REALISTA optimizes continuous combinations of valid editing directions in latent space to produce realistic adversarial prompts that elicit hallucinations more effectively than prior methods, including on large reasoning models.
arXiv preprint arXiv:2410.05193 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
roles
dataset 1polarities
use dataset 1representative citing papers
Compares LLMs against semantic similarity for binary classification of student self-explanations in programming education.
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
citing papers explorer
-
REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
REALISTA optimizes continuous combinations of valid editing directions in latent space to produce realistic adversarial prompts that elicit hallucinations more effectively than prior methods, including on large reasoning models.
-
Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education
Compares LLMs against semantic similarity for binary classification of student self-explanations in programming education.
-
LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.