2 citations · 4 across the 16 of their papers we have counts for
10 papers · 1 filter
FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs
Qian Chen, Jinlan Fu, Changsong Li +3
Although Multimodal Large Language Models (MLLMs) demonstrate strong omni-modal perception, their ability to forecast future events from audio-visual cues remains largely unexplore…
AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents
Jiafeng Liang, Hao Li, Chang Li +12
Memory serves as the pivotal nexus bridging past and future, providing both humans and AI systems with invaluable concepts and experience to navigate complex tasks. Recent research…
World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning
Siyin Wang, Zhaoye Fei, Qinyuan Cheng +4
Recent advances in large vision-language models (LVLMs) have shown promise for embodied task planning, yet they struggle with fundamental challenges like dependency constraints and…
CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs
Jinlan Fu, Shenzhen Huangfu, Hao Fei +4
Multimodal Large Language Models (MLLMs) still struggle with hallucinations despite their impressive capabilities. Recent studies have attempted to mitigate this by applying Direct…
Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism
Anhao Zhao, Fanghua Ye, Jinlan Fu +1
Large language models (LLMs) exhibit remarkable in-context learning (ICL) capabilities. However, the underlying working mechanism of ICL remains poorly understood. Recent research…
Chain of Thought Explanation for Dialogue State Tracking
Lin Xu, Ningxin Peng, Daquan Zhou +2
Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction achieved by maintaining a predefined set of slots and their corresponding va…
CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations
Lin Xu, Qixian Zhou, Jinlan Fu +1
Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge. Previous knowledge selection met…
Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge
Jinlan Fu, Shenzhen Huangfu, Hang Yan +2
Large Language Models (LLMs) have recently showcased remarkable generalizability in various domains. Despite their extensive knowledge, LLMs still face challenges in efficiently ut…
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
Siyin Wang, Shimin Li, Tianxiang Sun +6
In the realm of Large Language Models (LLMs), users commonly employ diverse decoding strategies and adjust hyperparameters to control the generated text. However, a critical questi…
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System
Mingtao Yang, See-Kiong Ng, Jinlan Fu
Pre-trained conversation models (PCMs) have demonstrated remarkable results in task-oriented dialogue (TOD) systems. Many PCMs focus predominantly on dialogue management tasks like…