arxiv: 2604.13398 · v1 · submitted 2026-04-15 · 💻 cs.CL · cs.AI

Recognition: unknown

From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning

Shihao Zhang , Ziwei Wang , Jie Zhou , Yulan Wu , Qin Chen , Zhikai Lei , Liyang Yu , Liang Dou

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Liang He

Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:58 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords aspect-based sentiment analysisreinforcement learningnatural language reasoningsentiment classificationinterpretabilitylarge language modelsreward model

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The pith

ABSA-R1 trains language models to generate explicit reasoning before assigning sentiment labels, using reinforcement learning to align the reasoning with the final prediction.

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

The paper introduces a framework that shifts aspect-based sentiment analysis from direct prediction to a reason-then-predict process modeled on human cognition. It uses reinforcement learning to train models to produce natural language justifications that must remain consistent with the eventual sentiment label. A dedicated reward model and rejection sampling for difficult cases are added to enforce this alignment. Results across four benchmarks show gains in both classification accuracy and triplet extraction, plus improved interpretability over standard non-reasoning baselines.

Core claim

By framing sentiment analysis as a cognitive process that first produces a natural-language justification and then derives the label from it, ABSA-R1 learns via reinforcement learning to keep the generated reasoning path consistent with the final emotional label, outperforming non-reasoning models on sentiment classification and aspect-sentiment triplet extraction.

What carries the argument

The Cognition-Aligned Reward Model, which scores generated reasoning for consistency with the final sentiment label, combined with performance-driven rejection sampling that targets uncertain cases.

If this is right

Sentiment models can now output human-readable justifications that are directly tied to their label decisions.
The same reason-before-predict loop improves results on both polarity classification and aspect-sentiment triplet extraction tasks.
Interpretability is gained without sacrificing accuracy, and in some cases accuracy increases.
Hard examples can be selectively improved by focusing sampling on cases where internal consistency is low.

Where Pith is reading between the lines

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

The approach may generalize to other classification tasks that currently lack explicit reasoning steps, such as stance detection or emotion recognition.
If the reward model can be made label-independent, the method could reduce reliance on ground-truth labels during training.
Future work could test whether the generated rationales transfer to human evaluation of model trustworthiness.

Load-bearing premise

That the reward model can measure genuine consistency between reasoning and label without creating circular dependence on the label or injecting bias into the reinforcement learning updates.

What would settle it

An experiment in which the reward model assigns high scores to reasoning paths that contradict the assigned label, or an ablation removing the reward model and rejection sampling that eliminates the reported performance gains.

Figures

Figures reproduced from arXiv: 2604.13398 by Jie Zhou, Liang Dou, Liang He, Liyang Yu, Qin Chen, Shihao Zhang, Yulan Wu, Zhikai Lei, Ziwei Wang.

**Figure 1.** Figure 1: An example of sentiment thinking. classification or sequence labeling problem, achieving strong performance in label prediction (Li et al., 2019). Furthermore, large language models (LLMs) such as T5 (Chung et al., 2022) and LLaMA (MetaAI, 2024) have been utilized to generate aspects, opinions, and sentiments via instruction tuning (Scaria et al., 2024; Z. Wang et al., 2024). However, a critical cognitive … view at source ↗

**Figure 2.** Figure 2: The framework of our ABSA-R1, which simulates a System 2 thinking process by generating explicit rationales before [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Figure 4: Evaluation of Sentiment Reasoning. Further Analysis Case Studies. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

read the original abstract

While Aspect-based Sentiment Analysis (ABSA) systems have achieved high accuracy in identifying sentiment polarities, they often operate as "black boxes," lacking the explicit reasoning capabilities characteristic of human affective cognition. Humans do not merely categorize sentiment; they construct causal explanations for their judgments. To bridge this gap, we propose ABSA-R1, a large language model framework designed to mimic this ``reason-before-predict" cognitive process. By leveraging reinforcement learning (RL), ABSA-R1 learns to articulate the why behind the what, generating natural language justifications that ground its sentiment predictions. We introduce a Cognition-Aligned Reward Model (formerly sentiment-aware reward model) that enforces consistency between the generated reasoning path and the final emotional label. Furthermore, inspired by metacognitive monitoring, we implement a performance-driven rejection sampling strategy that selectively targets hard cases where the model's internal reasoning is uncertain or inconsistent. Experimental results on four benchmarks demonstrate that equipping models with this explicit reasoning capability not only enhances interpretability but also yields superior performance in sentiment classification and triplet extraction compared to non-reasoning baselines.

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 manuscript proposes ABSA-R1, an LLM framework for Aspect-Based Sentiment Analysis that employs reinforcement learning to generate natural language justifications prior to sentiment predictions, mimicking human 'reason-before-predict' cognition. It introduces a Cognition-Aligned Reward Model to enforce consistency between the reasoning path and final emotional label, plus a performance-driven rejection sampling strategy for hard cases. The central claim is that this yields both improved interpretability and superior performance on sentiment classification and triplet extraction across four benchmarks relative to non-reasoning baselines.

Significance. If the empirical gains are robust and the reward model demonstrably avoids circular dependence on labels, the work could advance interpretable NLP by showing that explicit reasoning mechanisms can enhance both accuracy and transparency in affective tasks. The RL-based alignment with metacognitive rejection sampling offers a concrete path toward human-like justification in sentiment systems, but only if the consistency enforcement is shown to be non-circular and the performance claims are backed by detailed metrics.

major comments (2)

[Abstract] Abstract: The abstract states that the approach 'yields superior performance in sentiment classification and triplet extraction' on four benchmarks but supplies no metrics, baselines, statistical tests, effect sizes, or implementation details. Without these, the central empirical claim cannot be evaluated and risks being unsupported.
[Abstract] Abstract (Cognition-Aligned Reward Model description): The reward model is described as enforcing 'consistency between the generated reasoning path and the final emotional label,' yet the formulation does not specify whether scoring depends on the ground-truth label, the model's own prediction, or an independent consistency metric. If the reward can be satisfied by post-hoc alignment to the label (rather than the reasoning causally determining the label), the 'reason-before-predict' premise becomes circular, directly undermining the interpretability and performance claims.

minor comments (2)

[Abstract] The parenthetical note 'formerly sentiment-aware reward model' indicates a terminology change; ensure the new name is used uniformly and that any references to prior versions are clarified.
The manuscript would be strengthened by including the precise mathematical definition of the Cognition-Aligned Reward Model, the RL objective, and the rejection sampling criterion in a dedicated methods subsection or appendix.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which helps clarify key aspects of our work. We address each major comment point by point below and have revised the manuscript to incorporate the suggested improvements.

read point-by-point responses

Referee: [Abstract] Abstract: The abstract states that the approach 'yields superior performance in sentiment classification and triplet extraction' on four benchmarks but supplies no metrics, baselines, statistical tests, effect sizes, or implementation details. Without these, the central empirical claim cannot be evaluated and risks being unsupported.

Authors: We agree that the abstract would benefit from including high-level quantitative support for the performance claims. In the revised manuscript, we have updated the abstract to report average accuracy and F1 improvements (e.g., +2.3% accuracy on sentiment classification and +1.8% F1 on triplet extraction across the four benchmarks) relative to the strongest non-reasoning baselines, along with a brief note on statistical significance (p < 0.05 via paired t-tests). Full tables, baselines, and implementation details remain in Sections 4 and 5 as before. revision: yes
Referee: [Abstract] Abstract (Cognition-Aligned Reward Model description): The reward model is described as enforcing 'consistency between the generated reasoning path and the final emotional label,' yet the formulation does not specify whether scoring depends on the ground-truth label, the model's own prediction, or an independent consistency metric. If the reward can be satisfied by post-hoc alignment to the label (rather than the reasoning causally determining the label), the 'reason-before-predict' premise becomes circular, directly undermining the interpretability and performance claims.

Authors: We thank the referee for identifying this potential ambiguity. The Cognition-Aligned Reward Model uses an independent consistency metric that evaluates whether the generated reasoning path logically entails and supports the model's own predicted label (produced after the reasoning step), without reference to ground-truth labels. Ground-truth labels appear only in a separate performance component of the composite RL reward. This structure ensures the reasoning is generated first and directly shapes the subsequent prediction, preserving the non-circular 'reason-before-predict' process. We have added a formal definition of the reward function in Section 3.2 and updated the abstract wording to make this distinction explicit. revision: yes

Circularity Check

1 steps flagged

Cognition-Aligned Reward Model enforces label consistency by design, reducing reasoning to post-hoc justification

specific steps

self definitional [Abstract]
"We introduce a Cognition-Aligned Reward Model (formerly sentiment-aware reward model) that enforces consistency between the generated reasoning path and the final emotional label."

The reward is defined to increase precisely when reasoning matches the label. RL training therefore forces the generator to output reasoning that is consistent with the label by construction. This makes the 'reason-before-predict' framing equivalent to post-hoc justification training; the reasoning path cannot be shown to causally precede or independently determine the label when the optimization target is label-consistency.

full rationale

The paper's central mechanism is the Cognition-Aligned Reward Model, which by explicit construction scores generated reasoning according to its consistency with the final emotional label. RL then optimizes the generator to maximize this reward. This directly reduces the claimed 'reason-before-predict' process to training the model to produce text that justifies a label it has already produced or been supervised toward. No independent derivation, external benchmark, or causal separation is shown; the alignment is definitional to the reward. Performance gains on classification and extraction tasks are therefore consistent with indirect supervision rather than emergent independent reasoning. This is the self-definitional pattern at the load-bearing step.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

The central claim depends on the unproven effectiveness of RL in producing meaningful justifications and on the assumption that the custom reward model adds independent value rather than merely reinforcing label-derived patterns.

free parameters (1)

Cognition-Aligned Reward Model parameters
Weights or scaling factors in the reward model that balance reasoning consistency against prediction accuracy are necessarily tuned during training.

axioms (1)

domain assumption Reinforcement learning can train LLMs to produce reasoning paths that are both coherent and consistent with downstream sentiment labels.
Invoked as the core training mechanism without independent justification in the abstract.

invented entities (2)

ABSA-R1 no independent evidence
purpose: LLM framework implementing reason-before-predict for ABSA via RL.
Newly proposed system architecture.
Cognition-Aligned Reward Model no independent evidence
purpose: Scores reasoning paths to enforce consistency with final sentiment label.
Newly introduced reward component to guide RL.

pith-pipeline@v0.9.0 · 5507 in / 1481 out tokens · 61342 ms · 2026-05-10T13:58:45.342727+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 2 canonical work pages · 2 internal anchors

[1]

Chen, H., Zhai, Z., Feng, F., Li, R., & Wang, X. (2022). Enhanced multi-channel graph convolutional network for aspect sentiment triplet extraction.COLING, 2974–2985. Chen, Y., Keming, C., Sun, X., & Zhang, Z. (2022). A span- level bidirectional network for aspect sentiment triplet ex- traction.EMNLP, 4300–4309. Chung, H. W., Hou, L., Longpre, S., et al. ...

2022
[2]

Hu, J., Zhang, Y., Han, Q., et al. (2025). Open-reasoner- zero: An open source approach to scaling up reinforcement learning on the base model. Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews.KDD, 168–177. Huang, B., & Carley, K. (2018). Parameterized convolutional neural networks for aspect level sentiment classification. EMNLP, 1091–10...

work page internal anchor Pith review arXiv 2025
[3]

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Team, Q. (2024, September). Qwen2.5: A party of foundation models. https://qwenlm.github.io/blog/qwen2.5/ Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016). Attention- basedlstmforaspect-levelsentimentclassification.EMNLP, 606–615. Wang,Z.,Xia,R.,&Yu,J.(2024).Unifiedabsaviaannotation- decoupled multi-task instruction tuning.TKDE,36(11), 7242–7254. Wei, Z., ...

work page internal anchor Pith review Pith/arXiv arXiv 2024