Search Results for author:
Found 13 papers, 6 papers with code
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query.
Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding
In this paper, we adapt Whisper, which is a large-scale multilingual pre-trained speech recognition model, to CS from both encoder and decoder parts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models
These help us curate CMoralEval that encompasses both explicit moral scenarios (14, 964 instances) and moral dilemma scenarios (15, 424 instances), each with instances from different data sources.
Establishing Rigorous and Cost-effective Clinical Trials for Artificial Intelligence Models
We envision DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner.
AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing.
International Trade Flow Prediction with Bilateral Trade Provisions
This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape.
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Existing automatic prompt engineering algorithms primarily focus on language modeling and classification tasks, leaving the domain of IR, particularly reranking, underexplored.
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
To address this issue, we introduce a novel approach to long-text classification and prediction.
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
In this paper, we present MaxK-GNN, an advanced high-performance GPU training system integrating algorithm and system innovation.
Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis
The increasing use of Advanced Language Models (ALMs) in diverse sectors, particularly due to their impressive capability to generate top-tier content following linguistic instructions, forms the core of this investigation.
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Utilizing these principles, we formulated a kernel for sparse matrix multiplication (SpMM) in GCNs that employs block-level partitioning and combined warp strategy.
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues.
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns.
