Trending Research
The Rise and Potential of Large Language Model Based Agents: A Survey
Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks.
NExT-GPT: Any-to-Any Multimodal LLM
While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities.
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8x A100 machine.
ProPainter: Improving Propagation and Transformer for Video Inpainting
We also propose a mask-guided sparse video Transformer, which achieves high efficiency by discarding unnecessary and redundant tokens.
Ranked #1 on
Video Inpainting
on DAVIS
Agents: An Open-source Framework for Autonomous Language Agents
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications
Language model applications are becoming increasingly popular and complex, often including features like tool usage and retrieval augmentation.
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
Communicative Agents for Software Development
At the core of this paradigm lies ChatDev, a virtual chat-powered software development company that mirrors the established waterfall model, meticulously dividing the development process into four distinct chronological stages: designing, coding, testing, and documenting.
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
This shows a failure of logical deduction that we hypothesize is caused by the Reversal Curse.
DreamLLM: Synergistic Multimodal Comprehension and Creation
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation.
