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

• URL: http://arxiv.org/abs/2604.13398v1
• Date: Wed, 15 Apr 2026 01:55:40 GMT
• Title: From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning
• Abstract summary: ABSA-R1 is a large language model framework designed to mimic the reason-before-predict' cognitive process.<n>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.<n>We show that equipping models with this explicit reasoning capability not only enhances interpretability but also yields superior performance in sentiment classification and triplet extraction.
• Score: 22.540816588473557
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Cognitive Profiling of LRMs' Reasoning Traces Using Bloom's Taxonomy [51.08110837378928]
  We introduce a framework for automatic annotation of reasoning steps through the lens of Bloom's taxonomy.<n>We perform a large-scale analysis across models and datasets, revealing both similarities and differences in thinking patterns across models and tasks.<n>Our findings establish a fine-grained framework for analyzing thinking patterns in LRMs and provide actionable insights for enhancing reasoning quality.
  arXiv  Detail & Related papers  (2026-08-24T12:55:51Z)
• AffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related Scenes [59.134503227559634]
  Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding.<n>Models may predict correct answers while neglecting people-centric cues such as micro expressions and body language.<n>We propose Affect Omni, a framework for verifiable affective reasoning.
  arXiv  Detail & Related papers  (2026-08-24T11:25:04Z)
• Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models [56.88565331713778]
  reasoning-oriented training can make traces look more deliberative without amplifying the behaviors most tied to model correctness.<n>We quantify this mismatch with Behavioral Lift, a metric that measures how much correctness changes when a behavior is present versus absent in a model's reasoning trace.<n>We find evidence for an Amplification-Lift Gap, in which thinking models strongly amplify self-awareness, hypothesis testing, and uncertainty acknowledgment.
  arXiv  Detail & Related papers  (2026-08-13T20:37:59Z)
• Taming the Centaur(s) with LAPITHS: a framework for a theoretically grounded interpretation of AI performances [0.0]
  We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS)<n>We use it to show that several major claims advanced by models such as CENTAUR, proposed as an artificial Unified Model of Cognition, are not theoretically or empirically justified.
  arXiv  Detail & Related papers  (2026-04-30T14:29:50Z)
• ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation [4.265094703231012]
  We introduce textbfALIVE (emphAdrial Learning with Instructive Verbal Evaluation), a hands-free alignment framework.<n>By coupling adversarial learning with instructive verbal feedback, ALIVE enables models to internalize evaluative criteria directly from raw corpora.<n>With identical data and compute, ALIVE achieves markedly improved cross-domain generalization, and higher self-correction rates.
  arXiv  Detail & Related papers  (2026-02-05T09:20:23Z)
• Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks [10.06554565520216]
  We introduce a two-stage experimental pipeline that explicitly separates perception and reasoning.<n>We show that the perception capability is the dominant factor underlying the observed performance gap.<n>Our findings underscore the need for evaluation protocols that disentangle perception from reasoning.
  arXiv  Detail & Related papers  (2025-12-24T18:58:04Z)
• Learning What to Attend First: Modality-Importance-Guided Reasoning for Reliable Multimodal Emotion Understanding [50.014363382140324]
  Modality Importance (MI) is a simple yet effective mechanism for identifying the emotion-dominant modality.<n>MIGR reorganizes reasoning sequences so that explanations begin from the modality most critical to the target emotion.<n>Results show that MIGR substantially improves reasoning reliability, decreasing instances of correct predictions accompanied by emotionally inconsistent explanations.
  arXiv  Detail & Related papers  (2025-12-02T12:29:41Z)
• Cognitive Foundations for Reasoning and Their Manifestation in LLMs [63.12951576410617]
  Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning.<n>We synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, representations for organizing reasoning & knowledge, and transformation operations.<n>We develop test-time reasoning guidance that automatically scaffold successful structures, improving performance by up to 66.7% on complex problems.
  arXiv  Detail & Related papers  (2025-11-20T18:59:00Z)
• Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models [0.0]
  Reasoning Language Models (RLMs) have gained traction for their ability to perform complex, multi-step reasoning tasks.<n>While these capabilities promise improved reliability, their impact on robustness to social biases remains unclear.<n>We leverage the CLEAR-Bias benchmark to investigate the adversarial robustness of RLMs to bias elicitation.
  arXiv  Detail & Related papers  (2025-07-03T17:01:53Z)
• Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training [86.70255651945602]
  We introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE)<n>RICE aims to improve reasoning performance without additional training or complexs.<n> Empirical evaluations with leading MoE-based LRMs demonstrate noticeable and consistent improvements in reasoning accuracy, cognitive efficiency, and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-05-20T17:59:16Z)
• Application of Multiple Chain-of-Thought in Contrastive Reasoning for Implicit Sentiment Analysis [1.9472869221587836]
  Implicit sentiment analysis aims to uncover emotions that are subtly expressed, often obscured by ambiguity and figurative language.<n>We propose a novel Dual Reverse Chain Reasoning framework to enhance the performance of implicit sentiment analysis.<n>We also introduce a Triple Reverse Chain Reasoning framework to address the limitations of random hypotheses.
  arXiv  Detail & Related papers  (2025-03-10T10:10:50Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• Learning Causal Semantic Representation for Out-of-Distribution Prediction [125.38836464226092]
  We propose a Causal Semantic Generative model (CSG) based on a causal reasoning so that the two factors are modeled separately. We show that CSG can identify the semantic factor by fitting training data, and this semantic-identification guarantees the boundedness of OOD generalization error.
  arXiv  Detail & Related papers  (2020-11-03T13:16:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.