15 citations · 58 across the 18 of their papers we have counts for
20 papers
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Xiujin Liu +77
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…
SAE-SSV: Supervised Steering in Sparse Representation Spaces for Reliable Control of Language Models
Zirui He, Mingyu Jin, Bo Shen +3
Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but controlling their behavior reliably remains challenging…
Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models
Xinyi Wang, Shawn Tan, Shenbo Xu +4
Reasoning is a core capability of language models (LMs), yet it remains unclear how much model capacity is necessary to support reasoning during pretraining. In this work, we study…
Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding
Mingyu Jin, Kai Mei, Wujiang Xu +5
Large language models (LLMs) have achieved remarkable success in contextual knowledge understanding. In this paper, we show that these concentrated massive values consistently emer…
Game-theoretic LLM: Agent Workflow for Negotiation Games
Wenyue Hua, Ollie Liu, Lingyao Li +9
This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several…
Disentangling Memory and Reasoning Ability in Large Language Models
Mingyu Jin, Weidi Luo, Sitao Cheng +5
Large Language Models (LLMs) have demonstrated strong performance in handling complex tasks requiring both extensive knowledge and reasoning abilities. However, the existing LLM in…
Visual Agents as Fast and Slow Thinkers
Guangyan Sun, Mingyu Jin, Zhenting Wang +7
Achieving human-level intelligence requires refining cognitive distinctions between System 1 and System 2 thinking. While contemporary AI, driven by large language models, demonstr…
Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction
Yunxiao Shi, Wujiang Xu, Haimin Zhang +2
Modeling high-order feature interactions is crucial for click-through rate (CTR) prediction, yet traditional approaches typically predefine a maximum interaction order and exhausti…
Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models
Qingcheng Zeng, Mingyu Jin, Qinkai Yu +12
Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. One commonly used method to assess the reliability…
When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
Chong Zhang, Xinyi Liu, Zhongmou Zhang +10
Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fund…
MoralBench: Moral Evaluation of LLMs
Jianchao Ji, Yutong Chen, Mingyu Jin +3
In the rapidly evolving field of artificial intelligence, large language models (LLMs) have emerged as powerful tools for a myriad of applications, from natural language processing…
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?
Mingyu Jin, Qinkai Yu, Jingyuan Huang +10
Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities rem…
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
Dong Shu, Tianle Chen, Mingyu Jin +3
The task of multi-hop link prediction within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, as it requires the model to reason through and u…
What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents
Zhaoqian Xue, Beichen Wang, Suiyuan Zhu +5
This study introduces "CosmoAgent," an innovative artificial intelligence system that utilizes Large Language Models (LLMs) to simulate complex interactions between human and extra…
EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs
Sam Lin, Wenyue Hua, Zhenting Wang +3
Cloud-based Large Language Models (LLMs) such as ChatGPT have become increasingly integral to daily operations. Nevertheless, they also introduce privacy concerns: firstly, numerou…
TrustAgent: Towards Safe and Trustworthy LLM-based Agents
Wenyue Hua, Xianjun Yang, Mingyu Jin +4
The rise of LLM-based agents shows great potential to revolutionize task planning, capturing significant attention. Given that these agents will be integrated into high-stake domai…
AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models
Dong Shu, Chong Zhang, Mingyu Jin +3
Jailbreak attacks represent one of the most sophisticated threats to the security of large language models (LLMs). To deal with such risks, we introduce an innovative framework tha…
The Impact of Reasoning Step Length on Large Language Models
Mingyu Jin, Qinkai Yu, Dong Shu +5
Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length…
Assessing Prompt Injection Risks in 200+ Custom GPTs
Jiahao Yu, Yuhang Wu, Dong Shu +3
In the rapidly evolving landscape of artificial intelligence, ChatGPT has been widely used in various applications. The new feature - customization of ChatGPT models by users to ca…
MathAttack: Attacking Large Language Models Towards Math Solving Ability
Zihao Zhou, Qiufeng Wang, Mingyu Jin +6
With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the secur…