Search Results for author:
Found 31 papers, 13 papers with code
Large Language Models for Bioinformatics
With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications.
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.
DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters
Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal.
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.
Legal Evalutions and Challenges of Large Language Models
In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions.
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training
Recent approaches for visually-rich document understanding (VrDU) uses manually annotated semantic groups, where a semantic group encompasses all semantically relevant but not obviously grouped words.
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.
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.
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
We have conducted extensive experiments on existing large multimodal models, offering insights into their performance and areas for improvement in image-to-web domain.
Putting People in LLMs' Shoes: Generating Better Answers via Question Rewriter
By enhancing the intelligibility of human questions for black-box LLMs, our question rewriter improves the quality of generated answers.
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain.
States Hidden in Hidden States: LLMs Emerge Discrete State Representations Implicitly
Our work presents a novel exploration of LLMs' symbolic calculation abilities and the underlying mechanisms.
Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency
Our key contribution is the introduction of generation rate, which corresponds to the local deformation of manifold over time around an image component.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs
Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text.
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness.
Ranked #29 on
Rgb-T Tracking
on RGBT234
Ultraman: Single Image 3D Human Reconstruction with Ultra Speed and Detail
In this paper, we propose a new method called \emph{Ultraman} for fast reconstruction of textured 3D human models from a single image.
Ranked #2 on
Lifelike 3D Human Generation
on THuman2.0 Dataset
$\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens
Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction.
Fine-Grained Stateful Knowledge Exploration: A Novel Paradigm for Integrating Knowledge Graphs with Large Language Models
Most existing methods use a paradigm that treats the question as the objective, with relevant knowledge being incrementally retrieved from the knowledge graph.
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.
MaxQ: Multi-Axis Query for N:M Sparsity Network
Meanwhile, a sparsity strategy that gradually increases the percentage of N:M weight blocks is applied, which allows the network to heal from the pruning-induced damage progressively.
Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object
We introduce a large language model to parse the text and identify stylization goals and specific styles.
Asca: less audio data is more insightful
Audio recognition in specialized areas such as birdsong and submarine acoustics faces challenges in large-scale pre-training due to the limitations in available samples imposed by sampling environments and specificity requirements.
ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models
To this end, we propose the ZhuJiu benchmark, which has the following strengths: (1) Multi-dimensional ability coverage: We comprehensively evaluate LLMs across 7 ability dimensions covering 51 tasks.
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning
This approach is different from the pruning method as it is not limited by the initial number of training parameters, and each parameter matrix has a higher rank upper bound for the same training overhead.
Data-driven multinomial random forest
In this article, we strengthen the proof methods of some previously weakly consistent variants of random forests into strongly consistent proof methods, and improve the data utilization of these variants, in order to obtain better theoretical properties and experimental performance.
Data-driven multinomial random forest: A new random forest variant with strong consistency
In this paper, we modify the proof methods of some previously weakly consistent variants of random forests into strongly consistent proof methods, and improve the data utilization of these variants in order to obtain better theoretical properties and experimental performance.
