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Found 8 papers, 1 papers with code
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, where the model fabricates facts and produces non-factual statements.
LUNA: A Model-Based Universal Analysis Framework for Large Language Models
the trustworthiness perspective, is bound to and enriches the abstract model with semantics, which enables more detailed analysis applications for diverse purposes.
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset.
Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy
In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applications.
Neural Episodic Control with State Abstraction
Generally, episodic control-based approaches are solutions that leverage highly-rewarded past experiences to improve sample efficiency of DRL algorithms.
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness
Therefore, to better understand the current quality status and challenges of these SOTA FL techniques in the presence of attacks and faults, we perform a large-scale empirical study to investigate the SOTA FL's quality from multiple angles of attacks, simulated faults (via mutation operators), and aggregation (defense) methods.
Neural Hidden Markov Model for Machine Translation
Attention-based neural machine translation (NMT) models selectively focus on specific source positions to produce a translation, which brings significant improvements over pure encoder-decoder sequence-to-sequence models.
Hybrid Neural Network Alignment and Lexicon Model in Direct HMM for Statistical Machine Translation
Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks.
