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Found 8 papers, 3 papers with code
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Motivated by this gap, we introduce a novel UQ method, sampling with perturbation for UQ (SPUQ), designed to tackle both aleatoric and epistemic uncertainties.
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
Uncertainty estimation is a crucial aspect of deploying dependable deep learning models in safety-critical systems.
PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models
Crafting an ideal prompt for Large Language Models (LLMs) is a challenging task that demands significant resources and expert human input.
Customizing Language Model Responses with Contrastive In-Context Learning
However, it can be challenging to align LLMs with our intent, particularly when we want to generate content that is preferable over others or when we want the LLM to respond in a certain style or tone that is hard to describe.
DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models
Evaluating the quality and variability of text generated by Large Language Models (LLMs) poses a significant, yet unresolved research challenge.
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models
The performance of optical character recognition (OCR) heavily relies on document image quality, which is crucial for automatic document processing and document intelligence.
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency
Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs).
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification
Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
