Search Results for author:
Found 10 papers, 4 papers with code
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
This work enables SNNs to match ANN performance while maintaining the low-power advantage, marking a significant step towards SNNs as a general visual backbone.
FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs
Based on this, we curate a challenging dataset, \texttt{FairMT-1K}, and test 15 current state-of-the-art (SOTA) LLMs on this dataset.
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning
Achieving consistent high-quality machine translation (MT) across diverse domains remains a significant challenge, primarily due to the limited and imbalanced parallel training data available in various domains.
Learnable Privacy Neurons Localization in Language Models
To bridge this gap, we introduce a pioneering method for pinpointing PII-sensitive neurons (privacy neurons) within LLMs.
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
CNN-based SNNs are the current mainstream of neuromorphic computing.
PolyLM: An Open Source Polyglot Large Language Model
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis
Recent advances in pretrained vision-language models (VLMs) such as CLIP have shown great performance for zero-shot natural image recognition and exhibit benefits in medical applications.
ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images
In computer-assisted orthodontics, three-dimensional tooth models are required for many medical treatments.
WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition
The model first trains the sentence pairs in the text, calculate similarity between sentence pairs, and fine-tunes BERT used for the named entity recognition task according to the similarity, so as to alleviate word ambiguity.
Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance.
