7 citations · 15 across the 12 of their papers we have counts for
5 papers · 1 filter
Reinforcement Learning Enhanced LLMs: A Survey
Shuhe Wang, Shengyu Zhang, Jie Zhang +7
Reinforcement learning (RL) enhanced large language models (LLMs), particularly exemplified by DeepSeek-R1, have exhibited outstanding performance. Despite the effectiveness in imp…
Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning
Shuhe Wang, Guoyin Wang, Yizhong Wang +3
Packing, initially utilized in the pre-training phase, is an optimization technique designed to maximize hardware resource efficiency by combining different training sequences to f…
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information
Zhiwei Liu, Kailai Yang, Qianqian Xie +3
Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society. However, current methods for cross-domain misinformati…
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models
Jacek Wiland, Max Ploner, Alan Akbik
Knowledge probing assesses to which degree a language model (LM) has successfully learned relational knowledge during pre-training. Probing is an inexpensive way to compare LMs of…
A Sentiment Consolidation Framework for Meta-Review Generation
Miao Li, Jey Han Lau, Eduard Hovy
Modern natural language generation systems with Large Language Models (LLMs) exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain…