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Towards More Trustworthy and Interpretable LLMs for Code through
  Syntax-Grounded Explanations

Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations

12 July 2024
David Nader-Palacio
Daniel Rodríguez-Cárdenas
Alejandro Velasco
Dipin Khati
Kevin Moran
Denys Poshyvanyk
ArXivPDFHTML

Papers citing "Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations"

5 / 5 papers shown
Title
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of
  Large Language Models for Code Generation
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Jiawei Liu
Chun Xia
Yuyao Wang
Lingming Zhang
ELM
ALM
172
388
0
02 May 2023
Explainable AI for Android Malware Detection: Towards Understanding Why
  the Models Perform So Well?
Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?
Yue Liu
C. Tantithamthavorn
Li Li
Yepang Liu
30
28
0
02 Sep 2022
A Systematic Evaluation of Large Language Models of Code
A Systematic Evaluation of Large Language Models of Code
Frank F. Xu
Uri Alon
Graham Neubig
Vincent J. Hellendoorn
ELM
ALM
193
624
0
26 Feb 2022
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao
Stella Biderman
Sid Black
Laurence Golding
Travis Hoppe
...
Horace He
Anish Thite
Noa Nabeshima
Shawn Presser
Connor Leahy
AIMat
242
1,977
0
31 Dec 2020
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
225
3,658
0
28 Feb 2017
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