Buchumschlag

Mind the Gap: How Elicitation Protocols Shape the Stated-Revealed Preference Gap in Language Models

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Mahajan, Pranav, Kendiukhov, Ihor, Hussain, Syed, Nottingham, Lydia
Format:	Preprint
Veröffentlicht:	2026
Schlagworte:	Artificial Intelligence Emerging Technologies
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocol
von: Jobs, Niklas, et al.
Veröffentlicht: (2025)

Circuit Partitioning Using Large Language Models for Quantum Compilation and Simulations
von: Sinha, Pranav, et al.
Veröffentlicht: (2025)

Beyond the Data Mesh Illusion: Designing Modern AI-augmented Lakehouses to Bridge the Gap Between Theory and Practice
von: Angélil, Oliver, et al.
Veröffentlicht: (2026)

An Onto-Relational-Sophic Framework for Governing Synthetic Minds
von: Ning, Huansheng, et al.
Veröffentlicht: (2026)

AI/ML in 3GPP 5G Advanced -- Services and Architecture
von: Taksande, Pradnya, et al.
Veröffentlicht: (2025)

mcp-proto-okn: Natural-language access to open scientific knowledge graphs through the Model Context Protocol
von: Rose, Peter W., et al.
Veröffentlicht: (2026)

Artificial General Intelligence Forecasting and Scenario Analysis: State of the Field, Methodological Gaps, and Strategic Implications
von: Sarma, Gopal P., et al.
Veröffentlicht: (2026)

Rethinking Agentic Reinforcement Learning In Large Language Models
von: Cui, Fangming, et al.
Veröffentlicht: (2026)

Managing Escalation in Off-the-Shelf Large Language Models
von: Elbaum, Sebastian, et al.
Veröffentlicht: (2025)

A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction
von: Liu, Chengjie, et al.
Veröffentlicht: (2025)

Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)
von: Machot, Fadi Al, et al.
Veröffentlicht: (2024)

Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles
von: Lu, Qiujing, et al.
Veröffentlicht: (2024)

VR-GPT: Visual Language Model for Intelligent Virtual Reality Applications
von: Konenkov, Mikhail, et al.
Veröffentlicht: (2024)

MindCraft: Revolutionizing Education through AI-Powered Personalized Learning and Mentorship for Rural India
von: Bardia, Arihant, et al.
Veröffentlicht: (2025)

The Maximum Coverage Model and Recommendation System for UAV Vertiports Location Planning
von: Hua, Chunliang, et al.
Veröffentlicht: (2025)

SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models
von: Zhou, Zhiyuan, et al.
Veröffentlicht: (2024)

Enhancing Software Quality Assurance with an Adaptive Differential Evolution based Quantum Variational Autoencoder-Transformer Model
von: Barma, Seshu Babu, et al.
Veröffentlicht: (2025)

Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment
von: Adorni, Giorgia, et al.
Veröffentlicht: (2024)

ETDI: Mitigating Tool Squatting and Rug Pull Attacks in Model Context Protocol (MCP) by using OAuth-Enhanced Tool Definitions and Policy-Based Access Control
von: Bhatt, Manish, et al.
Veröffentlicht: (2025)

Beyond the model: Key differentiators in large language models and multi-agent services
von: Goyal, Muskaan, et al.
Veröffentlicht: (2025)

Artificial Agency and Large Language Models
von: van Lier, Maud, et al.
Veröffentlicht: (2024)

Hierarchical Molecular Language Models (HMLMs)
von: Hays, Hasi, et al.
Veröffentlicht: (2025)

Systematic Evaluation of Single-Cell Foundation Model Interpretability Reveals Attention Captures Co-Expression Rather Than Unique Regulatory Signal
von: Kendiukhov, Ihor
Veröffentlicht: (2026)

Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliance
von: Seth, Pratinav, et al.
Veröffentlicht: (2025)

Are Large Language Models Reliable Argument Quality Annotators?
von: Mirzakhmedova, Nailia, et al.
Veröffentlicht: (2024)

Can Large Language Models Act as Symbolic Reasoners?
von: Sullivan, Rob, et al.
Veröffentlicht: (2024)

Autonomous Traffic Signal Optimization Using Digital Twin and Agentic AI for Real-Time Decision-Making
von: Jan, Salman, et al.
Veröffentlicht: (2026)

Automatic Qiskit Code Refactoring Using Large Language Models
von: Suárez, José Manuel, et al.
Veröffentlicht: (2025)

Iterative Prompting with Persuasion Skills in Jailbreaking Large Language Models
von: Ke, Shih-Wen, et al.
Veröffentlicht: (2025)

CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis
von: Vijayaraghavan, Prashanth, et al.
Veröffentlicht: (2024)

SpecPylot: Python Specification Generation using Large Language Models
von: Ayon, Ragib Shahariar, et al.
Veröffentlicht: (2026)

Predictive Coding and Information Bottleneck for Hallucination Detection in Large Language Models
von: Bhatt, Manish
Veröffentlicht: (2026)

Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing
von: da Silva, Luis Miguel Vieira, et al.
Veröffentlicht: (2025)

Measuring the State of Open Science in Transportation Using Large Language Models
von: Ji, Junyi, et al.
Veröffentlicht: (2026)

A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Health
von: Marrapese, Alexander, et al.
Veröffentlicht: (2024)

LLM4DS: Evaluating Large Language Models for Data Science Code Generation
von: Nascimento, Nathalia, et al.
Veröffentlicht: (2024)

Bounded Minds, Generative Machines: Envisioning Conversational AI that Works with Human Heuristics and Reduces Bias Risk
von: Liu, Jiqun
Veröffentlicht: (2026)

MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings
von: Purwar, Anupam, et al.
Veröffentlicht: (2026)

Traffic and weather driven hybrid digital twin for bridge monitoring
von: Balijepalli, Phani Raja Bharath, et al.
Veröffentlicht: (2026)

Graph-Enhanced Deep Reinforcement Learning for Multi-Objective Unrelated Parallel Machine Scheduling
von: Soykan, Bulent, et al.
Veröffentlicht: (2026)