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Found 13 papers, 8 papers with code
The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge
This technical report outlines our submission system for the CHiME-8 NOTSOFAR-1 Challenge.
Model Hijacking Attack in Federated Learning
Machine learning (ML), driven by prominent paradigms such as centralized and federated learning, has made significant progress in various critical applications ranging from autonomous driving to face recognition.
SeqMIA: Sequential-Metric Based Membership Inference Attack
Building upon this signal, we introduce a novel attack method called Sequential-metric based Membership Inference Attack (SeqMIA).
UniGen: A Unified Framework for Textual Dataset Generation Using Large Language Models
Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expensive human-generated datasets.
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?
This paper introduces a method to enhance the multilingual performance of LLMs by aggregating knowledge from diverse languages.
GUI-World: A Video Benchmark and Dataset for Multimodal GUI-oriented Understanding
We evaluate the capabilities of current state-of-the-art MLLMs, including Image LLMs and Video LLMs, in understanding various types of GUI content, especially dynamic and sequential content.
HonestLLM: Toward an Honest and Helpful Large Language Model
Subsequently, we present two approaches to augmenting honesty and helpfulness in LLMs: a training-free enhancement and a fine-tuning-based improvement.
WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs
The benchmarking results demonstrate that our dataset significantly improves the ability of MLLMs to generate code from webpage designs, confirming its effectiveness and usability for future applications in front-end design tools.
NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries
Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis.
I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench
Do large language models (LLMs) exhibit any forms of awareness similar to humans?
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests.
FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing
The bottleneck of solving this problem is the accurate perception of rapid dynamic objects.
Motion Compensation
Robust Object Detection
+1
Robotics
