Copertina

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Huang, Chengpiao, Wu, Yuhang, Wang, Kaizheng
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Methodology Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Model Assessment and Selection under Temporal Distribution Shift
di: Han, Elise, et al.
Pubblicazione: (2024)

Distribution-Free Predictive Inference under Unknown Temporal Drift
di: Han, Elise, et al.
Pubblicazione: (2024)

A Stability Principle for Learning under Non-Stationarity
di: Huang, Chengpiao, et al.
Pubblicazione: (2023)

Model-Free Assessment of Simulator Fidelity via Quantile Curves
di: Iyengar, Garud, et al.
Pubblicazione: (2025)

SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation
di: Fan, Grace Jiarui, et al.
Pubblicazione: (2026)

Credal Ensemble Distillation for Uncertainty Quantification
di: Wang, Kaizheng, et al.
Pubblicazione: (2025)

Adaptive Uncertainty Quantification for Generative AI
di: Kim, Jungeum, et al.
Pubblicazione: (2024)

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
di: Nagler, Thomas, et al.
Pubblicazione: (2025)

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
di: Li, Peiwen, et al.
Pubblicazione: (2024)

Discovering and Reasoning of Causality in the Hidden World with Large Language Models
di: Liu, Chenxi, et al.
Pubblicazione: (2024)

Efficient Causal Graph Discovery Using Large Language Models
di: Jiralerspong, Thomas, et al.
Pubblicazione: (2024)

A Similarity Measure Between Functions with Applications to Statistical Learning and Optimization
di: Huang, Chengpiao, et al.
Pubblicazione: (2025)

Can Large Language Models Help Experimental Design for Causal Discovery?
di: Li, Junyi, et al.
Pubblicazione: (2025)

Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning
di: Ren, Yutian, et al.
Pubblicazione: (2024)

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
di: Takayama, Masayuki, et al.
Pubblicazione: (2024)

HOLOGRAPH: Active Causal Discovery via Sheaf-Theoretic Alignment of Large Language Model Priors
di: Kim, Hyunjun
Pubblicazione: (2025)

CausalGuard: Conformal Inference under Graph Uncertainty
di: Singh, Vikash, et al.
Pubblicazione: (2026)

Estimating Item Difficulty with Large Language Models as Experts
di: Kolesnikova, Diana, et al.
Pubblicazione: (2026)

Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length
di: Xu, Zhiyu, et al.
Pubblicazione: (2025)

Provable Multi-Party Reinforcement Learning with Diverse Human Feedback
di: Zhong, Huiying, et al.
Pubblicazione: (2024)

Evaluating Interventional Reasoning Capabilities of Large Language Models
di: Kasetty, Tejas, et al.
Pubblicazione: (2024)

Generally-Occurring Model Change for Robust Counterfactual Explanations
di: Xu, Ao, et al.
Pubblicazione: (2024)

Language Agents Meet Causality -- Bridging LLMs and Causal World Models
di: Gkountouras, John, et al.
Pubblicazione: (2024)

A Critical Perspective on Finite Sample Conformal Prediction Theory in Medical Applications
di: Kladny, Klaus-Rudolf, et al.
Pubblicazione: (2025)

Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
di: Furman, Oleksii, et al.
Pubblicazione: (2024)

Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)
di: Drapier, Nicolas, et al.
Pubblicazione: (2024)

TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
di: González, Javier, et al.
Pubblicazione: (2023)

Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning
di: Cai, Hengrui, et al.
Pubblicazione: (2023)

Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
di: Komanduri, Aneesh, et al.
Pubblicazione: (2024)

Partially Observed Structural Causal Models
di: Orujlu, Turan, et al.
Pubblicazione: (2026)

Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models
di: Guo, Xichen, et al.
Pubblicazione: (2024)

Statistical Tests for Replacing Human Decision Makers with Algorithms
di: Feng, Kai, et al.
Pubblicazione: (2023)

Is Elo Rating Reliable? A Study Under Model Misspecification
di: Tang, Shange, et al.
Pubblicazione: (2025)

A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation
di: Sarmah, Bhaskarjit, et al.
Pubblicazione: (2024)

Time Series Domain Adaptation via Latent Invariant Causal Mechanism
di: Cai, Ruichu, et al.
Pubblicazione: (2025)

Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
di: Wang, Kaizheng, et al.
Pubblicazione: (2024)

Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing
di: Sun, Rongyi, et al.
Pubblicazione: (2026)

Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
di: Jantre, Sanket, et al.
Pubblicazione: (2025)

Towards a holistic understanding of Selection Bias for Causal Effect Identification
di: Qiu, Yiwen, et al.
Pubblicazione: (2026)

Causal Unit Selection using Tractable Arithmetic Circuits
di: Huang, Haiying, et al.
Pubblicazione: (2024)