20 citations · 30 across the 4 of their papers we have counts for
4 papers
Beyond the Prompt in Large Language Models: Comprehension, In-Context Learning, and Chain-of-Thought
Yuling Jiao, Yanming Lai, Huazhen Lin +3
Large Language Models (LLMs) have demonstrated remarkable proficiency across diverse tasks, exhibiting emergent properties such as semantic prompt comprehension, In-Context Learnin…
Accelerating RLHF Training with Reward Variance Increase
Zonglin Yang, Zhexuan Gu, Houduo Qi +1
Reinforcement learning from human feedback (RLHF) is an essential technique for ensuring that large language models (LLMs) are aligned with human values and preferences during the…
A Survey on Large Language Model-based Agents for Statistics and Data Science
Maojun Sun, Ruijian Han, Binyan Jiang +4
In recent years, data science agents powered by Large Language Models (LLMs), known as "data agents," have shown significant potential to transform the traditional data analysis pa…
LAMBDA: A Large Model Based Data Agent
Maojun Sun, Ruijian Han, Binyan Jiang +4
We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large language models. LAMBDA is des…