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Found 5 papers, 2 papers with code
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output.
Efficient Conformal Prediction under Data Heterogeneity
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions.
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized.
Scalable Batch Acquisition for Deep Bayesian Active Learning
In deep active learning, it is especially important to choose multiple examples to markup at each step to work efficiently, especially on large datasets.
