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Uncertainty-Aware Attention Heads: Efficient Unsupervised Uncertainty Quantification for LLMs

Uncertainty-Aware Attention Heads: Efficient Unsupervised Uncertainty Quantification for LLMs

26 May 2025
Artem Vazhentsev
Lyudmila Rvanova
Gleb Kuzmin
Ekaterina Fadeeva
Ivan Lazichny
Alexander Panchenko
Maxim Panov
Timothy Baldwin
Mrinmaya Sachan
Preslav Nakov
Artem Shelmanov
    EDLHILM
ArXiv (abs)PDFHTML

Papers citing "Uncertainty-Aware Attention Heads: Efficient Unsupervised Uncertainty Quantification for LLMs"

15 / 15 papers shown
Title
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in
  Large Language Models Using Only Attention Maps
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps
Yung-Sung Chuang
Linlu Qiu
Cheng-Yu Hsieh
Ranjay Krishna
Yoon Kim
James R. Glass
HILM
64
46
0
09 Jul 2024
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
Roman Vashurin
Ekaterina Fadeeva
Artem Vazhentsev
Akim Tsvigun
Daniil Vasilev
...
Timothy Baldwin
Timothy Baldwin
Maxim Panov
Artem Shelmanov
Artem Shelmanov
HILM
125
28
0
21 Jun 2024
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of
  Language Models
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
Mert Yuksekgonul
Varun Chandrasekaran
Erik Jones
Suriya Gunasekar
Ranjita Naik
Hamid Palangi
Ece Kamar
Besmira Nushi
HILM
48
48
0
26 Sep 2023
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long
  Form Text Generation
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Sewon Min
Kalpesh Krishna
Xinxi Lyu
M. Lewis
Wen-tau Yih
Pang Wei Koh
Mohit Iyyer
Luke Zettlemoyer
Hannaneh Hajishirzi
HILMALM
132
693
0
23 May 2023
The Internal State of an LLM Knows When It's Lying
The Internal State of an LLM Knows When It's Lying
A. Azaria
Tom Michael Mitchell
HILM
269
344
0
26 Apr 2023
Training Verifiers to Solve Math Word Problems
Training Verifiers to Solve Math Word Problems
K. Cobbe
V. Kosaraju
Mohammad Bavarian
Mark Chen
Heewoo Jun
...
Jerry Tworek
Jacob Hilton
Reiichiro Nakano
Christopher Hesse
John Schulman
ReLMOffRLLRM
308
4,533
0
27 Oct 2021
Unsupervised Quality Estimation for Neural Machine Translation
Unsupervised Quality Estimation for Neural Machine Translation
M. Fomicheva
Shuo Sun
Lisa Yankovskaya
Frédéric Blain
Francisco Guzmán
Mark Fishel
Nikolaos Aletras
Vishrav Chaudhary
Lucia Specia
UQLM
85
206
0
21 May 2020
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive
  Summarization
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
Bogdan Gliwa
Iwona Mochol
M. Biesek
A. Wawer
124
631
0
27 Nov 2019
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction
  to Concepts and Methods
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier
Willem Waegeman
PERUD
244
1,421
0
21 Oct 2019
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional
  Neural Networks for Extreme Summarization
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Shashi Narayan
Shay B. Cohen
Mirella Lapata
AILaw
143
1,682
0
27 Aug 2018
CoQA: A Conversational Question Answering Challenge
CoQA: A Conversational Question Answering Challenge
Siva Reddy
Danqi Chen
Christopher D. Manning
RALMHAI
111
1,205
0
21 Aug 2018
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for
  Reading Comprehension
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Mandar Joshi
Eunsol Choi
Daniel S. Weld
Luke Zettlemoyer
RALM
213
2,676
0
09 May 2017
Get To The Point: Summarization with Pointer-Generator Networks
Get To The Point: Summarization with Pointer-Generator Networks
A. See
Peter J. Liu
Christopher D. Manning
3DPC
306
4,025
0
14 Apr 2017
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
UQCVBDL
831
9,345
0
06 Jun 2015
Bayesian Active Learning for Classification and Preference Learning
Bayesian Active Learning for Classification and Preference Learning
N. Houlsby
Ferenc Huszár
Zoubin Ghahramani
M. Lengyel
122
915
0
24 Dec 2011
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