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arxiv: 2606.00728 · v1 · pith:JWDUP5OPnew · submitted 2026-05-30 · 💻 cs.CL

From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users

Wuqiang Zheng , Chengbing Wang , Yilin Yang , Junyi Cheng , Jianfei Xiao , Hu Sun , Yi Xie , Yangyang Li
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Wenjie Wang
This is my paper

Pith reviewed 2026-06-28 18:53 UTC · model grok-4.3

classification 💻 cs.CL
keywords personalized empathylarge language modelsreward modelingempathetic strategiespersona datasetLLM evaluationlong-term interaction
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The pith

Adapting empathetic strategies to individual user personalities from history improves LLM performance in long-term interactions.

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

The paper establishes personalized empathy as a distinct task for LLMs, where responses must adapt to personality traits extracted from user history rather than applying generic empathy. It builds the PersonaEmp dataset from long-term user-AI exchanges that include persona details and empathy-seeking queries. A new reward modeling approach called PereGRM combines fixed empathy evaluation with dynamically generated criteria to produce finer-grained signals. Experiments across settings and multiple judge models indicate consistent gains from this method. If correct, this would mean current one-size-fits-all empathy training leaves measurable performance on the table when users differ in stable traits.

Core claim

The central claim is that reward modeling which integrates an empathy evaluation structure with dynamic evaluation criteria generation enables LLMs to select and apply empathetic strategies matched to users' personalized characteristics derived from interaction history, yielding stronger performance than prior approaches on the constructed PersonaEmp dataset.

What carries the argument

PereGRM, a reward modeling framework that combines the empathy evaluation structure with dynamic evaluation criteria generation for fine-grained reward modeling.

If this is right

  • Reward models that incorporate dynamic criteria can produce more effective personalized empathetic outputs than static evaluation alone.
  • Datasets built from extended user-AI histories enable measurable study of personality-driven empathy adaptation.
  • Performance gains from the approach hold when evaluated by multiple different judge models and across varied experimental settings.
  • The task of personalized empathy requires explicit modeling of long-term user characteristics rather than single-turn context alone.

Where Pith is reading between the lines

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

  • If the approach scales, dialogue systems could maintain consistent empathetic tone across many sessions without manual persona engineering.
  • The same dynamic-criteria idea might transfer to other subjective qualities such as humor or politeness that also vary by user.
  • Real-world deployment would require testing whether history-derived traits remain predictive when users change over months or years.
  • Integration with existing user modeling pipelines could reduce the need for separate personality questionnaires.

Load-bearing premise

User personality traits derived from history provide a reliable and stable signal for selecting empathetic strategies, and the PersonaEmp dataset accurately captures long-term interaction dynamics without significant selection or annotation bias.

What would settle it

A controlled study that measures whether strategy selection based on history-derived personality traits produces higher user-rated satisfaction or engagement than generic empathy, or whether the reported gains vanish under alternative dataset construction or different personality annotation methods.

Figures

Figures reproduced from arXiv: 2606.00728 by Chengbing Wang, Hu Sun, Jianfei Xiao, Junyi Cheng, Wenjie Wang, Wuqiang Zheng, Yangyang Li, Yilin Yang, Yi Xie.

Figure 1
Figure 1. Figure 1: An example illustrating personalized empathy. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: PersonaEmp dataset construction pipeline. We first collect long-term user-AI interactions with extracted [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our PereGRM framework. The GRM-based judge model leverages both user-AI interaction [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) Performance comparison of PereGRM with and without the empathy evaluation framework. (b) Performance of PereGRM under different reward sam￾pling numbers K, compared with manually designed fixed evaluation criteria. is insufficient for learning personalized empa￾thetic ability. PERM, as a generic empathy RL method, consistently improves empathetic ca￾pability across different settings, but its user￾agno… view at source ↗
Figure 5
Figure 5. Figure 5: Performance comparison across users with [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Statistical information of the dataset PersonaEmp. The left figure shows the distribution of different [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: An example illustrating the differences in response strategies among different models. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Trade-off between efficiency and performance [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Base prompt for PereGRM. Measures the depth and accuracy of the responder's ability to enter the user's Internal Frame of Reference, with special emphasis on personality detection. It evaluates whether the responder captures: 1. The explicit emotion and content. 2. The causal link to the user's stable personality traits (from memory). 3. The deeper psychological need behind the reaction [PITH_FULL_IMAGE:f… view at source ↗
Figure 12
Figure 12. Figure 12: General criteria for Reception Rrec. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
read the original abstract

As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability. However, existing research overlooks the influence of users' personality traits on empathetic strategies during long-term interactions. To address this gap, we introduce the task of personalized empathy, which focuses on adapting empathetic strategies according to users' personalized characteristics derived from history. To study and enhance this capability, we construct PersonaEmp, a personalized empathy dataset built from long-term user-AI interactions, featuring rich user histories, persona information, and empathy-seeking queries. We further propose PereGRM, a reward modeling framework that combines the empathy evaluation structure with dynamic evaluation criteria generation for fine-grained reward modeling. Experimental results across different settings and multiple judge models show that PereGRM consistently achieves the strongest performance improvements, indicating its effectiveness for enhancing personalized empathetic capabilities.

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 / 0 minor

Summary. The paper introduces the task of personalized empathy for LLMs in long-term interactions, which adapts empathetic strategies according to users' personality traits derived from interaction history. It constructs the PersonaEmp dataset from long-term user-AI interactions, including rich user histories, persona information, and empathy-seeking queries. The authors propose PereGRM, a reward modeling framework combining an empathy evaluation structure with dynamic evaluation criteria generation for fine-grained modeling. Experimental results across settings and multiple judge models are reported to show that PereGRM achieves the strongest performance improvements.

Significance. If the central empirical claims hold after verification of experimental details, the work addresses an important gap by moving from generic empathy to personalization based on stable user traits. The new PersonaEmp dataset and the PereGRM framework could provide useful resources and methods for reward modeling in empathetic dialogue systems. The reported use of multiple judge models is a positive step toward robustness assessment.

major comments (2)
  1. [Abstract] Abstract: The claim that 'PereGRM consistently achieves the strongest performance improvements' is the central empirical result, yet the abstract provides no information on experimental details, error bars, dataset construction methods, judge-model prompts, or statistical significance tests. This prevents verification of whether the reported gains are load-bearing or could be explained by uncontrolled variables in the PersonaEmp construction.
  2. [Abstract] Abstract: The weakest assumption—that user personality traits derived from history provide a reliable and stable signal for selecting empathetic strategies, and that PersonaEmp accurately captures long-term dynamics without selection or annotation bias—is required for the task to be well-posed, but no evidence or validation procedure for this assumption is described in the provided text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments highlighting the need for greater transparency in the abstract and for questioning the core assumptions of the personalized empathy task. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'PereGRM consistently achieves the strongest performance improvements' is the central empirical result, yet the abstract provides no information on experimental details, error bars, dataset construction methods, judge-model prompts, or statistical significance tests. This prevents verification of whether the reported gains are load-bearing or could be explained by uncontrolled variables in the PersonaEmp construction.

    Authors: We agree the abstract is overly concise and omits these details, which are instead provided in Sections 3 (dataset construction), 4 (experimental settings with multiple judge models), and 5 (results). To improve verifiability, we will revise the abstract to briefly note the evaluation across settings and multiple judges, the PersonaEmp construction process, and that improvements are observed consistently without claiming statistical significance tests that were not performed. revision: yes

  2. Referee: [Abstract] Abstract: The weakest assumption—that user personality traits derived from history provide a reliable and stable signal for selecting empathetic strategies, and that PersonaEmp accurately captures long-term dynamics without selection or annotation bias—is required for the task to be well-posed, but no evidence or validation procedure for this assumption is described in the provided text.

    Authors: The manuscript's Section 3 details how PersonaEmp is built from long-term interactions to derive persona information and empathy-seeking queries. However, we acknowledge that the abstract (and to some extent the provided excerpt) does not explicitly describe validation for trait stability or bias checks. We will add a short clause in the revised abstract referencing the long-term interaction sourcing as the basis for this assumption, while noting that deeper bias analysis could be expanded in future work if space permits. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces a new task (personalized empathy), constructs a dataset (PersonaEmp), proposes a reward modeling framework (PereGRM), and reports experimental performance gains across settings and judges. No equations, derivations, or parameter-fitting steps are described that reduce by construction to the inputs or evaluation signals. Claims rest on empirical results rather than any self-definitional, fitted-prediction, or self-citation load-bearing chain. This is a standard empirical NLP paper with no detectable internal circularity in its reported methodology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; typical reward modeling papers introduce fitted scaling factors and domain assumptions about personality stability that are not visible here.

pith-pipeline@v0.9.1-grok · 5698 in / 1123 out tokens · 14232 ms · 2026-06-28T18:53:49.979448+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

15 extracted references · 6 canonical work pages · 4 internal anchors

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