A Quantitative Approach to Asch Conformity in Frontier Large Language Models

Senthil Rajasekaran; Stylianos Loukas Vasileiou

cs.CLOct 27, 2026

A Quantitative Approach to Asch Conformity in Frontier Large Language Models

Authors: Senthil Rajasekaran, Stylianos Loukas Vasileiou

Organizations: Université libre de Bruxelles · New Mexico State University

Abstract

The Asch conformity experiment has recently gained considerable attention as a way of measuring conformity in large language models (LLMs). While previous studies have focused on demonstrating conformity in LLMs, our work adds a further quantitative layer to this approach. Instead of sampling a frontier LLM on a suite of questions, our approach samples a single question repeatedly, leveraging the probabilistic nature of LLMs to produce a distribution of answers. In doing so, we can compare the distributions of answers before and after the model is presented with the manufactured consensus. Our experiments confirm that frontier LLMs do conform, reinforcing prior literature, and, through our distribution-to-distribution comparisons, provide additional quantitative data on the conditions that lead to conformity and the degree to which it occurs.

Figures & tables

Figure 1: Left: the line-judgment task of Asch (1956) ; Right: its analog for LLMs, shown for one sentence and gpt-6-luna . The preferred-model-words (shown with their counts) are the words the model wrote in at least

\tau=5\%

of the trials. The non-preferred-model-words are shown at their levels of closeness in meaning to

w_{1}

(relentless), together with the two fillers.

Figure 2: Mean conformity shift

\Delta_{n}

by question type and number of other participants

n

, pooled over models and sentences. Bands show 95% cluster-bootstrap intervals.

Figure 3: Mean conformity shift $\Delta_{n}$ (percentage points) per model and question type, for each number of other participants $n$ .

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Sent.

Question

Key

Distractor

\hat{s}_{0}

n=1

n=2

n=5

n=10

\Delta_{n}

JSD

\Delta_{n}

JSD

\Delta_{n}

JSD

\Delta_{n}

JSD

(w_{1},\text{near})

wanton

indiscriminate

.03

+64 ∗

.44 †

+49 ∗

.31 †

+29 ∗

.20 †

(w_{1},\text{med.})

wanton

unrestrained

.03 †

+36 ∗

.23 †

+14 ∗

.13 †

.01

(w_{1},\text{far})

wanton

necessary

.01

.00

.03 †

(\text{near},\text{med.})

indiscriminate

unrestrained

+28 ∗

.07 †

+65 ∗

.34 †

+51 ∗

.24 †

+21 ∗

.16 †

(\text{near},\text{far})

indiscriminate

necessary

.00

.01

.03 †

Appendix

Table 1: Detailed results for gpt-6-sol .

Sent.

Question

Key

Distractor

\hat{s}_{0}

n=1

n=2

n=5

n=10

\Delta_{n}

JSD

\Delta_{n}

JSD

\Delta_{n}

JSD

\Delta_{n}

JSD

(w_{1},w_{2})

wholesale

relentless

.00

.01

+25 ∗

.14 †

+16 ∗

.09 †

(w_{1},w_{3})

wanton

relentless

.00

+17 ∗

.06 †

+23 ∗

.09 †

(w_{1},w_{4})

inexorable

relentless

.00

.01

+29 ∗

.06 †

+16

.02 †

(w_{1},\text{near})

relentless

unceasing

.01

.04 †

+34 ∗

.20 †

+19 ∗

.10 †

(w_{1},\text{med.})

relentless

ongoing

.00

.04 †

+37 ∗

.22 †

Appendix

Table 2: Detailed results for gpt-6-luna .

Sent.	Question	Key	Distractor	$\hat{s}_{0}$	$n=1$		$n=2$		$n=5$		$n=10$
					$\Delta_{n}$	JSD	$\Delta_{n}$	JSD	$\Delta_{n}$	JSD	$\Delta_{n}$	JSD
1	$(w_{1},w_{2})$	relentless	systematic	26	+36 ∗	.10 †	+71 ∗	.45 †	+74 ∗	.54 †	+74 ∗	.54 †
	$(w_{1},w_{3})$	systematic	wanton	0	0	.00	+19 ∗	.10 †	+1	.01	+1	.01
	$(w_{1},\text{near})$	systematic	methodical	3	+7	.01	+36 ∗	.16 †	+22 ∗	.08 †	+32 ∗	.14 †
	$(w_{1},\text{med.})$	systematic	widespread	0	0	.00	0	.00	0	.00	0	.00
	$(w_{1},\text{far})$	systematic	haphazard	0	0	.00	0	.00	0	.00	0	.00

Appendix

Table 3: Detailed results for claude-sonnet-5-5 .

Sent.

Question

Key

Distractor

\hat{s}_{0}

n=1

n=2

n=5

n=10

\Delta_{n}

JSD

\Delta_{n}

JSD

\Delta_{n}

JSD

\Delta_{n}

JSD

(w_{1},w_{2})

planned

relentless

+11

.01

.00

+11

.01

+13

.01

(w_{1},w_{3})

systematic

planned

+20

.03 †

+47 ∗

.18 †

+44 ∗

.15 †

+40 ∗

.12 †

(w_{1},w_{4})

wholesale

planned

.00

+13

.02 †

+10

.01

+34 ∗

.10 †

(w_{1},\text{near})

planned

programmed

.00

(w_{1},\text{med.})

planned

proposed

-

.00

+21 ∗

.06 †

+10

.02 †

+26 ∗

.08 †

Appendix

Table 4: Detailed results for claude-opus-5-5 .

Sent.

Question

Key

Distractor

\hat{s}_{0}

n=1

n=2

n=5

n=10

\Delta_{n}

JSD

\Delta_{n}

JSD

\Delta_{n}

JSD

\Delta_{n}

JSD

(w_{1},w_{2})

wanton

ruthless

.00

.03

+39 ∗

.23 †

(w_{1},w_{3})

wanton

relentless

.00

.02

+66 ∗

.45 †

+93 ∗

.81 †

(w_{1},\text{near})

wanton

senseless

.02

+37 ∗

.22 †

+84 ∗

.66 †

+97 ∗

.90 †

(w_{1},\text{med.})

wanton

unrestrained

.00

.01

+10 ∗

.05 †

+41 ∗

.24 †

(w_{1},\text{far})

wanton

limited

.00

Appendix

Table 5: Detailed results for gemini-3.8-flash .

Sent.	Question	Key	Distractor	$\hat{s}_{0}$	$n=1$		$n=2$		$n=5$		$n=10$
					$\Delta_{n}$	JSD	$\Delta_{n}$	JSD	$\Delta_{n}$	JSD	$\Delta_{n}$	JSD
1	$(w_{1},w_{2})$	systematic	wanton	0	0	.00	0	.00	+1	.01	0	.00
	$(w_{1},\text{near})$	systematic	coordinated	0	0	.00	0	.00	0	.00	0	.00
	$(w_{1},\text{med.})$	systematic	ongoing	0	0	.00	0	.00	0	.00	0	.00
	$(w_{1},\text{far})$	systematic	random	0	0	.00	0	.00	0	.00	0	.00
	$(\text{near},\text{med.})$	coordinated	ongoing	7	+19 ∗	.05 †	+24 ∗	.07 †	+47 ∗	.21 †	+55 ∗	.27 †

Appendix

Table 6: Detailed results for gemini-4-argon .

Model	Sent.	Question	$n$	$\Delta_{n}$	TVD	JSD
claude-sonnet-5-5	7	$(w_{1},w_{2})$	1	$-$ 20	0.22	.04 †
claude-sonnet-5-5	7	$(w_{1},w_{2})$	2	$-$ 37 ∗	0.39	.16 †
claude-sonnet-5-5	7	$(w_{1},w_{2})$	5	$-$ 41 ∗	0.43	.22 †
claude-sonnet-5-5	7	$(w_{1},w_{2})$	10	$-$ 41 ∗	0.43	.22 †
gpt-6-luna	7	$(\text{near},\text{far})$	10	+6	0.08	.04 †
gpt-6-luna	7	$(\text{med.},\text{far})$	2	+7	0.10	.05 †

Appendix

Table 7: The 20 combinations of question and

n

in which the total variation distance differs from

\lvert\Delta_{n}\rvert

by more than 0.02.

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Abstract

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