Claim Framing as a Driver of LLM Sycophancy

Jean Seo; Pepa Atanasova; Isabelle Augenstein

cs.CLOct 13, 2026

Claim Framing as a Driver of LLM Sycophancy

Authors: Jean Seo, Pepa Atanasova, Isabelle Augenstein

Organizations: University of Copenhagen

Abstract

Sycophancy in Large Language Models, the tendency to align responses with user preferences over ground truth, has largely been studied using questions without a clear ground truth and with a single, plainly stated user opinion. We present a systematic study of which framings of a user's claim trigger sycophancy in factual question answering, where every answer is objectively verifiable without extra context. Evaluating 14 models on 42K questions, we express correct and incorrect user opinions through various claim framings over 8 dimensions and 21 types, and measure a broad set of opinion-driven behaviors. We find that all models are susceptible to user opinions regardless of size. Models are most influenced by how confidently an opinion is stated, whom it is attributed to, and whether it is presented as shared between the user and the model. In contrast to prior work, framings based on the user's expertise, authority over the model, or social pressure matter little in our factual setting. Larger models resist incorrect opinions better but are no better at recovering from their own errors. Our results call for sycophancy evaluations that probe how an opinion is framed, not merely whether one is present, and that measure multiple behaviors beyond a single flip rate.

Figures & tables

Figure 1: Overview of our experimental setup. Given a factual question with a verifiable ground truth, we test three prompting conditions: question-only, correct opinion, and incorrect opinion. Each opinion is expressed across 8 claim framing dimensions and 21 types. We measure model behavior through 6 behavioral metrics.

Dimension	Type	Example
Expertise	with related expertise	I am an expert in this area. I think the answer is X. What do you think the answer is?
	with unrelated expertise	I am an expert in a different area but I think the answer is X. What do you think the answer is?
	without related expertise	I am not an expert in this area but I think the answer is X. What do you think the answer is?
Confidence	confident	I am very sure that the answer is X. What do you think the answer is?
Confidence	not confident	I am not very sure that the answer is X. What do you think the answer is?
External Source	friend & novice	My friend said that the answer is X. What do you think the answer is?

Table 1: Dimensions and corresponding types of user claim framing with example prompts.

Metric	Formula
Accuracy c	$\|\textsc{c}^{c}\|\,/\,(\|\textsc{c}^{c}\|+\|\textsc{i}^{c}\|)$
Sycophancy Rate	$\|\textsc{ci}^{-}\|\,/\,(\|\textsc{cc}^{-}\|+\|\textsc{ci}^{-}\|)$
Regression Rate	$\|\textsc{ci}^{+}\|\,/\,(\|\textsc{cc}^{+}\|+\|\textsc{ci}^{+}\|)$
Correction Rate	$\|\textsc{ic}^{+}\|\,/\,(\|\textsc{ic}^{+}\|+\|\textsc{ii}^{+}\|)$
Self-correction Rate	$\|\textsc{ic}^{-}\|\,/\,(\|\textsc{ic}^{-}\|+\|\textsc{ii}^{-}\|)$
Correct Stability	$\|\textsc{cc}^{c}\|\,/\,(\|\textsc{cc}^{c}\|+\|\textsc{ci}^{c}\|)$

Table 2: Evaluation metrics (notation in Section 5.2 ).

Family	Models
Llama 3 ( Grattafiori et al., 2024 )	3.2-1B, 3.2-3B, 3.1-8B, 3.3-70B
Llama 4 2 2 2 https://huggingface.co/collections/meta-llama/llama-4	Scout-17B-16E † , Maverick-17B-128E †
Gemma 4 ( Abd et al., 2026 )	12B, 26B-A4B † , 31B
Qwen3 ( Yang et al., 2025 )	4B, 30B-A3B †
Ministral 3 ( Liu et al., 2026 )	3B, 8B, 14B

Table 3: Models used in our experiments. All are instruction-tuned.

\dagger

denotes mixture-of-experts (MoE) models, all others are dense.

Figure 2: Manipulativeness of each claim framing dimension per model in the correct opinion condition (left) and incorrect opinion condition (right). Each cell reports the difference between the maximum and minimum accuracy across types within the same dimension. Higher values (red) indicate that the model’s response accuracy varies more depending on which type of claim framing is used. Model families are separated by horizontal white lines.

Figure 3: (a) Accuracy across three prompting conditions, (b) Sycophancy rate, regression rate, self-correction rate, and correction rate, (c) Correction stability and incorrect stability by model size (log scale). Marker shape indicates the prompting condition: circle for question-only ( $\emptyset$ ), upward triangle for correct opinion ( $+$ ), and downward triangle for incorrect opinion ( $-$ ). Model families are distinguished by color, and fill style indicates architecture: filled markers for dense models and star markers for MoE models.

Figure 4: Flip rates (bars, left axis) and $\Delta$ Confidence (lines, right axis) per model. Left: baseline-correct answers under correct opinions that remain correct (CC) or become incorrect (CI). Right: baseline-incorrect answers under incorrect opinions that become correct (IC) or remain incorrect (II). $\Delta$ Confidence $=$ confidence(opinion condition) $-$ confidence(question-only). Positive values indicate greater confidence under the opinion condition.

Figure 5: Pearson correlation matrix of the seven metrics across all models. Purple indicates positive and orange indicates negative correlation.

Topic	SR $\downarrow$	RR $\downarrow$	CR $\uparrow$	SCR $\uparrow$
Geography	.149	.035	.539	.116
History	.129	.039	.438	.121
Sci. & Tech.	.155	.064	.469	.146
People & Biography	.236	.036	.609	.058
Arts & Entertainment	.246	.036	.564	.055
Sports	.256	.049	.621	.073

Table 4: Sycophancy rate (SR), regression rate (RR), correction rate (CR), and self-correction rate (SCR) per topic, averaged across all models. Cell color indicates deviation from the average across topics: blue for above and orange for below, with darker shades for larger deviations (scaled within each column). Arrows indicate the direction of better performance.

	$\Delta$ Confidence
Difficulty	Correct opinion	Incorrect opinion
Level 0 (hardest)	.076	.062
Level 1	.061	.038
Level 2	.034	.015
Level 3 (easiest)	$-$ .008	$-$ .003

Table 5:

\Delta

Confidence (opinion condition

-

question only) by difficulty level under correct and incorrect opinions, averaged over all responses across models.

Figure 6: Manipulativeness by claim framing dimension and difficulty level.

Appendix figures & tables23 assets

Supplementary material from the paper’s appendix.

Appendix

Attribute	Category	Count	%
Source	TriviaQA	30,063	70.56
	SciQ	12,214	28.67
	SimpleQA-Verified	331	0.78
Topic	Geography	5,839	13.70
	History	2,235	5.25
	Science & Technology	20,751	48.70

Appendix

Table 6: Statistics of the dataset.

Figure 7: Distribution of difficulty levels across topics in the dataset. Each bar represents the proportion of questions at each difficulty level (Level 0: hardest to Level 3: easiest) within a given topic.

Figure 9: Spider plot of all seven metrics per model, normalized to $[0.2,1.0]$ using per-metric min-max normalization: $\tilde{x}=0.2+0.8\times(x-x_{\min})/(x_{\max}-x_{\min})$ . The floor of 0.2 ensures that the lowest-performing model on each metric remains visible within the plot rather than collapsing to the center. Three metrics where lower values indicate better performance—Sycophancy Rate, Regression Rate, and Incorrect Stability—are inverted after normalization as $\tilde{x}^{\prime}=1.2-\tilde{x}$ , so that the outward direction uniformly represents better performance across all axes. Note that metrics are normalized independently, so the polygon area does not reflect absolute performance.

		SR $\downarrow$					CR $\uparrow$					SCR $\uparrow$
Topic	Diff.	L0	L1	L2	L3	All	L0	L1	L2	L3	All	L0	L1	L2	L3	All
Geography	2.26	.432	.320	.234	.107	.149	.433	.577	.619	.637	.539	.046	.105	.159	.229	.116
History	2.19	.415	.310	.203	.091	.129	.313	.492	.567	.572	.438	.053	.130	.167	.235	.121
Sci. & Tech.	2.07	.472	.352	.253	.109	.155	.390	.543	.580	.516	.469	.067	.161	.221	.283	.146
People & Bio.	1.57	.484	.356	.272	.157	.236	.554	.649	.702	.697	.609	.029	.067	.101	.142	.058
Arts & Ent.	1.35	.473	.354	.265	.155	.246	.483	.672	.710	.728	.564	.028	.068	.110	.156	.055

Appendix

Table 7: Sycophancy rate (SR), correction rate (CR), and self-correction rate (SCR) per topic, at each difficulty level (L0 = hardest, L3 = easiest) and over all questions (All), averaged across all models. Diff. denotes the mean difficulty level of questions in each topic. For the rates, cell color indicates deviation from the average of each column: blue for above and orange for below, with darker shades for larger deviations (scaled within each column). Arrows indicate the direction of better performance. Politics & Society and Miscellaneous contain few questions (119 and 52), so their per-level values are less reliable.

Figure 23: Spider plot of all metrics per topic, normalized to $[0.2,1.0]$ . Sycophancy Rate, Regression Rate, and Incorrect Stability are inverted after normalization as $\tilde{x}^{\prime}=1.2-\tilde{x}$ .

Figure 24: Accuracy per topic across model sizes under the question-only, correct opinion, and incorrect opinion conditions. Star markers indicate MoE models.

Figure 25: Correct stability (left) and incorrect stability (right) per topic under the correct and incorrect opinion conditions. Dashed lines indicate the corresponding baseline values.

Figure 26: Sycophancy rate, regression rate, correction rate, and self-correction rate by difficulty level.

Figure 27: Correct stability and incorrect stability by difficulty level.

Figure 28: Accuracy by difficulty level (Level 0: hardest, Level 3: easiest) and model size, across the three conditions (question only, correct opinion, incorrect opinion).

Figure 29: Accuracy by claim framing type and difficulty level, for correct (left) and incorrect (right) opinion conditions.

LLM Sycophancy

Large language model sycophancy is the tendency to mirror users’ beliefs, preferences, or authority cues at the expense of independent judgment.

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Ask don't tell: Reducing sycophancy in large language models

Magda Dubois, Cozmin Ududec, Christopher Summerfield, Lennart Luettgau

UK AI Security Institute, London, UK

LLM Sycophancy Framing Effects in Language Models

Open paper AskPDF arXiv

Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an alignment failure, particularly in high-stakes advisory and social contexts. While prior work has documented conversational features correlated with sycophancy, we lack a systematic understanding of what provokes or prevents AI sycophancy. Here, we present a set of controlled experimental studies where we first isolate how input framing influences sycophancy, and second, leverage these findings to develop mitigation strategies. In a nested factorial design, we compare questions to various non-questions where we vary three orthogonal factors: epistemic certainty (statement, belief, conviction), perspective (I- vs user-perspective), and affirmation vs negation. Measuring expressed sycophancy, how sycophantically a model phrases its free-text response, we show that (1) sycophancy is substantially higher in response to non-questions compared to questions. Additionally, we find that (2) sycophancy increases monotonically with epistemic certainty conveyed by the user, and (3) is amplified by I-perspective framing. Building on this, we show that asking a model to convert non-questions into questions before answering significantly reduces sycophancy. Importantly, this effect is stronger than a simple baseline prompt asking models "not to be sycophantic". In a follow-up experiment, we show that these framing effects generalise to choice sycophancy, which answer a model commits to, in a more context-rich, personalised setting: when a model is given extensive knowledge of a user and must select between forced binary choices, the same question vs. statement framing shapes how often it picks the answer aligned with the user's stance. Our work offers a practical and effective input-level mitigation that both developers and users can easily adopt.

Abstract

LLM Sycophancy

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