Search Results for author:
Found 42 papers, 10 papers with code
Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm
Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE.
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students).
Editing Conceptual Knowledge for Large Language Models
Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs).
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions.
RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning
Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis.
Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies
The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language fluency and reasoning capacities.
Unified Hallucination Detection for Multimodal Large Language Models
Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination.
EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems
Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer from.
MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts
However, the gate's routing mechanism also gives rise to narrow vision: the individual MoE's expert fails to use more samples in learning the allocated sub-task, which in turn limits the MoE to further improve its generalization ability.
OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy
We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novel multi-adapter method, OrchMoE, which capitalizes on modular skill architecture for enhanced forward transfer in neural networks.
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue.
CharPoet: A Chinese Classical Poetry Generation System Based on Token-free LLM
A video demonstration of CharPoet is available at https://youtu. be/voZ25qEp3Dc.
Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy
Hence, this paper presents a generic framework for accelerating the inference process, resulting in a substantial increase in speed and cost reduction for our RAG system, with lossless generation accuracy.
GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System
of a cascade RS, when a user triggers a request, we define two actions that determine the computation: (1) the trained instances of models with different computational complexity; and (2) the number of items to be inferred in the stage.
Multiple Instance Learning for Uplift Modeling
Uplift modeling is widely used in performance marketing to estimate effects of promotion campaigns (e. g., increase of customer retention rate).
RJUA-QA: A Comprehensive QA Dataset for Urology
We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications.
Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup
In our research, we constructed a dataset, the Multimodal Advertisement Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted experiments to validate the reliability of our proposed strategy.
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
In this paper, we seek to carefully prompt a Large Language Model (LLM) with domain-level knowledge as a better marketing-oriented knowledge miner for marketing-oriented knowledge graph construction, which is however non-trivial, suffering from several inevitable issues in real-world marketing scenarios, i. e., uncontrollable relation generation of LLMs, insufficient prompting ability of a single prompt, the unaffordable deployment cost of LLMs.
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
Modular and composable transfer learning is an emerging direction in the field of Parameter Efficient Fine-Tuning, as it enables neural networks to better organize various aspects of knowledge, leading to improved cross-task generalization.
ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference
Aligning language models to human expectations, e. g., being helpful and harmless, has become a pressing challenge for large language models.
Intelligent Virtual Assistants with LLM-based Process Automation
This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests.
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
(3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs.
Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and reasoning of history to generate high-quality responses.
Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster
In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself.
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation.
AntM$^{2}$C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
3) AntM$^{2}$C provides 1 billion CTR data with 200 features, including 200 million users and 6 million items.
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards.
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs.
Leveraging Large Language Models for Pre-trained Recommender Systems
In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs.
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage.
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses
\emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify.
Adversarial Learning for Incentive Optimization in Mobile Payment Marketing
This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy.
Asynchronous Decentralized Online Learning
To overcome this problem, we study decentralized online learning in the asynchronous setting, which allows different learners to work at their own pace.
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning
To apply value-based methods to CRL, a recent groundbreaking line of game-theoretic approaches uses the mixed policy that randomizes among a set of carefully generated policies to converge to the desired constraint-satisfying policy.
A framework for massive scale personalized promotion
In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage.
Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction
We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly.
Graph Neural Network Based VC Investment Success Prediction
Predicting the start-ups that will eventually succeed is essentially important for the venture capital business and worldwide policy makers, especially at an early stage such that rewards can possibly be exponential.
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign
In this paper, we analyze the network A/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data.
A Reduction Approach to Constrained Reinforcement Learning
In this paper, we present a reduction approach to find sparse policies that randomize among a constant number of policies for the constrained RL problem.
Adaptive Optimizers with Sparse Group Lasso
We develop a novel framework that adds the regularizers to a family of adaptive optimizers in deep learning, such as MOMENTUM, ADAGRAD, ADAM, AMSGRAD, ADAHESSIAN, and create a new class of optimizers, which are named GROUP MOMENTUM, GROUP ADAGRAD, GROUP ADAM, GROUP AMSGRAD and GROUP ADAHESSIAN, etc., accordingly.
Robust Offline Reinforcement Learning from Low-Quality Data
We theoretically show that AdaPT produces a tight upper bound on the distributional deviation between the learned policy and the behavior policy, and this upper bound is the minimum requirement to guarantee policy improvement at each iteration.
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.
