ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2311.09358
  4. Cited By
Empirical evaluation of Uncertainty Quantification in
  Retrieval-Augmented Language Models for Science

Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science

15 November 2023
S. Wagle
Sai Munikoti
Anurag Acharya
Sara Smith
Sameera Horawalavithana
ArXivPDFHTML

Papers citing "Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science"

4 / 4 papers shown
Title
Benchmarking LLMs via Uncertainty Quantification
Benchmarking LLMs via Uncertainty Quantification
Fanghua Ye
Mingming Yang
Jianhui Pang
Longyue Wang
Derek F. Wong
Emine Yilmaz
Shuming Shi
Zhaopeng Tu
ELM
25
47
0
23 Jan 2024
Uncertainty Quantification with Pre-trained Language Models: A
  Large-Scale Empirical Analysis
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Yuxin Xiao
Paul Pu Liang
Umang Bhatt
Willie Neiswanger
Ruslan Salakhutdinov
Louis-Philippe Morency
175
87
0
10 Oct 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,683
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,156
0
06 Jun 2015
1