Search Results for author:
Found 65 papers, 13 papers with code
Jailbreaking? One Step Is Enough!
Specifically, REDA starts from the target response, guiding the model to embed harmful content within its defensive measures, thereby relegating harmful content to a secondary role and making the model believe it is performing a defensive task.
QueEn: A Large Language Model for Quechua-English Translation
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics.
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity.
OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion
Oracle bone script (OBS), as China's earliest mature writing system, present significant challenges in automatic recognition due to their complex pictographic structures and divergence from modern Chinese characters.
Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation
Our comparison with the zero-shot performance of GPT-4o and LLaMA 3. 1 405B, highlights the significant challenges these models face when translating into low-resource languages.
Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios
Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making.
EchoFM: Foundation Model for Generalizable Echocardiogram Analysis
In this paper, we introduce EchoFM, a foundation model specifically designed to represent and analyze echocardiography videos.
Large Language Models for Manufacturing
The rapid advances in Large Language Models (LLMs) have the potential to transform manufacturing industry, offering new opportunities to optimize processes, improve efficiency, and drive innovation.
Analyzing Nobel Prize Literature with Large Language Models
This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis.
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware.
ECHOPulse: ECG controlled echocardio-grams video generation
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices.
Instruction Embedding: Latent Representations of Instructions Towards Task Identification
In this work, we introduce a new concept, instruction embedding, and construct Instruction Embedding Benchmark (IEB) for its training and evaluation.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement Learning
In this paper, we propose HARP (Human-Assisted Regrouping with Permutation Invariant Critic), a multi-agent reinforcement learning framework designed for group-oriented tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation
Biomedical image segmentation is crucial for accurately diagnosing and analyzing various diseases.
GP-GPT: Large Language Model for Gene-Phenotype Mapping
To address these challenges, we present GP-GPT, the first specialized large language model for genetic-phenotype knowledge representation and genomics relation analysis.
LLM-POTUS Score: A Framework of Analyzing Presidential Debates with Large Language Models
Large language models have demonstrated remarkable capabilities in natural language processing, yet their application to political discourse analysis remains underexplored.
Focused Large Language Models are Stable Many-Shot Learners
With the increase in available context length of LLMs, recent experiments have shown that the performance of ICL does not necessarily scale well in many-shot (demonstration) settings.
Poor-Supervised Evaluation for SuperLLM via Mutual Consistency
To alleviate the insufficiencies of the conditions in reality, we further introduce an algorithm that treats humans (when available) and the models under evaluation as reference models, alternately conducting model weights calibration and filtering during E-step and M-step.
Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning
To demonstrate the effectiveness of DSC, we conduct extensive experiments on three popular categories of reasoning tasks: arithmetic, commonsense and symbolic reasoning on six benchmarks.
Examining the Commitments and Difficulties Inherent in Multimodal Foundation Models for Street View Imagery
The emergence of Large Language Models (LLMs) and multimodal foundation models (FMs) has generated heightened interest in their applications that integrate vision and language.
CogLM: Tracking Cognitive Development of Large Language Models
As Large Language Models (LLMs) have recently shown remarkable abilities across a wide variety of tasks, we are curious about the cognitive levels of current LLMs: to what extent they have developed and how this development has been achieved.
Biomedical SAM 2: Segment Anything in Biomedical Images and Videos
Medical image segmentation and video object segmentation are essential for diagnosing and analyzing diseases by identifying and measuring biological structures.
CityX: Controllable Procedural Content Generation for Unbounded 3D Cities
With the surge of embodied intelligence, recent years have witnessed an increasing presence of physical agents in urban areas, such as autonomous vehicles and delivery robots.
Integrate the Essence and Eliminate the Dross: Fine-Grained Self-Consistency for Free-Form Language Generation
These methods, however, face limitations due to their inability to fully utilize the nuanced consensus knowledge present within multiple candidate samples, often resulting in suboptimal outputs.
Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation
In DDS, both sequence-level and token-level adaptive search can be achieved to adjust the decoding process in a unified framework.
SceneX: Procedural Controllable Large-scale Scene Generation
Extensive experiments demonstrated the capability of our method in controllable large-scale scene generation, including nature scenes and unbounded cities, as well as scene editing such as asset placement and season translation.
Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning
This data-reliance may lead to low generalization of the learned alignment relationships.
LLMs for Coding and Robotics Education
To teach young children how to code and compete in robot challenges, large language models are being utilized for robot code explanation, generation, and modification.
Revolutionizing Finance with LLMs: An Overview of Applications and Insights
Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions.
Generative Dense Retrieval: Memory Can Be a Burden
Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced to conduct fine-grained intra-cluster matching from clusters to relevant documents.
Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding
This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics.
Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.
BatchEval: Towards Human-like Text Evaluation
Significant progress has been made in automatic text evaluation with the introduction of large language models (LLMs) as evaluators.
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications
The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision.
Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data
In this work, we illustrate the merit of negative data and propose a model specialization framework to distill LLMs with negative samples besides positive ones.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Evaluating Large Language Models in Ophthalmology
Methods: A 100-item ophthalmology single-choice test was administered to three different LLMs (GPT-3. 5, GPT-4, and PaLM2) and three different professional levels (medical undergraduates, medical masters, and attending physicians), respectively.
Evaluating multiple large language models in pediatric ophthalmology
IMPORTANCE The response effectiveness of different large language models (LLMs) and various individuals, including medical students, graduate students, and practicing physicians, in pediatric ophthalmology consultations, has not been clearly established yet.
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education.
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification
Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients' data.
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models
In this study, we investigate a privacy policy text analysis framework PolicyGPT based on the LLM.
RadOnc-GPT: A Large Language Model for Radiation Oncology
This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods.
Radiology-Llama2: Best-in-Class Large Language Model for Radiology
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning.
NewsDialogues: Towards Proactive News Grounded Conversation
In this paper, we propose a novel task, Proactive News Grounded Conversation, in which a dialogue system can proactively lead the conversation based on some key topics of the news.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
SAMAug: Point Prompt Augmentation for Segment Anything Model
Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts.
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.
Radiology-GPT: A Large Language Model for Radiology
We introduce Radiology-GPT, a large language model for radiology.
Artificial General Intelligence for Medical Imaging Analysis
Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks.
Heterogeneous-Branch Collaborative Learning for Dialogue Generation
Collaborative learning, also known as online knowledge distillation, is an effective way to conduct one-stage group distillation in the absence of a well-trained large teacher model.
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.
Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables
Specifically, HLV constrains the global semantics of responses through discrete latent variables and enriches responses with continuous latent variables.
Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder
Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings remains a huge challenge for neural dialogue systems.
Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning
However, the discarded samples may obtain high scores in other perspectives and can provide regularization effects on the model learning, which causes the performance improvement to be sensitive to the filtering ratio.
Diversifying Neural Dialogue Generation via Negative Distillation
Generative dialogue models suffer badly from the generic response problem, limiting their applications to a few toy scenarios.
Federated Stochastic Primal-dual Learning with Differential Privacy
Specifically, we show that, by guaranteeing $(\epsilon, \delta)$-DP for each client per communication round, the proposed algorithm guarantees $(\mathcal{O}(q\epsilon \sqrt{p T}), \delta)$-DP after $T$ communication rounds while maintaining an $\mathcal{O}(1/\sqrt{pTQ})$ convergence rate for a convex and non-smooth learning problem, where $Q$ is the number of local SGD steps, $p$ is the client sampling probability, $q=\max_{i} q_i/\sqrt{1-q_i}$ and $q_i$ is the data sampling probability of each client under PCP.
Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders
Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables.
THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses
In this work, we proposed a conversation model named "THINK" (Teamwork generation Hover around Impressive Noticeable Keywords) to make the decoder more complicated and avoid generating duplicated and self-contradicting responses.
A Charge-Density-Wave Topological Semimetal
We here demonstrate that the room-temperature phase of (TaSe$_4$)$_2$I is a Weyl semimetal with 24 pairs of Weyl nodes.
Band Gap
Materials Science
Strongly Correlated Electrons
Generalization of k-means Related Algorithms
This article briefly introduced Arthur and Vassilvitshii's work on \textbf{k-means++} algorithm and further generalized the center initialization process.
Variational Neural Networks: Every Layer and Neuron Can Be Unique
The lack of guiding principles for the selection of activation function is lamentable.
