12 citations · 14 across the 5 of their papers we have counts for
5 papers
Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework
Yanchen Wu, Tenghui Lin, Yingli Zhou +7
Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…
Mistral-C2F: Coarse to Fine Actor for Analytical and Reasoning Enhancement in RLHF and Effective-Merged LLMs
Chen Zheng, Ke Sun, Xun Zhou
Despite the advances in Large Language Models (LLMs), exemplified by models like GPT-4 and Claude, smaller-scale LLMs such as Llama and Mistral often struggle with generating in-de…
Balancing Enhancement, Harmlessness, and General Capabilities: Enhancing Conversational LLMs with Direct RLHF
Chen Zheng, Ke Sun, Hang Wu +2
In recent advancements in Conversational Large Language Models (LLMs), a concerning trend has emerged, showing that many new base LLMs experience a knowledge reduction in their fou…
ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers
Chen Zheng, Ke Sun, Da Tang +4
The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMA encounter limitations in domain-specific tasks, with these models often lacking depth and accuracy in specia…
Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy
Zheng Zhang, Chen Zheng, Da Tang +5
This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks. The goal is to balance general lang…