Search Results for author:
Found 22 papers, 7 papers with code
LoRA-as-an-Attack! Piercing LLM Safety Under The Share-and-Play Scenario
Our aim is to raise awareness of the potential risks under the emerging share-and-play scenario, so as to proactively prevent potential consequences caused by LoRA-as-an-Attack.
Large Language Models As Faithful Explainers
In this work, we introduce a generative explanation framework, xLLM, to improve the faithfulness of the explanations provided in natural language formats for LLMs.
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks.
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning
Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts).
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation.
DEGREE: Decomposition Based Explanation For Graph Neural Networks
Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data.
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks.
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching
The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care.
SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes.
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
To overcome this challenge, we introduce a clean-label backdoor watermarking framework that uses imperceptible perturbations to replace mislabeled samples.
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction.
Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23. 37% to 63. 99% for the named entity recognition task and from 75. 86% to 83. 59% for the relation extraction task.
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
The Science of Detecting LLM-Generated Texts
The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans.
Mitigating Relational Bias on Knowledge Graphs
Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning.
Defense Against Explanation Manipulation
Explainable machine learning attracts increasing attention as it improves transparency of models, which is helpful for machine learning to be trusted in real applications.
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks
The main idea is to insert a watermark which is only known to defender into the protected model and the watermark will then be transferred into all stolen models.
Fairness via Representation Neutralization
This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder.
Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection
In this paper, we present DSN (Deep Serial Number), a simple yet effective watermarking algorithm designed specifically for deep neural networks (DNNs).
Mitigating Gender Bias in Captioning Systems
Image captioning has made substantial progress with huge supporting image collections sourced from the web.
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
In this paper, we investigate a specific security problem called trojan attack, which aims to attack deployed DNN systems relying on the hidden trigger patterns inserted by malicious hackers.
