Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documents
- URL: http://arxiv.org/abs/2604.18122v1
- Date: Mon, 20 Apr 2026 11:42:51 GMT
- Title: Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documents
- Abstract summary: We present Decisive, an interactive decision-making framework that combines document-grounded reasoning with Bayesian preference inference.<n>Our approach grounds decisions in an objective option-scoring matrix extracted from source documents, while actively learning a user's latent preference vector through targeted elicitation.<n>Users answer pairwise tradeoff questions adaptively selected to maximize information gain over the final decision.
- Score: 3.7993411952027163
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Decision-making is a cognitively intensive task that requires synthesizing relevant information from multiple unstructured sources, weighing competing factors, and incorporating subjective user preferences. Existing methods, including large language models and traditional decision-support systems, fall short: they often overwhelm users with information or fail to capture nuanced preferences accurately. We present Decisive, an interactive decision-making framework that combines document-grounded reasoning with Bayesian preference inference. Our approach grounds decisions in an objective option-scoring matrix extracted from source documents, while actively learning a user's latent preference vector through targeted elicitation. Users answer pairwise tradeoff questions adaptively selected to maximize information gain over the final decision. This process converges efficiently, minimizing user effort while ensuring recommendations remain transparent and personalized. Through extensive experiments, we demonstrate that our approach significantly outperforms both general-purpose LLMs and existing decision-making frameworks achieving up to 20% improvement in decision accuracy over strong baselines across domains.
Related papers
- CARA: Cognitive Adaptive Recommendation Agent [3.268212305496309]
We propose CARA, a framework that formulates recommendation as a structured decision-making process.<n>The core intuition of CARA is that user decisions are jointly shaped by two complementary mechanisms: intuitive affective preference and deliberate rational evaluation.<n>CARA achieves the best performance on most evaluation metrics, with relative improvements of up to 10.15% over the baseline.
arXiv Detail & Related papers (2026-08-02T19:57:59Z) - Learning Transferable Latent User Preferences for Human-Aligned Decision Making [4.1789291746171715]
Human-aligned decision making requires accounting for both explicitly stated goals and latent user preferences.<n>We introduce CLIPR (Conversational Learning for Inferring Preferences and Reasoning), a framework that learns actionable, transferable natural language rules.<n> Evaluations on three datasets and a user study show that CLIPR consistently outperforms existing methods in improving alignment and reducing inference costs.
arXiv Detail & Related papers (2026-05-12T19:32:10Z) - RecThinker: An Agentic Framework for Tool-Augmented Reasoning in Recommendation [63.74915464611075]
RecThinker is an agentic framework for tool-augmented reasoning in recommendation.<n>We develop a suite of specialized tools for RecThinker, enabling the model to acquire user item-side and collaborative information.
arXiv Detail & Related papers (2026-03-10T16:07:17Z) - Tree of Preferences for Diversified Recommendation [54.183647833064136]
We study diversified recommendation from a data-bias perspective.<n>Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach.
arXiv Detail & Related papers (2025-12-24T04:13:17Z) - Implementing Rational Choice Functions with LLMs and Measuring their Alignment with User Preferences [15.72977233489024]
We put forward design principles for using large language models to implement rational choice functions.<n>We demonstrate the applicability of our approach through an empirical study in a practical application of an IUI in the automotive domain.
arXiv Detail & Related papers (2025-04-22T09:08:21Z) - Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection [6.33238947511697]
This paper introduces Multi-Output LOcal Narrative Explanation (MOLONE)<n>MOLONE is a novel comparative explanation method designed to enhance preference selection in human-in-the-loop Preference Bayesian optimization (PBO)<n>We show that MOLONE significantly accelerates convergence in human-in-the-loop scenarios by facilitating more efficient identification of preferred options.
arXiv Detail & Related papers (2025-04-01T15:23:54Z) - Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making [3.3044728148521623]
We propose a participatory framework that reframes decision-making as a multi-stakeholder optimization problem.<n>Our modular, model-agnostic framework employs k-fold cross-validation to fine-tune user-provided prediction models.<n>A synthetic scoring mechanism aggregates user-defined preferences across multiple metrics to rank strategies.
arXiv Detail & Related papers (2025-02-12T16:27:40Z) - Reason4Rec: Large Language Models for Recommendation with Deliberative User Preference Alignment [69.11529841118671]
We propose a new Deliberative Recommendation task, which incorporates explicit reasoning about user preferences as an additional alignment goal.<n>We then introduce the Reasoning-powered Recommender framework for deliberative user preference alignment.
arXiv Detail & Related papers (2025-02-04T07:17:54Z) - Comparison-based Active Preference Learning for Multi-dimensional Personalization [7.349038301460469]
Large language models (LLMs) have shown remarkable success, but aligning them with human preferences remains a core challenge.<n>Recent studies have explored multi-dimensional personalization, which aims to enable models to generate responses personalized to explicit preferences.<n>We propose Active Multi-dimensional Preference Learning (AMPLe), designed to capture implicit user preferences from interactively collected comparative feedback.
arXiv Detail & Related papers (2024-11-01T11:49:33Z) - An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
We first construct a max-margin optimization-based model to model potentially non-monotonic preferences.
We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration.
Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
arXiv Detail & Related papers (2024-09-04T14:36:20Z) - Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap.
We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert.
Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
arXiv Detail & Related papers (2021-01-24T05:40:29Z) - Inverse Active Sensing: Modeling and Understanding Timely
Decision-Making [111.07204912245841]
We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure.
We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
arXiv Detail & Related papers (2020-06-25T02:30:45Z)
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.