Search Results for author:
Found 9 papers, 6 papers with code
LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking
We identify two kinds of intrinsic inconsistency in LLM-based pairwise comparisons: order inconsistency which leads to conflicting results when switching the passage order, and transitive inconsistency which leads to non-transitive triads among all preference pairs.
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
Through conducting extensive experiments on a large scale of harmful and safe prompts, we validate the effectiveness of the proposed AutoDefense in improving the robustness against jailbreak attacks, while maintaining the performance at normal user request.
A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model
2) Prompt model is designed to generate individualized creatives for different user groups, which can further improve the diversity and quality.
Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising
These methods typically involve the training of calibrators using a validation set and subsequently applying these calibrators to correct the original estimated values during online inference.
LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation
Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems.
i2LQR: Iterative LQR for Iterative Tasks in Dynamic Environments
This work introduces a novel control strategy called Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which aims to improve closed-loop performance with local trajectory optimization for iterative tasks in a dynamic environment.
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors
IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge devices itself.
Optimized Cost per Click in Taobao Display Advertising
Moreover, the platform has to be responsible for the business revenue and user experience.
