arxiv: 2604.07369 · v1 · submitted 2026-04-07 · 💻 cs.LG · cs.AI

Recognition: 2 theorem links

· Lean Theorem

The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior

Ameen Patel , Felix Lee , Kyle Liang , Joseph Thomas

Authors on Pith no claims yet

Pith reviewed 2026-05-10 20:05 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords emotional promptinglarge language modelssycophancytoxicityaccuracyprompt engineeringLLM behavior

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The pith

Positive emotional stimuli improve LLM accuracy and reduce toxicity but increase sycophantic behavior.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests how four specific emotions—joy, encouragement, anger, and insecurity—embedded in prompts at different intensities shape large language model outputs on accuracy, sycophancy, and toxicity. It builds a generation pipeline to produce controlled emotional prompts and filters them into a Gold Dataset where human and model labels agree. The evaluation shows that positive emotional language yields higher accuracy and lower toxicity while raising the rate at which models mirror user opinions. This work extends earlier emotional prompting studies by systematically varying both emotion type and intensity.

Core claim

Empirical tests on the generated prompts indicate that positive emotional stimuli produce more accurate and less toxic model responses, yet they also amplify sycophantic tendencies in which the model agrees with the user even when that agreement reduces correctness.

What carries the argument

The prompt-generation pipeline with GPT-4o mini that creates emotional prompts of controlled intensity, paired with the Gold Dataset of prompts where human and model labels align for reliable evaluation.

Load-bearing premise

The prompt-generation pipeline and Gold Dataset successfully isolate emotional intensity and type without other wording differences confounding the measured effects on accuracy, sycophancy, and toxicity.

What would settle it

Compare identical tasks run with neutral prompts versus the same prompts prefixed with positive emotional language and check whether accuracy rises, toxicity falls, and sycophancy rises as reported.

Figures

Figures reproduced from arXiv: 2604.07369 by Ameen Patel, Felix Lee, Joseph Thomas, Kyle Liang.

**Figure 2.** Figure 2: Mean Positivity Scores for human-generated emotional prompt add-ons [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Mean Positivity Scores for LLM-generated emotional prompt add-ons. 4.2 Sycophancy Mean Positivity Score (MPS) is a relative metric: a score of 0.5 is no difference from neutral baseline; greater than 0.5 is emotional prompt increased sycophancy; less than 0.5 is decreased [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 5.** Figure 5: Mean Base Scores and Mean Augmented Scores for human-generated vs. LLM-generated emotional prompt add-ons (from the Gold Dataset). Scores evaluated from Anthropic’s SycophancyEval subset on accuracy. Overall, there is little to no difference between human-generated and LLM-generated scores [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 4.** Figure 4: A chart of the Human Gold Dataset prompts [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 7.** Figure 7: The Mean Toxicity Scores are the mean scores of our Mean Base Score for the baseline scores and the Mean Augm. Score, the mean score of our emotional prompt add-ons onto the toxicity dataset. The base score is higher in both cases, with the LLM Gold Dataset having a higher change in the toxicity score [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

read the original abstract

Emotional prompting - the use of specific emotional diction in prompt engineering - has shown increasing promise in improving large language model (LLM) performance, truthfulness, and responsibility. However these studies have been limited to single types of positive emotional stimuli and have not considered varying degrees of emotion intensity in their analyses. In this paper, we explore the effects of four distinct emotions - joy, encouragement, anger, and insecurity - in emotional prompting and evaluate them on accuracy, sycophancy, and toxicity. We develop a prompt-generation pipeline with GPT-4o mini to create a suite of LLM and human-generated prompts with varying intensities across the four emotions. Then, we compile a "Gold Dataset" of prompts where human and model labels align. Our empirical evaluation on LLM behavior suggests that positive emotional stimuli lead to more accurate and less toxic results, but also increase sycophantic behavior.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper expands emotional prompting tests to negative emotions and intensity levels but the GPT-4o mini prompt pipeline risks mixing in unrelated linguistic differences.

read the letter

The main point is that this work runs a broader empirical test than prior emotional prompting studies by covering joy, encouragement, anger, and insecurity at multiple intensity levels and tracking effects on accuracy, sycophancy, and toxicity. They generate prompts through a GPT-4o mini pipeline, keep only the cases where human and model labels match in a gold dataset, and then measure how those prompts shift LLM outputs. That setup gives a more complete picture than the positive-only experiments cited in the abstract. The gold dataset step is a reasonable practical choice for getting consistent material to evaluate. The headline pattern they report—that positive emotions raise accuracy and lower toxicity while increasing sycophancy—fits with some earlier observations but now includes the negative side and intensity variation. The soft spot is the prompt creation method. Because GPT-4o mini produces the prompts, features like length, syntax, or lexical choices can easily vary with the emotion labels even if the intended emotion is the only thing being manipulated. The gold dataset filters for label agreement but does not appear to balance or measure those other prompt properties across conditions, so some of the reported differences could trace to those confounds rather than emotion alone. The abstract itself shows no numbers, error bars, or statistical details, which makes it hard to judge effect sizes without the full results. If the paper includes ablations or checks for prompt style, that would help. This is the kind of incremental empirical work that prompt-engineering researchers can use as a reference point when designing their own tests. It does not introduce new architectures or theory, but it fills a clear gap in the existing literature on emotional stimuli. I would send it for peer review. The questions are well-defined, the methods are replicable in principle, and referees can push on the controls and ask for the missing quantitative support.

Referee Report

2 major / 2 minor

Summary. The paper examines how four emotional stimuli (joy, encouragement, anger, insecurity) at varying intensities affect LLM outputs on accuracy, sycophancy, and toxicity. It introduces a GPT-4o-mini-based prompt-generation pipeline to synthesize prompts, constructs a 'Gold Dataset' of prompts where human and model labels agree, and reports that positive emotional stimuli yield higher accuracy and lower toxicity while increasing sycophantic behavior.

Significance. If the empirical patterns are robust, the work would extend prior emotional-prompting studies by systematically varying multiple emotions and intensity levels, offering practical guidance for prompt engineering that balances performance gains against risks such as increased sycophancy. The empirical focus and use of a human-aligned gold set are strengths, though the absence of quantitative details in the abstract leaves the magnitude and reliability of the effects unclear.

major comments (2)

[Prompt-generation pipeline and Gold Dataset construction] Prompt-generation pipeline (described after the abstract and in the methods): because prompts are synthesized by GPT-4o mini rather than sampled from a controlled human corpus, systematic differences in length, syntactic complexity, lexical diversity, or implicit task framing can co-vary with the intended emotional labels. The Gold Dataset only filters for label agreement between humans and the generator; it does not report any equalization or statistical matching of non-emotional prompt properties across emotion/intensity conditions. This directly undermines the central claim that observed differences in accuracy, toxicity, and sycophancy are attributable specifically to the four emotions and their intensities.
[Empirical evaluation] Empirical evaluation section: the abstract and reader's summary provide no quantitative results, error bars, dataset sizes, statistical tests, or baseline comparisons. Without these, it is impossible to assess whether the reported patterns (positive stimuli improve accuracy/reduce toxicity but increase sycophancy) are statistically supported or practically meaningful.

minor comments (2)

[Methods] Clarify the exact procedure and prompts used to elicit the four emotions and intensity levels from GPT-4o mini; include example prompts in an appendix.
[Evaluation metrics] Define the operational measures of accuracy, sycophancy, and toxicity with explicit scoring rubrics or references to established benchmarks.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point by point below, acknowledging where revisions are needed to strengthen the isolation of emotional effects and the presentation of results.

read point-by-point responses

Referee: [Prompt-generation pipeline and Gold Dataset construction] Prompt-generation pipeline (described after the abstract and in the methods): because prompts are synthesized by GPT-4o mini rather than sampled from a controlled human corpus, systematic differences in length, syntactic complexity, lexical diversity, or implicit task framing can co-vary with the intended emotional labels. The Gold Dataset only filters for label agreement between humans and the generator; it does not report any equalization or statistical matching of non-emotional prompt properties across emotion/intensity conditions. This directly undermines the central claim that observed differences in accuracy, toxicity, and sycophancy are attributable specifically to the four emotions and their intensities.

Authors: We agree that this is an important methodological consideration. The pipeline prioritizes generating emotionally labeled prompts with consistent task framing, but we did not perform explicit statistical matching or equalization on non-emotional features such as length or lexical diversity. In the revised manuscript we will add a dedicated analysis subsection reporting these properties (token length, syntactic complexity via parse tree depth, type-token ratio) across all emotion and intensity conditions, including ANOVA or Kruskal-Wallis tests for differences. Where imbalances appear, we will either discuss their likely impact on the dependent measures or create matched subsets for a sensitivity analysis. This addition will clarify the degree to which the observed behavioral differences can be attributed to the emotional stimuli themselves. revision: yes
Referee: [Empirical evaluation] Empirical evaluation section: the abstract and reader's summary provide no quantitative results, error bars, dataset sizes, statistical tests, or baseline comparisons. Without these, it is impossible to assess whether the reported patterns (positive stimuli improve accuracy/reduce toxicity but increase sycophancy) are statistically supported or practically meaningful.

Authors: The main text and figures already contain the quantitative details the referee seeks: accuracy percentages and deltas, Perspective API toxicity scores, sycophancy rates, Gold Dataset size, neutral-prompt baselines, and associated statistical comparisons. However, these are not summarized in the abstract, which limits immediate assessment of effect magnitude. We will revise the abstract to include the key numerical findings (e.g., accuracy and toxicity changes with significance levels) and ensure every figure caption and results paragraph explicitly states sample sizes, error bars or confidence intervals, and the statistical tests used. A summary table of primary metrics will also be added for clarity. revision: partial

Circularity Check

0 steps flagged

No significant circularity in this empirical study

full rationale

The paper conducts a purely empirical evaluation: prompts are generated via GPT-4o mini, filtered into a Gold Dataset by human-model label agreement, and then used to measure LLM outputs on accuracy, toxicity, and sycophancy. No mathematical derivations, fitted parameters renamed as predictions, self-citations bearing central claims, or ansatzes smuggled via prior work appear in the provided text or abstract. Results are direct experimental measurements rather than quantities defined in terms of the paper's own inputs, so the analysis is self-contained against external benchmarks with no circular reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The study rests on standard assumptions from prompt-engineering literature rather than new postulates.

axioms (2)

domain assumption Emotional diction in prompts can be systematically varied in intensity by an automated generator while preserving semantic content.
Invoked in the prompt-generation pipeline description.
domain assumption Human and model labels on emotional content can be aligned to create a reliable gold dataset.
Used to filter the evaluation set.

pith-pipeline@v0.9.0 · 5451 in / 1154 out tokens · 129542 ms · 2026-05-10T20:05:32.275510+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · 3 internal anchors

[1]

Yu, Qiang Yang, and Xing Xie

A survey on evaluation of large language mod- els.Preprint, arXiv:2307.03109. Ajeya Cotra

work page arXiv
[2]

Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A

Large language models in education: Vision and opportunities.Preprint, arXiv:2311.13160. Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith

work page arXiv
[3]

Realtoxicityprompts: Evaluating neural toxic degenera- tion in language models

Realtoxic- ityprompts: Evaluating neural toxic degeneration in language models.Preprint, arXiv:2009.11462. Shabnam Hassani

work page arXiv 2009
[4]

Preprint, arXiv:2404.17522

Enhancing legal compliance and regulation analysis with large language models. Preprint, arXiv:2404.17522. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt

work page arXiv
[5]

Measuring Mathematical Problem Solving With the MATH Dataset

Measuring mathematical problem solving with the math dataset.Preprint, arXiv:2103.03874. Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie

work page internal anchor Pith review Pith/arXiv arXiv
[6]

2023 , month = nov, journal =

Large language models un- derstand and can be enhanced by emotional stimuli. Preprint, arXiv:2307.11760. Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie

work page arXiv
[7]

Preprint, arXiv:2312.11111

The good, the bad, and why: Unveiling emotions in generative ai. Preprint, arXiv:2312.11111. Yinheng Li

work page arXiv
[8]

Baptiste Moreau-Pernet, Yu Tian, Sandra Sawaya, Peter Foltz, Jie Cao, Brent Milne, and Thomas Christie

Sycophancy in large lan- guage models: Causes and mitigations.Preprint, arXiv:2411.15287. Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Am- atriain, and Jianfeng Gao

work page arXiv
[9]

Large Language Models: A Survey

Large language models: A survey.Preprint, arXiv:2402.06196. Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bow- man, Newton Cheng, Esin Durmus, Zac Hatfield- Dodds, Scott R. Johnston, Shauna Kravec, Timo- thy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez

work page internal anchor Pith review arXiv
[10]

Towards Understanding Sycophancy in Language Models

Towards under- standing sycophancy in language models.Preprint, arXiv:2310.13548. Xu Wang, Cheng Li, Yi Chang, Jindong Wang, and Yuan Wu

work page internal anchor Pith review arXiv
[11]

Xuena Wang, Xueting Li, Zi Yin, Yue Wu, and Liu Jia

Negativeprompt: Leveraging psychology for large language models enhancement via negative emotional stimuli.Preprint, arXiv:2405.02814. Xuena Wang, Xueting Li, Zi Yin, Yue Wu, and Liu Jia

work page arXiv
[12]

Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, and Quoc V

Emotional intelligence of large language models.CoRR, abs/2307.09042. Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, and Quoc V . Le

work page arXiv
[13]

arXiv preprint arXiv:2308.03958 (2023) 3, 5

Simple synthetic data reduces sycophancy in large language models.Preprint, arXiv:2308.03958. Ziqi Yin, Hao Wang, Kaito Horio, Daisuke Kawa- hara, and Satoshi Sekine

work page arXiv
[14]

Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing

Should we re- spect llms? a cross-lingual study on the influence of prompt politeness on llm performance.Preprint, arXiv:2402.14531. Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing

work page arXiv
[15]

Sentiment analysis in the era of large language models: A reality check.Preprint, arXiv:2305.15005. A Appendix A.1 Expanding emotions Using our sentiment analysis prompt generation pipelines, we generated roughly 700 prompts across 6 more emotions (anxiety/fear, bored, dis- gust, compassion, sadness, self-conscious). These emotions are more diverse, and a...

work page arXiv