16 citations · 23 across the 10 of their papers we have counts for
10 papers
LLMMapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System
Yu Chao, Siyu Lin, xiaorong wang +7
We introduce LLM x MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM x MapReduce-V2, this version incorp…
Exploring the Benefit of Activation Sparsity in Pre-training
Zhengyan Zhang, Chaojun Xiao, Qiujieli Qin +7
Pre-trained Transformers inherently possess the characteristic of sparse activation, where only a small fraction of the neurons are activated for each token. While sparse activatio…
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Cheng Qian, Bingxiang He, Zhong Zhuang +8
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adep…
Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment
Yiju Guo, Ganqu Cui, Lifan Yuan +9
Alignment in artificial intelligence pursues the consistency between model responses and human preferences as well as values. In practice, the multifaceted nature of human preferen…
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language
Zekai Qu, Ruobing Xie, Chaojun Xiao +5
With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual in…
Universal Multi-modal Multi-domain Pre-trained Recommendation
Wenqi Sun, Ruobing Xie, Shuqing Bian +2
There is a rapidly-growing research interest in modeling user preferences via pre-training multi-domain interactions for recommender systems. However, Existing pre-trained multi-do…
MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation
Xiaozhi Wang, Hao Peng, Yong Guan +9
Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-eve…
Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models
Weize Chen, Xiaoyue Xu, Xu Han +5
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments, enabling substantial reductions in model storage and…
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules
Chaojun Xiao, Yuqi Luo, Wenbin Zhang +8
Pre-trained language models (PLMs) have achieved remarkable results on NLP tasks but at the expense of huge parameter sizes and the consequent computational costs. In this paper, w…
CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of Pre-trained Language Models
Yusheng Su, Xu Han, Yankai Lin +5
Fine-tuning pre-trained language models (PLMs) has demonstrated its effectiveness on various downstream NLP tasks recently. However, in many low-resource scenarios, the conventiona…