18 citations · 24 across the 8 of their papers we have counts for
10 papers
LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models
Fengqi Zhu, Shaoxuan Xu, Jingyang Ou +11
Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling, yet the scaling behavior of Mixture-of-Experts (MoE) dLLMs remains poorly understoo…
Improved Large Language Diffusion Models
Shen Nie, Qiyang Min, Shaoxuan Xu +7
Modern large language models are predominantly trained with autoregressive factorization and causal attention. We present \emph{iLLaDA}, an 8B masked diffusion language model train…
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models
Fengqi Zhu, Rongzhen Wang, Shen Nie +8
While Masked Diffusion Models (MDMs), such as LLaDA, present a promising paradigm for language modeling, there has been relatively little effort in aligning these models with human…
DeepCritic: Deliberate Critique with Large Language Models
Wenkai Yang, Jingwen Chen, Yankai Lin +1
As Large Language Models (LLMs) are rapidly evolving, providing accurate feedback and scalable oversight on their outputs becomes an urgent and critical problem. Leveraging LLMs as…
Large Language Diffusion Models
Shen Nie, Fengqi Zhu, Zebin You +7
The capabilities of large language models (LLMs) are widely regarded as relying on autoregressive models (ARMs). We challenge this notion by introducing LLaDA, a diffusion model tr…
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System
Zeyu Zhang, Jianxun Lian, Chen Ma +9
Trending topics have become a significant part of modern social media, attracting users to participate in discussions of breaking events. However, they also bring in a new channel…
GenSim: A General Social Simulation Platform with Large Language Model based Agents
Jiakai Tang, Heyang Gao, Xuchen Pan +11
With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. Whi…
ICLEval: Evaluating In-Context Learning Ability of Large Language Models
Wentong Chen, Yankai Lin, ZhenHao Zhou +4
In-Context Learning (ICL) is a critical capability of Large Language Models (LLMs) as it empowers them to comprehend and reason across interconnected inputs. Evaluating the ICL abi…
Large Language Model-based Human-Agent Collaboration for Complex Task Solving
Xueyang Feng, Zhi-Yuan Chen, Yujia Qin +4
In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite…
Don't Make Your LLM an Evaluation Benchmark Cheater
Kun Zhou, Yutao Zhu, Zhipeng Chen +6
Large language models~(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a…