arxiv: 2604.07801 · v1 · submitted 2026-04-09 · 💻 cs.CL · cs.AI

Recognition: no theorem link

TEMPER: Testing Emotional Perturbation in Quantitative Reasoning

Atahan Dokme , Benjamin Reichman , Larry Heck

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:04 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords emotional framingquantitative reasoninglarge language modelsrobustness evaluationbenchmark constructionGSM8KMultiArithARC-Challenge

0 comments

The pith

Emotional framing reduces LLM accuracy on quantitative tasks by 2-10 percentage points even when all numbers and logic remain identical.

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

The paper introduces a controlled translation method that converts standard math and science problems into emotionally charged versions such as frustration or enthusiasm without altering any quantities or relationships. Testing across eighteen models and three datasets shows that these emotional versions lower accuracy by 2 to 10 points compared with neutral originals. Neutralizing the emotional language restores most of the lost performance, while ordinary paraphrases without emotion produce no drop. This indicates that stylistic emotional content itself interferes with reasoning rather than any change in factual content. The approach also supplies a reusable procedure for generating verified stylistic variants to measure robustness more broadly.

Core claim

Emotional variants of quantitative reasoning problems cause a consistent drop in model accuracy compared to neutral versions, and neutralizing the emotion restores performance, demonstrating that stylistic emotional content specifically impairs reasoning.

What carries the argument

The emotion translation framework that rewrites neutral problems into emotional variants while preserving all quantities and relationships exactly, supported by semantic verification to create the Temper-5400 benchmark of 5,400 pairs.

If this is right

Emotional framing produces measurable accuracy drops of 2-10 points on GSM8K, MultiArith, and ARC-Challenge across model sizes from 1B to frontier scale.
Neutralizing emotional language at inference time recovers most of the performance lost to emotional framing.
Non-emotional paraphrases cause no accuracy degradation, isolating the effect to emotional content rather than surface changes.
The translation procedure provides a general method for constructing controlled stylistic variants to test model robustness on other attributes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Models could gain robustness by training on datasets that include controlled emotional variants of the same underlying problems.
The same translation technique might reveal similar sensitivities in non-quantitative tasks where emotional language appears in prompts.
Inference pipelines could routinely apply neutralization steps before reasoning tasks to improve reliability on real-world queries.

Load-bearing premise

The emotion translation framework preserves all quantities and relationships exactly, with semantic verification ensuring no content corruption.

What would settle it

Finding no accuracy difference between emotional and neutral versions of the same problems on the evaluated models, or observing that neutralization fails to recover the lost performance.

Figures

Figures reproduced from arXiv: 2604.07801 by Atahan Dokme, Benjamin Reichman, Larry Heck.

**Figure 1.** Figure 1: Overview. (a) The translator rewrites a neutral math problem into an emotional variant, preserving numerical structure (bold). (b) Training combines generation loss (LCE) with auxiliary alignment (Laux). hL denotes the final-layer (layer L=32) hidden state of Llama 3.1-8B (4096-d), mean-pooled and linearly projected to match the teacher’s representation space. EMO100 supervises at the 100-dim latent layer… view at source ↗

**Figure 2.** Figure 2: Representational analysis (900 problems, 5400 translation, four architectures). Cosine distance. For each transformed version of a problem, the cosine distance between its hidden state and the original is computed. Emotional variants shift representations 3–4× further than neutralized text, consistently across all four architectures (Figure 2a). Disgust and fear produce the largest shifts, matching the emo… view at source ↗

**Figure 3.** Figure 3: t-SNE visualization of emotion representations from EMO100-L translations (1,200 samples). The 100-dim bottleneck (left) learns a structured emotion manifold; the 7-dim categorical representation (right) collapses this richness to 1D curves. 60 40 20 0 20 40 60 t-SNE Dimension 1 60 40 20 0 20 40 60 80 t-SNE Dimension 2 Emotion Space: Original vs Emotional vs Neutralized vs Verbose (Emo100-L, 100-dim Bottle… view at source ↗

**Figure 4.** Figure 4: Original vs. emotional vs. neutralized vs. paraphrase (verbose) in the teacher’s 100-dim bottleneck space (EMO100-L, 1,200 samples). Emotional translations (red) form separated clusters by emotion; original (gray), neutralized (blue), and paraphrases (green) overlap in a diffuse central region. B Intensity control example [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

read the original abstract

Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language. However, real-world queries are often wrapped in frustration, urgency or enthusiasm. Does emotional framing alone degrade reasoning when all numerical content is preserved? To investigate this, a controlled emotion translation framework is developed that rewrites problems into emotional variants while preserving all quantities and relationships. Using this framework, Temper-5400 (5,400 semantically verified emotion--neutral pairs) is constructed across GSM8K, MultiArith, and ARC-Challenge, and evaluated on eighteen models (1B to frontier scale). Two core results emerge: First, emotional framing reduces accuracy by 2-10 percentage points even though all numerical content is preserved. Second, neutralizing emotional variants recovers most of the lost performance, showing both that the degradation is tied to emotional style rather than content corruption and that neutralization can serve as a lightweight inference-time mitigation. Non-emotional paraphrases cause no such degradation, implicating emotional content rather than surface-level changes. Beyond emotion specifically, the benchmark construction procedure provides a general framework for controlled stylistic translation and robustness evaluation.

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

Emotional framing drops math accuracy 2-10 points on preserved content, and neutralization recovers most of it.

read the letter

Emotional framing drops math accuracy 2-10 points on preserved content, and neutralization recovers most of it. The paper builds a controlled translation framework that rewrites problems from GSM8K, MultiArith, and ARC-Challenge into emotional versions while keeping all numbers and relationships fixed. They produce Temper-5400 verified pairs and test eighteen models from 1B to frontier scale. The drop appears consistently, non-emotional paraphrases cause no drop, and stripping the emotion back out restores most performance. That points to a lightweight inference-time mitigation worth trying in practice. The construction and the multi-model results are the clearest new pieces. The scale of the benchmark and the clean separation from content changes give it a practical edge over vaguer robustness claims. The general style-translation procedure could also be reused for other perturbations. The verification step is the soft spot. The abstract claims semantic verification ensures exact preservation, but without details on the checks (numerical equality, operator matching, human review, or agreement metrics) it is hard to rule out that some pairs introduce small rephrasing effects that change difficulty. The non-emotional paraphrase control helps, yet more explicit documentation of how they confirmed no corruption would tighten the isolation of emotion as the cause. This is for people working on real-world LLM robustness and query handling. Readers who need benchmarks for style effects or quick mitigation ideas will get direct value. It deserves peer review because the core empirical pattern is testable and the mitigation is simple to check, even if the methods section needs expansion on verification and error analysis.

Referee Report

1 major / 2 minor

Summary. The paper claims to develop a controlled emotion translation framework that rewrites quantitative reasoning problems into emotional variants while preserving all quantities and relationships. It constructs the Temper-5400 dataset of 5,400 semantically verified emotion-neutral pairs from GSM8K, MultiArith, and ARC-Challenge. Evaluation on 18 models shows emotional framing reduces accuracy by 2-10 percentage points, with neutralization recovering most lost performance, and non-emotional paraphrases causing no degradation.

Significance. If the preservation of content is confirmed, the results indicate that emotional style in problem statements can significantly impact LLM performance on quantitative tasks, even when numbers and operations are identical. This has implications for the robustness of LLMs in real-world applications where emotional language is prevalent. The neutralization technique provides a potential lightweight fix, and the framework offers a template for testing other stylistic factors. The scale of the evaluation across multiple models and datasets adds to its potential impact.

major comments (1)

The abstract states that the framework 'preserves all quantities and relationships' and that pairs are 'semantically verified,' yet no description of the verification procedure is provided (human review, automated checks for numerical equality and operator identity, inter-annotator agreement?). This is load-bearing for the headline result, as subtle changes could produce the observed degradation without emotional effect.

minor comments (2)

The range of accuracy reduction (2-10 percentage points) is broad; providing more granular results per model size or dataset would strengthen the presentation.
No details on statistical significance tests or error analysis are mentioned, which would help assess the reliability of the reported drops.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thoughtful review and for highlighting the importance of transparency in our verification procedure. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses

Referee: The abstract states that the framework 'preserves all quantities and relationships' and that pairs are 'semantically verified,' yet no description of the verification procedure is provided (human review, automated checks for numerical equality and operator identity, inter-annotator agreement?). This is load-bearing for the headline result, as subtle changes could produce the observed degradation without emotional effect.

Authors: We agree that a detailed account of the verification procedure is essential to substantiate the claim that emotional framing, rather than content alteration, drives the observed accuracy drops. The current manuscript describes the overall construction pipeline in Section 3 but does not provide a dedicated, explicit subsection on verification steps. We will revise the paper to add a new subsection (3.3) that specifies: (1) automated checks confirming exact numerical equality and operator/relationship identity across each emotion-neutral pair, (2) the human review protocol (three independent annotators per pair, with instructions to flag any semantic drift or quantity change), and (3) the resulting inter-annotator agreement (Cohen's kappa). We will also reference this procedure from the abstract and introduction for visibility. These additions directly address the load-bearing concern and will be included in the revised version. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical dataset construction and evaluation

full rationale

The paper's core contribution is the construction of the Temper-5400 benchmark via an emotion translation framework that produces semantically verified pairs, followed by direct accuracy measurements across 18 models on GSM8K, MultiArith, and ARC-Challenge. No derivations, first-principles predictions, fitted parameters, or self-referential equations are present. Results (2-10 pp accuracy drop from emotional framing, recovery via neutralization) are reported as empirical observations rather than outputs that reduce to the inputs by construction. Non-emotional paraphrases are used as a control, but this remains a comparative measurement without definitional loops or load-bearing self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that emotional style can be isolated from numerical content via translation.

axioms (1)

domain assumption Emotional variants can be created while exactly preserving numerical content and relationships
Core premise of the controlled emotion translation framework.

pith-pipeline@v0.9.0 · 5492 in / 1036 out tokens · 42765 ms · 2026-05-10T18:04:39.801915+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

9 extracted references · 7 canonical work pages · 4 internal anchors

[1]

MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Han- naneh Hajishirzi. MathQA: Towards interpretable math word problem solving with operation-based formalisms.arXiv preprint arXiv:1905.13319,

work page Pith review arXiv 1905
[2]

Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? Try ARC, the AI2 reasoning challenge.arXiv preprint arXiv:1803.05457,

work page internal anchor Pith review Pith/arXiv arXiv
[3]

Training Verifiers to Solve Math Word Problems

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Dhariwal Prafulla, Timo Pohl, Alec Radford, Ilya Sutskever, et al. Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168,

work page internal anchor Pith review Pith/arXiv arXiv
[4]

DeepSeek-V3 Technical Report

DeepSeek-AI. DeepSeek-V3 technical report.arXiv preprint arXiv:2412.19437,

work page internal anchor Pith review Pith/arXiv arXiv
[5]

Distilling the Knowledge in a Neural Network

Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531,

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

2023 , month = nov, journal =

Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie. Large language models understand and can be enhanced by emotional stimuli.arXiv preprint arXiv:2307.11760, 2024a. Qintong Li, Leyang Cui, Xueliang Zhao, Lingpeng Kong, and Wei Bi. GSM-Plus: A com- prehensive benchmark for evaluating the robustne...

work page arXiv
[7]

Dear sir or madam, may I introduce the GYAFC dataset: Quantifying formality of text through crowdsourcing

Sudha Rao and Joel Tetreault. Dear sir or madam, may I introduce the GYAFC dataset: Quantifying formality of text through crowdsourcing. InProceedings of the 2018 Confer- ence of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 129–140,

2018
[8]

Under review

10 Preprint. Under review. Benjamin Reichman, Adar Avsian, Kartik Talamadupula, Toshish Jawale, and Larry Heck. Emotional RAG LLMs: Reading comprehension for the open internet.arXiv preprint arXiv:2408.11189,

work page arXiv
[9]

Solving general arithmetic word problems

Subhro Roy and Dan Roth. Solving general arithmetic word problems. InProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1743–1752,

2015