2 · pp. 2–3

3 Data and Evaluation Setup

The evaluation uses four-option AfriMMLU questions from 100 shared source items, with eligible observations restricted to parseable, correct first responses. It evaluates seven open-weight checkpoints, uses deterministic response parsing, and documents native-language template review and its limits.

Questions and eligible cases

  • The analysis includes 1,415 model–language–item observations from 100 shared source questions across Hausa, Igbo, Kinyarwanda, Swahili, Twi, and Yoruba.Eligibility requires a parseable turn-1 response matching the gold option; turn-1-correct describes observed first-response accuracy, not stable or latent knowledge.Source: §3 · p. 3

Models

  • Seven open-weight checkpoints are evaluated with greedy shortform generation; comparisons across checkpoints are descriptive because interfaces and training histories differ.The evaluated checkpoints include base and instruct versions of Llama-3.1-8B.Source: §3 · p. 3

Scoring

  • A deterministic parser scores whether the model selects the named target, retaining empty attempted generations as unparseable rather than silently dropping them.Available probability outputs are used as diagnostics, not calibrated beliefs.Source: §3 · p. 3

Human review

  • Two native-speaker review streams per language independently checked 36 native M+V and disavowed-mention templates across fidelity, naturalness, condition alignment, endorsement, certainty, target preservation, and gold leakage.The streams agreed on 29/36 accept/revise decisions (80.6%; pooled Cohen’s κ = 0.59), and produced identical corrected wording for 23/36 templates.Source: §3 · p. 3
  • Review and adjudication document template checks, not equal pragmatic force across languages; Yoruba’s 2/6 matching decisions and κ = 0 are high-variance process diagnostics.The manuscript reports that an institutional ethics determination was pending and makes no claim of approval or exemption.Source: §3 · p. 3

4 Experimental Design and Identification

The design combines native-language follow-ups with a controlled factorial and narrower robustness analyses. It keeps immediate and any-turn outcomes distinct and treats inference as conditional on evaluated items, prompt families, languages, and models.

4 Experimental Design and Identification

  • Table 1 presents the study as an evidence ladder, with immediate and any-turn outcomes kept distinct.“Frozen” means fixed before that stage’s own generation, not preregistration of the original project.Source: §4 · p. 3, Table 1 · p. 4

4.1 Native-language decomposition

  • The native-language decomposition compares neutral re-asks, disavowed mention, M+V, and assertive endorsement across three follow-up turns, using paired any-turn target selection as its primary estimand.Native M+V is composite because it attributes the target to a reported previous answer; E–M+V is not a pure assertion coefficient.Source: §4.1 · p. 4

4.2 Precommitted 2 × 2 factorial

  • The 2 × 2 factorial holds the false target mention fixed and crosses assertive framing with independent verification across label, sentence, and consider prompt families.Question, options, and target remain in the African language while follow-up framing is English, so the design is not a native-language pragmatic-equivalence experiment.Source: §4.2 · p. 4

4.3 Robustness experiments and existing-data reconciliation

  • Robustness analyses test persistence, matched truth, all targets, standalone native-wording replication, and capability after deterministic option rotation; the later reconciliation analyses are explicitly post-hoc.The option-rotation check retains 611 observations that remain parseable and correct.Source: §4.3 · p. 4

4.4 Inference, weighting, and multiplicity

  • Primary confidence intervals bootstrap the 100 shared source questions, moving linked model–language observations together; model, language, and family intervals are descriptive.The three-family sensitivity interval is not population-level inference over unseen prompts, and the appendix includes missingness and leave-one-out analyses.Source: §4.4 · p. 5

5 Results

In the evaluated factorial, assertive framing increases target selection, while verification lowers it overall but amplifies the assertive contrast. The size of these effects varies across tested wording and checkpoints, and supporting analyses preserve the main pattern.

5.1 Finding 1: Assertive framing has a large effect in the evaluated factorial

  • +34.8 pp is the factorial assertive effect among 611 observations correct after option reordering; native endorsement exceeds M+V by +29.3 pp over T2–T4.The native contrast is not a pure assertion coefficient because the native conditions differ in more than assertion alone.Source: §5.1 · p. 5
  • +30.4 pp is the primary factorial assertive effect averaged over three prompt families (95% CI [28.4, 32.3]).Figure 1 reports the controlled 2 × 2 design; the estimate covers 1,415 model–language–item observations from 100 source questions.Source: Figure 1 · p. 5, §5.1 · p. 5

5.2 Finding 2: Verification is an active factor and interacts with assertion

  • Verification lowers target selection by 17.4 pp, but the assertive effect rises from 20.5 pp without verification to 40.2 pp with it, an interaction of +19.7 pp.The interaction is behavioral; the experiments do not identify an internal mechanism such as confidence revision or authority tracking.Source: §5.2 · p. 5
  • Native M+V and the closest factorial non-assertive-plus-verification cell differ by 2.4 pp on the same cases, but this is not an equivalence test.The native experiment demonstrates switching under native prompts; the factorial estimates assertion and verification under a controlled English follow-up manipulation.Source: §5.2 · p. 6

5.3 Finding 3: Effect magnitude is highly wording- and model-sensitive

  • The assertive effect is positive across all three tested families but ranges from 20.1 pp for consider to 42.5 pp for label.The primary 30.4 pp estimate is conditional on the three tested realizations, not a population-level estimate over unseen prompts.Source: §5.3 · p. 6
  • For Qwen3-8B, Twi target selection shifts from 70.8% to 4.2% across paraphrase rows; checkpoint assertive effects range from 9.2 pp to 47.0 pp.The study does not identify model properties as the cause of checkpoint differences or treat the ranking as stable beyond this design.Source: §5.3 · p. 6

5.4 Supporting analyses preserve the three main findings

  • Supporting analyses yield +29.1 pp with equal weighting of 42 model–language cells and +32.5 pp when all 12 factorial responses must be parseable.The matched-truth difference-in-differences is −1.1 pp ([−3.5, 1.4]), providing little evidence in this design that the endorsed increment is larger for false than true targets, but not establishing equivalence.Source: §5.4 · p. 6

6 Discussion

The discussion treats the assertive effect as conditional on tested wording and observed first-response correctness. It also emphasizes verification’s interaction with assertion, prompt and checkpoint sensitivity, and the distinction between native-language findings and the controlled English-framed factorial.

6.1 The headline effect is conditional, not universal

  • 30.4 pp is averaged over three tested English follow-up families; it is neither a universal assertiveness coefficient nor a fully native-language effect.The native-language layer demonstrates switching, while the factorial identifies assertion and verification effects under a related cross-language manipulation.Source: §6.1 · p. 6
  • Turn-1-correct observations measure observed first-response accuracy, not latent knowledge, even when responses remain correct after option reordering.The analysis conditions on observed turn-1 correctness; the 611-observation recheck still does not establish a model’s latent knowledge state.Source: §6.1 · pp. 6–7

6.2 Verification changes the treatment, not just the instruction

  • Verification lowers target selection overall but raises the assertive contrast from 20.5 pp without verification to 40.2 pp with it.A verification-bearing control is therefore not automatically neutral; the factors should be separated where possible.Source: §6.2 · p. 7

6.3 Prompt realization is part of the measurement problem

  • Assertive effects range from 20.1 to 42.5 pp across prompt families, while the Twi/Qwen3 paraphrase shifts target selection from 70.8% to 4.2%.Checkpoint effects range from 9.2 to 47.0 pp, and the design does not identify why checkpoints differ; concrete wording can vary in force, naturalness, or pragmatic implication.Source: §6.3 · p. 7

6.4 What the African-language setting establishes

  • Native prompts demonstrate switching, whereas the factorial separates assertion and verification using English framing around African-language content.Review disagreements involved naturalness, grammar, lexical choices, certainty, semantic alignment, and condition preservation; six templates per language leave language-specific κ values too unstable for broad claims about translation reliability or language quality.Source: §6.4 · p. 7

7 Conclusion

AfriSyCo combines native-language answer-switching experiments with a controlled factorial around African-language item content. The factorial finds a substantial assertive effect, but the effect varies with wording and checkpoint, making prompt realization part of the measurement problem.

7 Conclusion

  • In tested prompt families, assertive framing raises named-target selection by 30.4 pp; the effect remains substantial among 611 observations that stay correct after option reordering.Verification lowers target selection overall but amplifies the assertive contrast, from 20.5 pp without verification to 40.2 pp with it.Source: §7 · p. 8
  • Prompt-family assertive effects range from 20.1 to 42.5 pp, the Twi/Qwen3 paraphrase shifts target selection from 70.8% to 4.2%, and checkpoint effects range from 9.2 to 47.0 pp.Claims should remain tied to the evaluated wordings, checkpoints, languages, and experimental layer.Source: §7 · p. 8

Limitations

The limitations constrain the findings to specific prompt families, selected observations, interaction contexts, review procedures, and evaluated tasks, models, and languages. The native-language and factorial layers are related but not equivalent.

Limitations

  • The factorial keeps African-language questions, options, and targets but uses English follow-up framing, so it is not a fully native-language pragmatic-equivalence experiment.The native experiment demonstrates switching under native prompts; the factorial separately identifies assertion and verification effects.Source: Limitations · p. 8
  • Prompt-family generalization covers three evaluated English families, not all possible wordings; the study does not claim prompt invariance.The Twi/Qwen3 paraphrase collapse shows that wording sensitivity can be extreme.Source: Limitations · p. 8
  • Eligibility selects parseable, gold-matching first responses, and even the 611-case option-reordering recheck does not establish latent knowledge.The main population is selected by one correct turn-1 response, which can occur by chance.Source: Limitations · p. 8
  • Visible washout turns measure within-context carry-over, not persistence after a context reset or across sessions.Source: Limitations · p. 8
  • Six templates per language make language-specific κ values unstable process diagnostics, and review does not establish equal pragmatic force across languages.Source: Limitations · p. 8
  • Four-option MCQs, short anchors, deterministic parsing, seven fixed checkpoints, and six fixed languages limit generalization; a confidence interval spanning zero does not establish equivalence.Source-item bootstrap intervals do not make models, languages, or prompt families random samples from broader populations.Source: Limitations · p. 8

Ethical Considerations

The paper describes its benchmark, human review, and release plans, while noting that an institutional ethics determination was still pending when the manuscript was prepared.

Ethical Considerations

  • The benchmark uses public factual educational items and contains no private user data.Source: Ethical Considerations · p. 8
  • Adult lifelong speakers reviewed the added interaction language and rendered examples, consented to de-identified use of judgments, and were not financially compensated.An institutional ethics determination had been requested but had not yet been received; the paper makes no claim of approval or exemption.Source: Ethical Considerations · pp. 8–9
  • Because false-suggestion prompts are dual-use, the authors plan to release them with controls, frozen plans, parser rules, and explicit limitations rather than as an attack recipe.Source: Ethical Considerations · p. 9

Reproducibility Statement

The reproducibility archive preserves prompts, review artifacts, scoring outputs, and analysis provenance. The factorial prompt families were precommitted, and the study makes turn-1 accuracy visible by model–language cell.

Reproducibility Statement

  • The release archive includes exact prompts, review artifacts, row-level scores, bootstrap outputs, frozen plans, and raw responses where retained.All expected events were completed for the persistence/truth/all-target, capability/native-wording, and factorial stages; final reviewer resolution used existing data only.Source: Reproducibility Statement · p. 9

A Factorial Prompt Families and Full Estimates

  • The factorial prompt families were precommitted; every cell names the target, and A = 0 means non-assertive candidate framing, not no suggestion.The control wording is English while the target text remains in the item's language.Source: A Factorial Prompt Families and Full… · p. 10

C Baseline Capability by Model and Language

  • Each model–language cell uses the same 100 source questions, and Table 7 reports turn-1 gold-answer accuracy with parseability separately.The main analysis conditions on responses that are both parseable and correct at turn 1.Source: C Baseline Capability by Model and… · p. 10

D Native Decomposition, Metric Reconciliation, and Missingness

Native endorsement produces more any-turn target selection than mention-plus-verification, while immediate-response and any-turn comparisons remain distinct. Replication, review, paraphrase, and chronology analyses qualify how these estimates should be read.

D Native Decomposition, Metric Reconciliation, and Missingness

  • 29.26 pp is the native any-turn E–M+V estimate; the immediate T2 contrast is +19.01 pp, and the factorial is not pragmatically equivalent to native prompts.Any-turn selection was 67.8% under endorsed wording and 38.6% under M+V; native endorsed T2 remained below both assertive factorial cells.Source: D Native Decomposition, Metric… · p. 10

E Native Wording Replication and Capability Recheck

  • The wording replication estimates endorsed minus M+V at +22.54 pp averaged across three wordings; the option-reordering subset retains a +30.55 pp average.The standalone replication comprised 8,490 branch events.Source: E Native Wording Replication and… · pp. 10–11

F Native-Review Agreement and Adjudication

  • The finalized artifact contains two completed native-speaker streams for each language; with only six templates per language, per-language κ values are high-variance diagnostics, not standalone conclusions.Cohen’s κ is undefined for Swahili because its marginal decision distribution is constant.Source: F Native-Review Agreement and… · p. 11

G Twi Paraphrase Boundary

  • For Qwen3-8B, Twi paraphrasing shifts target selection from 70.8% to 4.2%; this boundary test does not establish native prompt-family invariance.Other checkpoints have too few jointly parseable rows for a stable comparison.Source: G Twi Paraphrase Boundary · pp. 11–12

H Follow-up Estimates and Chronology

  • The final reviewer-resolution stage was analysis-only, and “frozen” means fixed before that stage’s own generation, not original-project preregistration.It reconstructed existing native observations, factorial events, and finalized templates without generating model outputs or collecting new native judgments.Source: H Follow-up Estimates and Chronology · p. 12, Table 9 · p. 13
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