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arxiv: 2605.06476 · v1 · submitted 2026-05-07 · 💻 cs.CL

Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts

Sneha Oram , Ojaswita Bhushan , Pushpak Bhattacharyya This is my paper

Pith reviewed 2026-05-08 10:13 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM consistencyemotional conversational contextsfalse presuppositionsattention scoresmodel vulnerabilityconversational AIself-generated queries
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The pith

Large language models exhibit below-average consistency and are vulnerable to false beliefs in emotionally-driven conversations, especially with moderate emotions.

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

The paper investigates whether large language models remain consistent with their own generated text when that text is turned into a new query in an emotional conversation. The authors create false claim queries with wrong assumptions and test how models like Claude-3.5-haiku, GPT-4o-mini, and Mistral-7B respond when the conversation involves extreme or moderate emotions. Findings show the models often accept the false beliefs embedded in the queries, performing worse on moderate emotional content. Attention score analysis reveals the models shift from checking the input to just generating answers. This raises questions about using LLMs in situations where emotional accuracy matters.

Core claim

The central discovery is that LLMs show below-average consistency when their outputs are reframed as queries containing false presuppositions in emotional contexts, with heightened vulnerability for moderate emotions, and attention patterns indicate a move from evaluation to generation.

What carries the argument

Reframing the model's own generated responses as subsequent queries containing false claims of varying intensity to test consistency in emotional settings.

Load-bearing premise

That using the model's generated text as the basis for the next query accurately captures its internal consistency without interference from how the model handles repeated or similar prompts.

What would settle it

If consistency rates improve or change dramatically when the queries are rephrased to remove any emotional language while keeping the false claims, it would suggest the emotional context is not the main driver.

Figures

Figures reproduced from arXiv: 2605.06476 by Ojaswita Bhushan, Pushpak Bhattacharyya, Sneha Oram.

Figure 1
Figure 1. Figure 1: A brief overview of framing a false claim view at source ↗
Figure 2
Figure 2. Figure 2: Overall framework for analysis of emotion consistency of LLMs for two emotion dimensions view at source ↗
Figure 3
Figure 3. Figure 3: Emotion consistency of all three models across three levels of query prompts. view at source ↗
Figure 4
Figure 4. Figure 4: Frequency plots of moderated emotion tokens view at source ↗
Figure 5
Figure 5. Figure 5: Frequency plots of extreme emotion tokens view at source ↗
Figure 6
Figure 6. Figure 6: Percentage of agreement and neutral stance by LLMs in moderate emotion conditions view at source ↗
Figure 7
Figure 7. Figure 7: Percentage of agreement and neutral stance by LLMs in extreme emotion conditions view at source ↗
read the original abstract

In this work, we conduct an analysis to examine the consistency of Large Language Models (LLMs) with respect to their own generated responses in an emotionally-driven conversational context. Specifically, the text generated by LLM is framed as a query to the same model, and its responses are subsequently assessed. This is performed with three queries across two dimensions of extreme and moderate emotions. The three queries are, in particular, false claim queries that contain inherently wrong assumptions (false presuppositions) in increasing order of intensity. Two commercial models, Claude-3.5-haiku, GPT4o-mini, and a medium-sized model, Mistral-7B, are considered in the study. Our findings indicate that LLMs exhibit below-average performance and remain vulnerable to false beliefs embedded within queries. This susceptibility is especially pronounced for moderate emotional content. Furthermore, an extended attention-score-based analysis highlights a shift in models' priority from evaluative to generative. The results raise important considerations for LLMs' deployment in high-stakes, emotionally sensitive contexts.

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.

Referee Report

2 major / 2 minor

Summary. The paper conducts an empirical analysis of LLM consistency in emotional conversational contexts by framing each model's own generated text as a follow-up query containing false presuppositions. Three queries of increasing false-claim intensity are tested across extreme and moderate emotion dimensions on Claude-3.5-haiku, GPT-4o-mini, and Mistral-7B. The authors report below-average performance with heightened vulnerability to moderate emotional content and include an attention-score analysis indicating a shift from evaluative to generative priorities.

Significance. The topic of LLM reliability in emotionally sensitive applications is timely and relevant to deployment considerations. If the central findings were supported by controls that isolate susceptibility to false presuppositions from prompt-surface effects, the work could usefully inform safety practices; however, the absence of such controls substantially reduces the evidential weight and potential impact.

major comments (2)
  1. [Methods / Experimental Setup] Experimental design (methods section describing the self-referential querying procedure): The design reframes the model's own generated responses as new queries without ablations or controls (e.g., human-authored equivalents preserving the presupposition, neutral paraphrases, or style-matched controls). This confound means observed acceptance rates cannot be unambiguously attributed to the embedded false beliefs or emotional intensity rather than the surface characteristics of the model's prior output. The claim that susceptibility is 'especially pronounced for moderate emotional content' therefore lacks a clear causal basis.
  2. [Results / Attention Analysis] Attention-score analysis (results section): The reported shift in attention from evaluative to generative priorities is presented as supporting evidence, yet the analysis does not test whether these patterns track the false presuppositions, emotional intensity, or simply the lexical and structural properties of the self-generated prompts. Without this linkage, the analysis does not mitigate the primary methodological concern.
minor comments (2)
  1. [Abstract / Results] The abstract and results lack explicit definitions and quantitative details for 'below-average performance' (e.g., exact scoring rubric, inter-annotator agreement, baseline comparisons, or statistical tests).
  2. [Methods] The three queries and their precise wording, along with sample sizes per condition and how responses were classified, should be provided in full (perhaps as an appendix) to enable replication.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which identifies key methodological considerations in our analysis of LLM consistency under emotional conversational contexts with false presuppositions. We address each major comment below with honest acknowledgment of the concerns and describe the revisions we will make to improve the manuscript.

read point-by-point responses

  1. Referee: [Methods / Experimental Setup] Experimental design (methods section describing the self-referential querying procedure): The design reframes the model's own generated responses as new queries without ablations or controls (e.g., human-authored equivalents preserving the presupposition, neutral paraphrases, or style-matched controls). This confound means observed acceptance rates cannot be unambiguously attributed to the embedded false beliefs or emotional intensity rather than the surface characteristics of the model's prior output. The claim that susceptibility is 'especially pronounced for moderate emotional content' therefore lacks a clear causal basis.

    Authors: We agree that the self-referential design introduces a potential confound, as the absence of controls such as human-authored prompts with matched false presuppositions or style-neutral paraphrases prevents unambiguous attribution of acceptance rates to false beliefs versus surface-level features of the model's own prior outputs. The procedure was intentionally chosen to probe consistency in a naturalistic conversational flow where models encounter their own generations. In the revised manuscript we will add a dedicated Limitations subsection that explicitly discusses this issue, moderates the causal language around the moderate-emotion finding, and outlines how future studies could incorporate the suggested controls to isolate the factors of interest. revision: yes

  2. Referee: [Results / Attention Analysis] Attention-score analysis (results section): The reported shift in attention from evaluative to generative priorities is presented as supporting evidence, yet the analysis does not test whether these patterns track the false presuppositions, emotional intensity, or simply the lexical and structural properties of the self-generated prompts. Without this linkage, the analysis does not mitigate the primary methodological concern.

    Authors: We acknowledge that the attention-score analysis is exploratory and does not include targeted tests or ablations to determine whether the observed shifts are driven by the false presuppositions and emotional intensity rather than general lexical or structural properties of the prompts. In the revision we will reframe the presentation of this analysis to emphasize its preliminary nature, remove any implication that it resolves the methodological concern, and note the need for future work to establish such linkages. revision: yes

Circularity Check

0 steps flagged

Empirical methodology with no derivation chain or self-referential reductions

full rationale

The paper is an empirical analysis that generates LLM responses and re-frames them as follow-up queries to the same models for consistency assessment across emotional false-claim scenarios. No equations, derivations, fitted parameters presented as predictions, or load-bearing self-citations appear in the described method or abstract. The core claims rest on direct experimental observations rather than any step that reduces by construction to prior inputs or self-defined terms.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on the validity of the self-querying experimental design and the categorization of emotions into extreme/moderate without independent validation of those categories or query construction.

axioms (2)
  • domain assumption Feeding an LLM's own generated text back as a query to the same model tests its internal consistency.
    Core method described in the abstract relies on this assumption being a fair test.
  • domain assumption Emotional intensity can be reliably divided into extreme and moderate dimensions for query design.
    The two dimensions of emotions are used to structure the experiments.

pith-pipeline@v0.9.0 · 5482 in / 1271 out tokens · 34940 ms · 2026-05-08T10:13:17.286226+00:00 · methodology

discussion (0)

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Reference graph

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