Search Results for author:
Found 23 papers, 18 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.
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.
Collaborative Contrastive Network for Click-Through Rate Prediction
E-commerce platforms provide entrances for customers to enter mini-apps to meet their specific shopping needs.
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.
Joint-Motion Mutual Learning for Pose Estimation in Videos
Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model.
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.
BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection
As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized.
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.
Deep Intention-Aware Network for Click-Through Rate Prediction
We identify that the key to TIRA is to extract customers' personalized entering intention and weigh the impact of triggers based on this intention.
Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump
With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors.
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction
Specifically, we establish a heterogeneous graph that contains physical and semantic linkages to guide the feature transfer process from warmed-up video to cold-start videos.
From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba
Except for introducing future knowledge for prediction, we propose Aliformer based on the bidirectional Transformer, which can utilize the historical information, current factor, and future knowledge to predict future sales.
