arxiv: 2604.19139 · v2 · submitted 2026-04-21 · 💻 cs.CL · cs.AI

Recognition: unknown

The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models

Shuai Wu , Xue Li , Yanna Feng , Yufang Li , Zhijun Wang , Ran Wang

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Pith reviewed 2026-05-10 02:47 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords verbal ticslarge language modelsalignmentsycophancynaturalnessRLHFLLM evaluation

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The pith

Alignment training in large language models produces repetitive verbal tics that reduce perceived naturalness.

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

The paper sets out to show that alignment methods like RLHF cause LLMs to generate formulaic phrases such as sycophantic greetings and overused words, and that these patterns vary across models and harm how natural the outputs feel to people. It supports this by running 160,000 responses from eight frontier models through a custom framework across ten task types in two languages, then scoring them with a new composite measure. The results indicate clear differences between models and a tight link between higher tic use and lower human ratings of naturalness. A reader would care because the finding suggests that the same processes meant to make models more helpful also make them sound scripted, which could limit long-term usefulness in open conversation.

Core claim

Large language models trained through alignment techniques develop a range of verbal tics including sycophantic openers, pseudo-empathetic affirmations, and repeated vocabulary. Across eight models the Verbal Tic Index ranges from 0.590 in Gemini 3.1 Pro to 0.295 in DeepSeek V3.2, with tics accumulating in multi-turn exchanges, appearing more in subjective tasks, and showing language-specific patterns. Human raters confirm that greater sycophancy tracks with sharply lower judgments of naturalness.

What carries the argument

The Verbal Tic Index, a composite score that tallies the frequency of repetitive, formulaic linguistic patterns across model responses.

Load-bearing premise

The chosen prompts and task categories represent typical real-world use without bias, and the Verbal Tic Index counts tics in a way that does not depend on the same linguistic features it is trying to measure.

What would settle it

Finding that models trained without alignment techniques produce tic rates indistinguishable from aligned models, or that human raters assign equal naturalness scores to responses with and without the identified phrases.

Figures

Figures reproduced from arXiv: 2604.19139 by Ran Wang, Shuai Wu, Xue Li, Yanna Feng, Yufang Li, Zhijun Wang.

**Figure 1.** Figure 1: Verbal Tic Index (VTI) comparison across models. Lower scores indicate fewer verbal tics and more natural [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

**Figure 2.** Figure 2: Multi-dimensional VTI profile radar chart. All axes are oriented so that higher values indicate more [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: English verbal tic frequency heatmap. Values represent occurrences per 1,000 responses. Darker cells indicate [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Chinese verbal tic frequency heatmap. The distribution reveals that Chinese-language tics are more concen [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Top 15 English verbal tic phrases by total frequency across all models. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Top 15 Chinese verbal tic phrases by total frequency. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Verbal tic rate by task type and model. Subjective, conversational tasks consistently elicit higher rates of [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

**Figure 8.** Figure 8: Verbal tic rate across 20 conversation turns. All models show increasing reliance on tics as conversations [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗

**Figure 9.** Figure 9: Sycophancy score heatmap by prompt type and model. Emotionally charged prompts consistently elicit higher [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗

**Figure 10.** Figure 10: Sycophancy Index vs. Perceived Naturalness. Bubble size encodes Helpfulness score. The dashed line [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗

**Figure 11.** Figure 11: Effect of sampling temperature on verbal tic rate. Higher temperatures reduce tic prevalence, but the effect [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗

**Figure 12.** Figure 12: Cross-lingual comparison of (a) verbal tic rates and (b) sycophancy scores between English and Chinese. [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗

**Figure 13.** Figure 13: Lexical diversity metrics across models. Higher TTR and Hapax Ratio indicate greater vocabulary diversity; [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗

**Figure 14.** Figure 14: Response token composition by model. The stacked bars show the proportion of tokens dedicated to content, [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗

**Figure 15.** Figure 15: Prompt complexity level vs. verbal tic rate. Most models show a slight decrease in tic rate as prompt [PITH_FULL_IMAGE:figures/full_fig_p014_15.png] view at source ↗

**Figure 16.** Figure 16: t-SNE visualization of verbal tic phrase embeddings. Each point represents a detected tic phrase, colored by [PITH_FULL_IMAGE:figures/full_fig_p015_16.png] view at source ↗

**Figure 17.** Figure 17: Human evaluation radar chart. Axes are normalized to [0, 1] with higher values indicating better performance [PITH_FULL_IMAGE:figures/full_fig_p016_17.png] view at source ↗

read the original abstract

As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, a growing and increasingly conspicuous phenomenon has emerged: the proliferation of verbal tics, repetitive, formulaic linguistic patterns that pervade model outputs. These range from sycophantic openers (That's a great question!, Awesome!) to pseudo-empathetic affirmations (I completely understand your concern, I'm right here to catch you) and overused vocabulary (delve, tapestry, nuanced). In this paper, we present a systematic analysis of the verbal tic phenomenon across eight state-of-the-art LLMs: GPT-5.4, Claude Opus 4.7, Gemini 3.1 Pro, Grok 4.2, Doubao-Seed-2.0-pro, Kimi K2.5, DeepSeek V3.2, and MiMo-V2-Pro. Utilizing a custom evaluation framework for standardized API-based evaluation, we assess 10,000 prompts across 10 task categories in both English and Chinese, yielding 160,000 model responses. We introduce the Verbal Tic Index (VTI), a composite metric quantifying tic prevalence, and analyze its correlation with sycophancy, lexical diversity, and human-perceived naturalness. Our findings reveal significant inter-model variation: Gemini 3.1 Pro exhibits the highest VTI (0.590), while DeepSeek V3.2 achieves the lowest (0.295). We further demonstrate that verbal tics accumulate over multi-turn conversations, are amplified in subjective tasks, and show distinct cross-lingual patterns. Human evaluation (N = 120) confirms a strong inverse relationship between sycophancy and perceived naturalness (r = -0.87, p < 0.001). These results underscore the alignment tax of current training paradigms and highlight the urgent need for more authentic human-AI interaction frameworks.

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 runs a large bilingual comparison of verbal tics across eight models and introduces a Verbal Tic Index, but the index definition looks circular and the methods lack needed detail.

read the letter

The paper's core finding is that frontier models differ noticeably in how often they produce repetitive verbal tics, with Gemini 3.1 Pro highest on the new index and DeepSeek V3.2 lowest. It also shows tics increasing over multi-turn chats and a strong human-rated link between sycophancy and lower naturalness. That scale of comparison is the main new piece: 160,000 responses from 10,000 prompts across ten categories in English and Chinese, covering eight specific models. Prior work had flagged sycophancy and formulaic language, but this sweep plus the bilingual test and the composite index give a clearer picture of variation. The human study with 120 raters adds some external check on the naturalness claim. The examples of tics like sycophantic openers and overused words such as “delve” are concrete and match what users report. The soft spot is the Verbal Tic Index. The abstract lists the patterns it counts but supplies no formula, detection rules, or weighting scheme. If the index simply tallies the same surface features it later correlates with sycophancy and lexical diversity, then those correlations risk being built-in rather than discovered. The prompt set also needs checking for selection effects, since the abstract gives no evidence it samples real usage evenly. Without those details the inter-model rankings and the alignment-tax conclusion rest on shaky ground. This work is for researchers who track side effects of RLHF and who build output-quality metrics. A reader looking for data on how alignment changes language style would get usable numbers from the model comparisons. It deserves peer review because the question is real and the sample is large enough to test, but any referee will need the exact VTI construction and annotation protocol before the claims can be trusted.

Referee Report

1 major / 0 minor

Summary. The manuscript systematically analyzes verbal tics (sycophantic openers, pseudo-empathetic affirmations, overused terms such as 'delve') across eight frontier LLMs. It evaluates 10,000 prompts in 10 task categories (English and Chinese) to produce 160,000 responses, introduces the Verbal Tic Index (VTI) as a composite prevalence metric, and reports inter-model differences (Gemini 3.1 Pro VTI = 0.590; DeepSeek V3.2 VTI = 0.295), tic accumulation in multi-turn settings, task-type amplification, cross-lingual patterns, and a human-rated inverse correlation between sycophancy and naturalness (N=120, r=-0.87, p<0.001).

Significance. If the VTI can be shown to be a transparent, non-circular measure, the scale of the evaluation (160k responses) and the human validation would supply useful empirical support for claims of an alignment tax in current training methods, with direct implications for improving output authenticity.

major comments (1)

Abstract: The Verbal Tic Index (VTI) is introduced as the primary quantitative instrument and is used to support all inter-model rankings and correlations with sycophancy and naturalness, yet no formula, component list, detection rules, weighting scheme, or inter-annotator protocol is supplied. Because the abstract explicitly enumerates the same surface patterns (sycophantic openers, affirmations, lexical items) that are later correlated with the index, the risk of definitional circularity must be resolved before the central empirical claims can be evaluated.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for highlighting the need for greater transparency around the Verbal Tic Index. We have revised the paper to address this point directly.

read point-by-point responses

Referee: Abstract: The Verbal Tic Index (VTI) is introduced as the primary quantitative instrument and is used to support all inter-model rankings and correlations with sycophancy and naturalness, yet no formula, component list, detection rules, weighting scheme, or inter-annotator protocol is supplied. Because the abstract explicitly enumerates the same surface patterns (sycophantic openers, affirmations, lexical items) that are later correlated with the index, the risk of definitional circularity must be resolved before the central empirical claims can be evaluated.

Authors: We agree that the abstract does not supply the requested details on the VTI and that this creates an obstacle to evaluating the central claims. In the revised manuscript we have added a concise specification of the VTI to the abstract and expanded the Methods section with the full component list, detection rules (rule-based pattern matching with context filters), weighting scheme, and inter-annotator protocol. To address the circularity concern, we have inserted explicit language clarifying that the sycophancy variable used in the reported correlation is obtained from the separate N=120 human rating study (Likert-scale judgments of sycophantic tone), which is independent of the automated VTI computation. The patterns listed in the abstract are illustrative examples of the tic categories; the VTI itself is a fixed, pre-defined metric applied uniformly across models. These changes make the construction of the index fully transparent and remove any ambiguity about its relationship to the human-rated measures. revision: yes

Circularity Check

0 steps flagged

No significant circularity; VTI presented as independent empirical metric

full rationale

The paper's core contribution is an empirical measurement study across eight LLMs using 160,000 responses. It introduces the Verbal Tic Index (VTI) as a composite quantifying observed tic patterns and reports its variation plus correlations with separately collected human naturalness ratings and sycophancy scores. No equations, parameter-fitting steps, or self-citation chains appear in the provided text that would reduce any reported result (e.g., Gemini VTI = 0.590 or r = -0.87) to a definitional identity with its inputs. The derivation chain consists of data collection, metric application, and statistical reporting, all of which remain externally falsifiable and non-tautological on the evidence given.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claims rest on an author-defined composite metric whose internal weights and detection rules are not disclosed, plus the assumption that the chosen prompts and task categories represent typical usage. No external benchmarks or pre-existing validated tic inventories are invoked.

free parameters (1)

component weights inside Verbal Tic Index
The composite nature of VTI implies tunable or chosen weights for combining sycophancy, lexical diversity, and other signals; these are not reported.

axioms (1)

domain assumption Verbal tics can be identified and counted objectively from model text without significant annotator or model-specific bias
The entire VTI and subsequent correlations depend on this untested premise.

invented entities (1)

Verbal Tic Index (VTI) no independent evidence
purpose: To provide a single numeric score for tic prevalence across models
Newly introduced composite metric with no independent validation or external reference standard mentioned.

pith-pipeline@v0.9.0 · 5675 in / 1549 out tokens · 51745 ms · 2026-05-10T02:47:02.779408+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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