-
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 78 -
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
Paper • 2309.04564 • Published • 16 -
LIMA: Less Is More for Alignment
Paper • 2305.11206 • Published • 26 -
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
Paper • 2302.01560 • Published • 1
丁煌浩
Iess
AI & ML interests
None yet
Organizations
None yet
Data
-
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
Paper • 2309.04662 • Published • 24 -
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
Paper • 2309.11998 • Published • 25 -
YanweiLi/MGM-Pretrain
Viewer • Updated • 1.27M • 40 • 16 -
YanweiLi/MGM-Instruction
Updated • 107 • 17
Inference
Prompt
VLM
CV-Mobile
Quantization
Agent
-
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
Paper • 2302.01560 • Published • 1 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11 -
LASER: LLM Agent with State-Space Exploration for Web Navigation
Paper • 2309.08172 • Published • 13 -
A Data Source for Reasoning Embodied Agents
Paper • 2309.07974 • Published • 7
NLP
LLM
-
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 78 -
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
Paper • 2309.04564 • Published • 16 -
LIMA: Less Is More for Alignment
Paper • 2305.11206 • Published • 26 -
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
Paper • 2302.01560 • Published • 1
CV-Mobile
Data
-
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
Paper • 2309.04662 • Published • 24 -
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
Paper • 2309.11998 • Published • 25 -
YanweiLi/MGM-Pretrain
Viewer • Updated • 1.27M • 40 • 16 -
YanweiLi/MGM-Instruction
Updated • 107 • 17
Quantization
Inference
Agent
-
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
Paper • 2302.01560 • Published • 1 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11 -
LASER: LLM Agent with State-Space Exploration for Web Navigation
Paper • 2309.08172 • Published • 13 -
A Data Source for Reasoning Embodied Agents
Paper • 2309.07974 • Published • 7
Prompt
NLP
VLM