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Found 23 papers, 9 papers with code
Uncertainty Quantification for In-Context Learning of Large Language Models
Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.
Open-ended Commonsense Reasoning with Unrestricted Answer Scope
In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision.
Large Language Models Can Be Good Privacy Protection Learners
To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.
Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains
This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features.
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples.
Ranked #6 on
Open Information Extraction
on OIE2016
Adaptation Speed Analysis for Fairness-aware Causal Models
In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
Multidimensional Uncertainty Quantification for Deep Neural Networks
In the first part of this thesis, we develop a general learning framework to quantify multiple types of uncertainties caused by different root causes, such as vacuity (i. e., uncertainty due to a lack of evidence) and dissonance (i. e., uncertainty due to conflicting evidence), for graph neural networks.
Dynamic Prompting: A Unified Framework for Prompt Tuning
Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks.
Knowledge-enhanced Neural Machine Reasoning: A Review
Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications.
A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning
We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty.
Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
SEED: Sound Event Early Detection via Evidential Uncertainty
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes.
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation
Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages.
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
In this work, we propose RETRIEVE, a coreset selection framework for efficient and robust semi-supervised learning.
Multidimensional Uncertainty-Aware Evidential Neural Networks
By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem.
Generative Adversarial Network
Multi-class Classification
+3
Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning
We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data.
Uncertainty Aware Semi-Supervised Learning on Graph Data
To clarify the reasons behind the results, we provided the theoretical proof that explains the relationships between different types of uncertainties considered in this work.
How Out-of-Distribution Data Hurts Semi-Supervised Learning
This work addresses the following question: How do out-of-distribution (OOD) data adversely affect semi-supervised learning algorithms?
Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
However, an ENN is trained as a black box without explicitly considering different types of inherent data uncertainty, such as vacuity (uncertainty due to a lack of evidence) or dissonance (uncertainty due to conflicting evidence).
Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data
Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions.
Evidence-Aware Entropy Decomposition For Active Deep Learning
We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data.
Uncertainty-Aware Prediction for Graph Neural Networks
In this work, we propose a Bayesian deep learning framework reflecting various types of uncertainties for classification predictions by leveraging the powerful modeling and learning capabilities of GNNs.
