Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation

arXiv:2412.07255 · cs.CL, cs.AI · Submitted 2024-12-10 · Read on arXiv

cs.CL, cs.AI

Submitted: 2024-12-10

Updated: 2026-09-16

Comments: 8 pages, 5 figures. Accepted at IJCNN 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Ablation — 7× in this paper · explained in 1 episode
• Uncertainty Quantification — 5× in this paper · explained in 8 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Natural Language Processing — 2× in this paper
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Semantic Entropy — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Aleatoric Uncertainty — 1× in this paper · explained in 4 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Area Under the Receiver Operating Characteristic — 1× in this paper
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Length Normalizaiton Predictive Entropy — 1× in this paper
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Shift Attention Towards Relevance — 1× in this paper
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
• LM vs LM: Detecting Factual Errors via Cross Examination
• A Survey on In-context Learning
• Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models
• Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience
• Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
• Mistral 7B
• TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
• Language Models (Mostly) Know What They Know
• Scaling Laws for Neural Language Models
• Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
• Teaching Models to Express Their Uncertainty in Words
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
• Uncertainty Estimation in Autoregressive Structured Prediction
• Correcting Length Bias in Neural Machine Translation
• Are NLP Models really able to Solve Simple Math Word Problems?
• The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
• Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation

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