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Found 27 papers, 20 papers with code
Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
While safety alignment has been extensively studied for LLM, there is still a large research gap for Large Reasoning Models (LRMs) that equip with improved reasoning capability.
Multi-Agent Reinforcement Learning with Focal Diversity Optimization
First, we develop an agent-fusion framework for encouraging multiple LLM based agents to collaborate in producing the final inference output for each LLM query.
TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs
Zeroth-order optimization (ZO) has demonstrated remarkable promise in efficient fine-tuning tasks for Large Language Models (LLMs).
Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation
Mainstream defenses aim to vaccinate the model such that the later harmful fine-tuning attack is less effective.
Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation
By designing a new red-teaming method, we in this paper show that purely relying on the moderation guardrail for data filtration is not reliable.
$H^3$Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs
The former penalizes the selection errors of the expert-router, and the latter mediates the expert weights drifting during fine-tuning and dynamically adjusts the fusion behavior of the resulting model by canalizing the activations on the experts.
Targeted Vaccine: Safety Alignment for Large Language Models against Harmful Fine-Tuning via Layer-wise Perturbation
Second, instead of applying uniform perturbation across all layers, T-Vaccine only applies perturbation to the safety-critical layers while keeping other layers frozen during training.
LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity
This paper presents LLM-TOPLA, a diversity-optimized LLM ensemble method with three unique properties: (i) We introduce the focal diversity metric to capture the diversity-performance correlation among component LLMs of an ensemble.
Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
To clear up concern, this paper provide a comprehensive overview to three aspects of harmful fine-tuning: attacks setting, defense design and evaluation methodology.
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
For the first time in the literature, we in this paper show that \textit{harmful perturbation} over the model weights should be the root cause of alignment-broken of harmful fine-tuning.
Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning
To this end, we propose Antidote, a post-fine-tuning stage solution, which remains \textbf{\textit{agnostic to the training hyper-parameters in the fine-tuning stage}}.
Personalized Privacy Protection Mask Against Unauthorized Facial Recognition
Second, we incorporate a perceptibility optimization to preserve the visual quality of the protected facial images.
Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-broken by fine-tuning on a dataset mixed with harmful data.
Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models.
A Survey on Large Language Model-Based Game Agents
The development of game agents holds a critical role in advancing towards Artificial General Intelligence.
Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
The new paradigm of finetuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the finetuning to produce an alignment-broken model.
PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
We introduce PokeLLMon, the first LLM-embodied agent that achieves human-parity performance in tactical battle games, as demonstrated in Pokemon battles.
Resource-Efficient Transformer Pruning for Finetuning of Large Models
With the recent advances in vision transformers and large language models (LLMs) finetuning costly large models on downstream learning tasks poses significant challenges under limited computational resources.
Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives
The goal of auditor is to yield a broad spectrum of vulnerabilities with the hope of encompassing the correct answer, whereas the goal of critic that evaluates the validity of identified vulnerabilities is to minimize the number of false positives.
Fusion of Global and Local Knowledge for Personalized Federated Learning
Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data.
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
Federated learning is an emerging distributed machine learning framework which jointly trains a global model via a large number of local devices with data privacy protections.
Adaptive Deep Neural Network Inference Optimization with EENet
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
Achieving Personalized Federated Learning with Sparse Local Models
To counter this issue, personalized FL (PFL) was proposed to produce dedicated local models for each individual user.
On Heterogeneously Distributed Data, Sparsity Matters
Federated learning (FL) is particularly vulnerable to heterogeneously distributed data, since a common global model in FL may not adapt to the heterogeneous data distribution of each user.
Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply
With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery.
Stochastic Client Selection for Federated Learning with Volatile Clients
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public.
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.
