Cover Image

Robustness is Important: Limitations of LLMs for Data Fitting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Hejia, Yang, Mochen, Adomavicius, Gediminas
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Applications
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LLMs for Supply Chain Management
by: Wang, Haojie, et al.
Published: (2025)

Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
by: Li, Siqi, et al.
Published: (2026)

Latent Space Data Fusion Outperforms Early Fusion in Multimodal Mental Health Digital Phenotyping Data
by: Barkat, Youcef, et al.
Published: (2025)

Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints
by: Raihan, Ahmed Shoyeb, et al.
Published: (2025)

Generating Synthetic Relational Tabular Data via Structural Causal Models
by: Hoppe, Frederik, et al.
Published: (2025)

FreDF: Learning to Forecast in the Frequency Domain
by: Wang, Hao, et al.
Published: (2024)

Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data
by: Hu, Yuxiao, et al.
Published: (2023)

Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning
by: Feldman, Aaron O., et al.
Published: (2025)

Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data
by: Mishra, Anurag, et al.
Published: (2024)

Enhancing Data Efficiency and Feature Identification for Lithium-Ion Battery Lifespan Prediction by Deciphering Interpretation of Temporal Patterns and Cyclic Variability Using Attention-Based Models
by: Lee, Jaewook, et al.
Published: (2023)

Binary Gaussian Copula Synthesis: A Novel Data Augmentation Technique to Advance ML-based Clinical Decision Support Systems for Early Prediction of Dialysis Among CKD Patients
by: Khosravi, Hamed, et al.
Published: (2024)

Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis
by: Lares, Oscar, et al.
Published: (2024)

On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction
by: Zhuang, Jinfeng, et al.
Published: (2025)

Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy
by: Zhou, Ya, et al.
Published: (2025)

Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series
by: Zhang, Weijia, et al.
Published: (2024)

Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection
by: Zhou, Ya, et al.
Published: (2025)

Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking
by: Xu, Yang, et al.
Published: (2026)

Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning
by: Yang, Yu, et al.
Published: (2026)

Curious Causality-Seeking Agents Learn Meta Causal World
by: Zhao, Zhiyu, et al.
Published: (2025)

Lightweight Adaptation for LLM-based Technical Service Agent: Latent Logic Augmentation and Robust Noise Reduction
by: Yu, Yi, et al.
Published: (2026)

Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
by: Zhou, Ya, et al.
Published: (2026)

Knowledge without Wisdom: Measuring Misalignment between LLMs and Intended Impact
by: Hardy, Michael, et al.
Published: (2026)

Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
by: Brown, Zachary C., et al.
Published: (2025)

Efficient Prediction of Pass@k Scaling in Large Language Models
by: Kazdan, Joshua, et al.
Published: (2025)

Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling
by: Merizzi, Fabio, et al.
Published: (2025)

Multi-task Neural Diffusion Processes
by: Rawson, Joseph, et al.
Published: (2025)

FactsR: A Safer Method for Producing High Quality Healthcare Documentation
by: Hansen, Victor Petrén Bach, et al.
Published: (2025)

Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach
by: Gond, Bishwajit Prasad
Published: (2025)

E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing
by: Sadhuka, Shuvom, et al.
Published: (2025)

A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models
by: Olivares, Kin G., et al.
Published: (2025)

PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
by: Schlender, Thalea, et al.
Published: (2025)

Synergizing chemical and AI communities for advancing laboratories of the future
by: Oh, Saejin, et al.
Published: (2025)

General Uncertainty Estimation with Delta Variances
by: Schmitt, Simon, et al.
Published: (2025)

Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland
by: Onibonoje, Oluwadurotimi, et al.
Published: (2025)

Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems
by: Tauberschmidt, Jan, et al.
Published: (2025)

ACT-Tensor: Tensor Completion Framework for Financial Dataset Imputation
by: Mo, Junyi, et al.
Published: (2025)

Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting
by: Ehsani, Sina, et al.
Published: (2024)

CVTN: Cross Variable and Temporal Integration for Time Series Forecasting
by: Zhou, Han, et al.
Published: (2024)

Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach
by: Ferstad, Johannes O., et al.
Published: (2024)

Causal Fairness for Survival Analysis
by: Plecko, Drago
Published: (2026)