From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation

• URL: http://arxiv.org/abs/2608.11493v1
• Date: Tue, 11 Aug 2026 23:06:39 GMT
• Title: From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation
• Abstract summary: This study identifies a critical failure mode termed bidirectional rationalization.<n>We develop and apply a sequential behavioral alignment framework pairing fine-tuning with preference optimization.<n>This approach achieves a 32.19% lift in Macro-F1 score over the zero-shot baseline.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale. While Large Language Models (LLMs) offer a promising alternative by predicting user engagement directly from raw text logs, empirical analysis in this study identifies a critical failure mode termed bidirectional rationalization. In a zero-shot setting, LLMs are found to convincingly argue for both positive and negative user engagement outcomes on the exact same item with identical evidence, highlighting the unreliability of off-the-shelf LLMs in predicting user engagement. To resolve this, we develop and apply a sequential behavioral alignment framework pairing fine-tuning with preference optimization over paired correct and counterfactual rationales. Evaluated on real-world homepage interaction logs, this aligned reasoning approach achieves a 32.19\% lift in Macro-F1 score over the zero-shot baseline and matches the production feature-engineered baseline. The results demonstrate that behavioral alignment mitigates bidirectional rationalization while delivering human-interpretable reasoning traces without manual pipeline overhead.

Related papers

• Training Large Language Models for Self-Explanation Faithfulness [10.933553638236036]
  We propose a Reinforcement Learning (RL) method to directly optimize the faithfulness of self-explanations.<n>We investigate whether models can be trained to accurately detect factors that affect their decisions.<n>We show that models can be trained to implicitly identify influential factors and disclose them.
  arXiv  Detail & Related papers  (2026-07-23T09:20:38Z)
• To Reason or to Fabricate: Reasoning Without Shortcuts via Hint-Anchored Pairwise Aggregation [20.92880098291496]
  HIPPO is a novel RL framework that integrates hint-injected aggregation with a tailored pairwise reward model.<n>It provides highly discriminable preference signals, enabling a lightweight judge model to reliably distinguish genuine reasoning deduction.
  arXiv  Detail & Related papers  (2026-06-28T16:21:04Z)
• Preference-Aware Rubric Learning for Personalized Evaluation [59.539429430690156]
  Existing evaluation methods fail to capture user-specific preferences embedded in long-term interaction histories.<n>We propose Personalized Evaluation as Learning, a paradigm that formulates personalized evaluation as a learning problem rather than a static judgment.<n>Experiments show that PARL consistently induces high-fidelity rubrics that reliably identify user-aligned responses and generalize across users.
  arXiv  Detail & Related papers  (2026-05-29T17:00:55Z)
• DISA: Offline Importance Sampling for Distribution-Matching LLM-RL [56.9445657766829]
  We introduce DISA, which moves this calibration problem outside the RL loop.<n> DISA draws proposal trajectories offline, estimates the partition function via importance sampling, and freezes the resulting partition-function estimate.<n>On two open-weight backbones across six math and three code benchmarks, DISA matches or exceeds the online-coupled distribution-matching baseline Flow.
  arXiv  Detail & Related papers  (2026-05-17T07:14:44Z)
• BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation [11.37493959290663]
  We introduce BERT-as-a-Judge, an encoder-driven approach for assessing answer correctness in reference-based generative settings.<n>We show that it consistently outperforms the lexical baseline while matching the performance of much larger LLM judges.
  arXiv  Detail & Related papers  (2026-04-10T17:08:40Z)
• REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge [83.2858110368572]
  We propose textbfREAL (underlineREgression-underlineAware Reinforcement underlineLThought), a principled RL framework designed to optimize regression rewards.<n>We show that REAL consistently outperforms both regression-aware SFT baselines and standard RL methods.
  arXiv  Detail & Related papers  (2026-03-17T21:19:08Z)
• Verifiable Reasoning for LLM-based Generative Recommendation [106.7765000777685]
  Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation.<n>We propose a novel textbftextitreason-verify-recommend paradigm, which interleaves reasoning with verification to provide reliable feedback.<n>Experiments on four real-world datasets demonstrate that VRec substantially enhances recommendation effectiveness and scalability without compromising efficiency.
  arXiv  Detail & Related papers  (2026-03-08T16:55:45Z)
• Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning [59.76691952347156]
  Reinforcement learning (RL) has emerged as a powerful framework for improving the reasoning capabilities of large language models (LLMs)<n>Most existing RL approaches rely on sparse outcome rewards, which fail to credit correct intermediate steps in partially successful solutions.<n>We propose Verifiable Prefix Policy Optimization (VPPO), which uses PRMs only to localize the first error during RL.
  arXiv  Detail & Related papers  (2026-01-26T21:38:20Z)
• Are LLMs Vulnerable to Preference-Undermining Attacks (PUA)? A Factorial Analysis Methodology for Diagnosing the Trade-off between Preference Alignment and Real-World Validity [45.92643973404507]
  We investigate whether aligned models are vulnerable to Preference-Undermining Attacks (PUA), a class of manipulative prompting strategies.<n>Surprisingly, more advanced models are sometimes more susceptible to manipulative prompts.
  arXiv  Detail & Related papers  (2026-01-10T15:16:23Z)
• RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning [64.46921169261852]
  RAG-Zeval is a novel end-to-end framework that formulates faithfulness and correctness evaluation as a rule-guided reasoning task.<n>Our approach trains evaluators with reinforcement learning, facilitating compact models to generate comprehensive and sound assessments.<n>Experiments demonstrate RAG-Zeval's superior performance, achieving the strongest correlation with human judgments.
  arXiv  Detail & Related papers  (2025-05-28T14:55:33Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• REAL: Response Embedding-based Alignment for LLMs [1.9513983244114355]
  We propose a strategy for constructing a high-quality training dataset that focuses on acquiring the less ambiguous preference pairs.<n>Experiments show that choosing dissimilar response pairs enhances the direct alignment of LLMs.<n>Findings suggest that focusing on distinct pairs can reduce the label error and improve LLM alignment efficiency.
  arXiv  Detail & Related papers  (2024-09-17T22:40:54Z)
• ROPO: Robust Preference Optimization for Large Language Models [59.10763211091664]
  We propose an iterative alignment approach that integrates noise-tolerance and filtering of noisy samples without the aid of external models. Experiments on three widely-used datasets with Mistral-7B and Llama-2-7B demonstrate that ROPO significantly outperforms existing preference alignment methods.
  arXiv  Detail & Related papers  (2024-04-05T13:58:51Z)
• REFINER: Reasoning Feedback on Intermediate Representations [47.36251998678097]
  We introduce REFINER, a framework for finetuning language models to generate intermediate inferences. REFINER works by interacting with a critic model that provides automated feedback on the reasoning. Empirical evaluations show significant improvements over baseline LMs of comparable scale.
  arXiv  Detail & Related papers  (2023-04-04T15:57:28Z)

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.