Search Results for author:
Found 16 papers, 6 papers with code
BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis
This novel system aims to simulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time.
Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
We employ a probing technique to extract representations from different layers of the model and apply these to classification tasks.
Goal-guided Generative Prompt Injection Attack on Large Language Models
Although there is currently a large amount of research on prompt injection attacks, most of these black-box attacks use heuristic strategies.
Large Language Models in Biomedical and Health Informatics: A Bibliometric Review
Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research.
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques.
NPHardEval4V: A Dynamic Reasoning Benchmark of Multimodal Large Language Models
Understanding the reasoning capabilities of Multimodal Large Language Models (MLLMs) is an important area of research.
What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents
In this study, we introduce "CosmoAgent," an innovative artificial intelligence framework utilizing Large Language Models (LLMs) to simulate complex interactions between human and extraterrestrial civilizations, with a special emphasis on Stephen Hawking's cautionary advice about not sending radio signals haphazardly into the universe.
Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities
However, there is a research gap in the LLMs' preferences in this field.
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction.
AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models
In our research, we pioneer a novel approach to evaluate the effectiveness of jailbreak attacks on Large Language Models (LLMs), such as GPT-4 and LLaMa2, diverging from traditional robustness-focused binary evaluations.
The Impact of Reasoning Step Length on Large Language Models
Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models.
Assessing Prompt Injection Risks in 200+ Custom GPTs
In the rapidly evolving landscape of artificial intelligence, ChatGPT has been widely used in various applications.
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training.
MathAttack: Attacking Large Language Models Towards Math Solving Ability
Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems.
A Simple and Effective Baseline for Attentional Generative Adversarial Networks
Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task.
Image Blending Algorithm with Automatic Mask Generation
In recent years, image blending has gained popularity for its ability to create visually stunning content.
