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AI with Emotions: Exploring Emotional Expressions in Large Language Models

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Told to feel a specific arousal-valence state, most large language models reply in that emotional register.

desk verdict Simple, honest experiment on numeric emotion prompting; the unvalidated GoEmotions-to-Russell label mapping makes the quantitative model ranking conditional. read the letter →

arxiv 2504.14706 v2 pith:2TWHSUJH submitted 2025-04-20 cs.AI

Shin-nosuke Ishikawa , Atsushi Yoshino This is my paper · ORCID
classification cs.AI
keywords emotionalexpressionlargelanguagemodelsRussell'scircumplexmodelarousalvalenceGoEmotionssentimentanalysisrole-playprompting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This study asks whether current large language models can make their written answers carry a specified emotion, not just a specified topic. The authors define each target emotion numerically, using the two axes of Russell's circumplex model (arousal and valence), feed the numbers to nine large language models in a role-play prompt, and score the replies with an independent sentiment classifier trained on GoEmotions. Across 12 evenly spaced emotional states and 10 questions, mean cosine similarity between the specified state and the state inferred from the text is positive for most model-question pairs and exceeds the heuristic baseline of 0.061. The authors conclude that LLMs can control their output emotional state within a range, which matters because emotion-typed agents become usable as advisors, consultants, or creative partners.

What carries the argument

The load-bearing object is Russell's circumplex model of affect, which places any emotional state at a point on a circle with axes arousal (sleepy-activated) and valence (pleasure-displeasure). The experiment fixes each target as a unit vector at one of 12 equally spaced angles, feeds the two coordinates into a role-play prompt, and measures the output by cosine similarity between the target vector and the vector of the sentiment label assigned by the GoEmotions-trained classifier. The chain lives or dies with the manual Table 3 mapping that converts 27 GoEmotions labels into Russell coordinates, because that mapping decides whether a response matches its instructed emotion.

What would settle it

Re-run the protocol with the Table 3 mapping replaced by coordinates from human raters or an independently published norming study, and compare mean cosine similarities. If the values fall to the 0.061 baseline or below across models, the reported emotional control was an artifact of the mapping rather than a property of the LLMs.

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Extended reading notes

Core claim

The central claim is that LLMs can translate a numerically specified emotional state into text whose emotional content matches the specification. The experiment uses the Russell circumplex: target states are unit vectors at 12 evenly spaced angles in the arousal-valence plane, and each prompt tells the model to role-play an agent experiencing that state while answering one of ten open questions. A BERT-based sentiment classifier trained on GoEmotions labels each generated text, and the label is mapped through the authors' Table 3 correspondence to a point in the same plane. Positive mean cosine similarities, generally above the 0.061 chance-level baseline, lead the authors to conclude that the models control their expressed emotional state within a certain range; GPT-4, GPT-4 turbo, and Llama3 70B Instruct show the steadiest performance, while GPT-3.5 turbo lags.

Load-bearing premise

The whole measurement rests on the authors' hand-built mapping from GoEmotions labels to coordinates on Russell's circle, which was not validated against human judgments or an independent reference.

Editorial extensions

If this is right

  • If the claim is right, the emotional tone of an LLM agent can be selected at inference time from two continuous numbers, without retraining or fine-tuning.
  • Because the same control appeared across ten unrelated questions, the effect is not tied to one topic: tone-setting generalizes to a range of conversations.
  • Continuous arousal and valence coordinates open the door to scripting emotional dynamics, such as an agent that grows calmer or more agitated over the course of a dialogue.
  • Emotion-typed agents become feasible for applications where a deliberate emotional stance matters, such as advisors that can disagree with a user while staying in a chosen register.
  • The observed differences across models suggest that emotional control is not simply a function of parameter count; training data and alignment choices matter.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The mapping in Table 3 could be doing much of the work: if a different but equally plausible label-to-coordinate mapping lowered all similarities, the models' apparent emotional expression would shrink toward a property of the evaluator rather than the generator.
  • Because the sentiment evaluator was trained on Reddit comments, its label geometry reflects one online register; an English-only, Reddit-derived evaluation may understate how well LLMs express emotion in other cultural or conversational contexts.
  • Showing that a model can imitate a specified emotional register is not evidence that the model experiences emotion; the data support instruction-following in expressive style, not inner states.
  • A natural next test is human-participant evaluation: if human raters agree with the classifier's label assignments on the same generated texts, the reported alignment would be robust to the choice of evaluator; if not, the effect is partly measurement-specific.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a framework for exploring whether current LLMs can express specified emotional states in generated text. Using Russell's Circumplex Model, the authors specify 12 emotional states as (valence, arousal) unit vectors and prompt nine LLMs (GPT-3.5/4/4-turbo/4o, Gemini 1.5 Flash/Pro, Llama3-8B/70B, Command R+) to answer ten questions while role-playing an agent with each specified state. The outputs are evaluated with a BERT-based sentiment classifier trained on GoEmotions; classifier predictions are mapped through a hand-crafted correspondence table (Table 3) to unit vectors in the same arousal–valence space, and cosine similarity between specified and evaluated vectors is the main metric. The authors report positive mean similarities for most model–question combinations, with GPT-4, GPT-4 turbo, and Llama3-70B Instruct showing the highest totals, and conclude that LLMs can control their outputs with specified emotional states. The paper also includes a baseline (0.061), an appendix validating the sentiment model via the same mapping, a check that text length does not correlate with similarity, and a word-conditioned control experiment.

Significance. If the central claim is adequately supported, the paper would provide a simple and transparent protocol for continuous emotional control of LLM-based agents, with plausible applications in advisory systems, creative generation, and human–AI interaction. The experimental design is commendably explicit: all models, prompts, and questions are listed, a heuristic baseline is used, and the authors checked for text-length confounds. However, the quantitative conclusion rests entirely on the authors' unvalidated mapping from GoEmotions labels to Russell coordinates, and the reported numbers lack any measure of uncertainty. Without external validation of that mapping and significance testing against the baseline, the model ranking and the 'capability for emotional expression' claim are conditional. With such validation, the paper would be a useful empirical contribution.

major comments (3)
  1. [Section 3.2, Table 3 (Appendix B)] Every cosine similarity reported in Table 2 and Figure 4 is computed from the unit vectors assigned in Table 3, yet that table is the product of the authors' own judgment and is not validated against human ratings, established affective norms, or an independent source. Entries such as 'confusion' mapped to 'alarmed', 'optimism' to 'at ease', 'disgust' to 'frustrated', and 'desire' to 'excited, aroused' illustrate that plausible alternative mappings exist, and a different mapping would change the numerical results and potentially the model ranking, including the claimed superiority of GPT-4, GPT-4 turbo, and Llama3-70B Instruct. This mapping is load-bearing for the central claim, so the authors should validate it externally and report a robustness analysis over alternative mappings.
  2. [Appendix A] Appendix A is presented as evidence that the sentiment analysis model can estimate emotional states in Russell space, but it uses the same Table 3 mapping to place both correct and predicted labels. This makes the reported mean cosine similarity of 0.680 a consistency check of the mapping, not an independent validation: a systematic error in Table 3 would shift the Appendix A results and the generation results in Table 2 in the same direction. The paper therefore cannot use Appendix A as support for the validity of the evaluation, and the central claim remains conditional on the unvalidated mapping.
  3. [Section 4, Table 2, Figure 4] Table 2 reports only means over the ten questions, with no standard deviations, confidence intervals, or significance tests, and its caption states that 'positive significance of all values' confirms the capability even though two cells are negative (GPT-3.5 Q2: -0.048; Gemini 1.5 Flash Q9: -0.049) and several cells fall below the 0.061 baseline. The discussion claims that 'LLMs can control their outputs with specified emotional states within a certain range,' but no statistical test quantifies this range or compares the observed aggregate similarities against the baseline. Please add dispersion measures and appropriate inferential tests, and correct the caption to reflect the actual values.
minor comments (4)
  1. [References] In the reference to Khan (2022), 'Febrary' should be 'February'.
  2. [Figure 1] Figure 1 appears as garbled or obfuscated text in the manuscript; if the published figure is likewise unreadable, it should be replaced with the actual prompt text.
  3. [Table 5 (Appendix D)] Table 5 (Appendix D) uses inconsistent decimal formatting, e.g., '0.66' in the GPT-3.5 Q4 column among three-decimal entries.
  4. [Table 2 caption] The phrase 'the positive significance of all values' in the Table 2 caption contradicts the negative entries; reword to 'the majority of values are positive' unless a significance test is added.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the generation targets and the evaluation labels are independent, and the author-defined label-to-Russell mapping is an unvalidated assumption, not a fitted input.

full rationale

The paper's central claim is that LLMs can, within a range, make generated text reflect specified arousal and valence coordinates. The derivation chain is: (1) the prompt specifies a Russell-coordinate emotional state; (2) an independent BERT-based sentiment classifier trained on GoEmotions assigns a discrete label to the generated text; (3) Table 3 maps each GoEmotions label to a Russell-coordinate vector; (4) cosine similarity is computed between the specified vector and the mapped predicted label. No parameter is fitted to the LLM outputs, no subset of the data is used to set a value that is then 'predicted' on a closely related quantity, and no self-citation or uniqueness theorem is invoked to force the result. The generation and evaluation models are different: the sentiment model is trained on human-annotated Reddit data and is independent of the GPT/Gemini/Llama/Command R+ generators. The main weakness is that Table 3, the correspondence between GoEmotions labels and Russell's terms, is authored by the researchers and is not independently validated; a different mapping could change the numerical similarities and possibly the model ranking. However, this is a measurement-validity and calibration concern, not circularity: the paper does not define the predicted emotional state in terms of the mapping, nor does it fit the mapping to the data. The Appendix A validation of the sentiment model also uses the same Table 3 mapping for both correct and predicted labels, so it cannot independently validate the mapping itself; again, this weakens the evidence but does not make the main result true by construction. The paper's own Limitation section acknowledges that 'the use of a discrete classifier may influence the evaluation metrics,' which is the appropriate place for this concern. Overall, the claimed capability is an empirical, externally evaluable result, and no circular step in the derivation chain was found.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or mathematical entities. Its free parameters are limited to the manual label mapping and the design choices of 12 states and unit-length vectors. The axioms are standard psychological assumptions about emotion representation and the validity of machine sentiment analysis.

free parameters (1)
  • GoEmotions-to-Russell label correspondence = hand-coded (Table 3)
    Each GoEmotions label is assigned one or two Russell terms, and the resulting unit vectors are used to compute all cosine similarities. Different mappings would change the results.
assumptions (4)
  • domain assumption Russell's Circumplex Model with arousal and valence axes adequately represents emotional states for text generation.
    The paper adopts this framework as the basis for specifying and evaluating emotions; it is not derived or validated within this work.
  • ad hoc to paper Every GoEmotions label can be mapped onto a point or mean of points in Russell's arousal-valence space.
    The mapping in Table 3 is constructed by the authors for this study and directly defines the evaluation metric.
  • domain assumption Cosine similarity in the arousal-valence plane is a valid measure of consistency between specified and expressed emotion.
    The paper uses cosine similarity as its headline metric without independent validation of this metric's psychological meaning.
  • domain assumption The sentiment model (BERT trained on GoEmotions) correctly classifies the emotional content of generated texts often enough to support the conclusions.
    Appendix A validates the model on GoEmotions test data, but the test distribution differs from the generated texts, and the mean cosine similarity of 0.680 is modest.

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Pith. "Pith review of AI with Emotions: Exploring Emotional Expressions in Large Language Models." pith.science (2026). https://pith.science/paper/2TWHSUJH

@misc{pith2026250414706,
  author       = {Pith},
  title        = {Pith review of: AI with Emotions: Exploring Emotional Expressions in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2TWHSUJH}},
  note         = {Machine review of arXiv:2504.14706}
}
read the original abstract

The human-level performance of Large Language Models (LLMs) across various tasks has raised expectations for the potential of Artificial Intelligence (AI) to possess emotions someday. To explore the capability of current LLMs to express emotions in their outputs, we conducted an experiment using several LLMs (OpenAI GPT, Google Gemini, Meta Llama3, and Cohere Command R+) to role-play as agents answering questions with specified emotional states. We defined the emotional states using Russell's Circumplex model, a well-established framework that characterizes emotions along the sleepy-activated (arousal) and pleasure-displeasure (valence) axes. We chose this model for its simplicity, utilizing two continuous parameters, which allows for better controllability in applications involving continuous changes in emotional states. The responses generated were evaluated using a sentiment analysis model, independent of the LLMs, trained on the GoEmotions dataset. The evaluation showed that the emotional states of the generated answers were consistent with the specifications, demonstrating the LLMs' capability for emotional expression. This indicates the potential for LLM-based AI agents to simulate emotions, opening up a wide range of applications for emotion-based interactions, such as advisors or consultants who can provide advice or opinions with a personal touch.

Figures

Figures reproduced from arXiv: 2504.14706 by the authors.

Figure 1
Figure 1. Input prompt for text generation with a spec [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Mapping of the GoEmotions labels in the arousal–valence space, as detailed in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Examples of answers generated with specified emotional states include: (1) GPT-4 with arousal: 0.866, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Correlation of emotional states in radial coordinates in the arousal–valence space between the state [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 6
Figure 6. Figure 6: Summary of the number of words generated [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Relation between the number of words and [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Input prompt for text generation with a speci [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.