From Clicks to Preference: A Multi-stage Alignment Framework for Generative Query Suggestion in Conversational System

• URL: http://arxiv.org/abs/2508.15811v1
• Date: Fri, 15 Aug 2025 10:17:01 GMT
• Title: From Clicks to Preference: A Multi-stage Alignment Framework for Generative Query Suggestion in Conversational System
• Authors: Junhao Yin, Haolin Wang, Peng Bao, Ju Xu, Yongliang Wang,
• Abstract summary: We introduce a multi-stage framework designed for progressive alignment between the generation policy and user intent.<n>Our framework significantly outperforms baselines on both automatic and human evaluations.
• Score: 11.373145953200137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative query suggestion using large language models offers a powerful way to enhance conversational systems, but aligning outputs with nuanced user preferences remains a critical challenge. To address this, we introduce a multi-stage framework designed for progressive alignment between the generation policy and user intent. Our pipeline begins with prompt engineering as a cold-start strategy, followed by the Supervised Fine-Tuning stage, in which we introduce a distillation method on click logs to create a robust foundational model. To better model user preferences while capturing their inherent uncertainty, we develop a Gaussian Reward Model (GaRM) that represents user preferences as probability distributions rather than point estimates. Finally, we employ reinforcement learning to align the generation policy with these preferences, guided by a composite reward function that integrates GaRM with auxiliary heuristics to mitigate reward hacking. To maintain training stability, this process is enhanced by a novel out-of-distribution regularization method and a two-stage reward fusion technique. Extensive experiments demonstrate that our framework significantly outperforms baselines on both automatic and human evaluations and yields a 34\% relative increase in user engagement as measured by click-through rate in live A/B tests.

Related papers

• Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control [82.30868101940068]
  We propose a paradigm in which a model bootstraps its own performance without reliance on external data or teacher models.<n>Our theoretical analysis shows that RSIR acts as a data-driven implicit regularizer, smoothing the optimization landscape.<n>We show that even smaller models benefit, and weak models can generate effective training curricula for stronger ones.
  arXiv  Detail & Related papers  (2026-02-17T15:31:32Z)
• P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling [66.55381105691818]
  We propose P-GenRM, the first Personalized Generative Reward Model with test-time user-based scaling.<n>P-GenRM transforms preference signals into structured evaluation chains that derive adaptive personas and scoring rubrics.<n>It further clusters users into User Prototypes and introduces a dual-granularity scaling mechanism.
  arXiv  Detail & Related papers  (2026-02-12T16:07:22Z)
• Euphonium: Steering Video Flow Matching via Process Reward Gradient Guided Stochastic Dynamics [49.242224984144904]
  We propose Euphonium, a novel framework that steers generation via process reward gradient guided dynamics.<n>Our key insight is to formulate the sampling process as a theoretically principled algorithm that explicitly incorporates the gradient of a Process Reward Model.<n>We derive a distillation objective that internalizes the guidance signal into the flow network, eliminating inference-time dependency on the reward model.
  arXiv  Detail & Related papers  (2026-02-04T08:59:57Z)
• SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models [53.19726629537694]
  Post-training alignment of video generation models with human preferences is a critical goal.<n>Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise.<n>We propose SoliReward, a systematic framework for video RM training.
  arXiv  Detail & Related papers  (2025-12-17T14:28:23Z)
• Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model [8.011282322871958]
  Reranking, as the final stage of recommender systems, demands real-time inference, accuracy, and diversity.<n>We propose Consistent Graph-structured Generative Recommendation (Congrats), a novel generative reranking framework.<n>Congrats has been evaluated on a large-scale video-sharing app, Kuaishou, with over 300 million daily active users.
  arXiv  Detail & Related papers  (2025-10-11T09:21:01Z)
• STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning [54.28691219536054]
  We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities.<n>We develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping.<n>Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-26T08:47:58Z)
• Multi-Metric Preference Alignment for Generative Speech Restoration [15.696247605348383]
  We propose a multi-metric preference alignment strategy for generative models.<n>We observe consistent and significant performance gains across three diverse generative paradigms.<n>Our aligned models can serve as powerful ''data annotators'', generating high-quality pseudo-labels.
  arXiv  Detail & Related papers  (2025-08-24T07:05:10Z)
• Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations [22.48125906976824]
  We introduce the Cascaded Organized Bi-Represented generAtive retrieval framework, which integrates sparse semantic IDs and dense vectors through a cascading process.<n>Our method alternates between generating these representations by first generating sparse IDs, which serve as conditions to aid in the generation of dense vectors.<n>During inference, COBRA employs a coarse-to-fine strategy, starting with sparse ID generation and refining them into dense vectors via the generative model.
  arXiv  Detail & Related papers  (2025-03-04T10:00:05Z)
• OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment [9.99840965933561]
  We propose OneRec, which replaces the cascaded learning framework with a unified generative model.<n>OneRec includes: 1) an encoder-decoder structure, which encodes the user's historical behavior sequences and gradually decodes the videos that the user may be interested in.
  arXiv  Detail & Related papers  (2025-02-26T09:25:10Z)
• LIRE: listwise reward enhancement for preference alignment [27.50204023448716]
  We propose a gradient-based reward optimization approach that incorporates the offline rewards of multiple responses into a streamlined listwise framework. LIRE is straightforward to implement, requiring minimal parameter tuning, and seamlessly aligns with the pairwise paradigm. Our experiments demonstrate that LIRE consistently outperforms existing methods across several benchmarks on dialogue and summarization tasks.
  arXiv  Detail & Related papers  (2024-05-22T10:21:50Z)
• Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems [5.46875780473223]
  We propose a novel automatic sampling framework for sequential recommendation, named AutoSAM, to non-uniformly treat historical behaviors.<n>We conduct extensive experiments on benchmark recommendation models and four real-world datasets.
  arXiv  Detail & Related papers  (2023-11-01T09:25:21Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)

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.