model calibration

225 papers

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Co-occurring keywords

uncertainty quantification (1365) uncertainty estimation (639) large language model (12755) confidence calibration (173) temperature scaling (67) expected calibration error (44) language model (4573) neural network (6616) knowledge distillation (3680) label smoothing (107)

Papers

Bayesian Calibration of Win Rate Estimation with LLM Evaluators EMNLP 2024

Extreme Miscalibration and the Illusion of Adversarial Robustness ACL 2024

Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification ACL 2024

Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models ACL 2024

Pearls from Pebbles: Improved Confidence Functions for Auto-labeling NIPS 2024

Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning NIPS 2024

Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction ACL 2024

Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information NIPS 2024

MaxEnt Loss: Calibrating Graph Neural Networks under Out-of-Distribution Shift (Student Abstract) AAAI 2024

CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration EACL 2024

Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? EACL 2024

On Calibration of Speech Classification Models: Insights from Energy-Based Model Investigations INTERSPEECH 2024

Large Language Models Must Be Taught to Know What They Don’t Know NIPS 2024

FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation EMNLP 2024

Don’t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration ACL 2024

SimCalib: Graph Neural Network Calibration Based on Similarity between Nodes AAAI 2024

Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space AAAI 2024

Bayesian Exploration of Pre-trained Models for Low-shot Image Classification CVPR 2024

Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Vision Foundation Models NIPS 2024

Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model EMNLP 2024

Improving GNN Calibration with Discriminative Ability: Insights and Strategies AAAI 2024

MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift AAAI 2024

Fill In The Gaps: Model Calibration and Generalization with Synthetic Data EMNLP 2024

Calibrating Language Models with Adaptive Temperature Scaling EMNLP 2024

Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing COLING 2024