12 citations · 14 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
Ruohong Zhang, Luyu Gao, Chen Zheng +6
Large Language Models (LLMs), despite their great power in language generation, often encounter challenges when dealing with intricate and knowledge-demanding queries in specific d…
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…