63 citations · 63 across the 7 of their papers we have counts for
6 papers · 1 filter
FDR: A Fine-Grained Full-Pipeline Reward Framework for DeepSearch Workflows
Bojian Xiong, Wentao Ding, Yujing Lu +11
With the widespread industrial deployment of Large Language Models (LLMs), DeepSearch has emerged as the dominant paradigm for resolving complex user queries. It typically operates…
DEP: A Decentralized Large Language Model Evaluation Protocol
Jianxiang Peng, Junhao Li, Hongxiang Wang +15
With the rapid development of Large Language Models (LLMs), a large number of benchmarks have been proposed. However, most benchmarks lack unified evaluation standard and require t…
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision
Yongqi Leng, Yikun Lei, Xikai Liu +7
Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., S…
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan
Lei Yang, Leiyu Pan, Bojian Xiong +14
Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their performance remains heavily biased toward high-res…
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
Haoran Sun, Renren Jin, Shaoyang Xu +10
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource lan…
Evaluating Large Language Models: A Comprehensive Survey
Zishan Guo, Renren Jin, Chuang Liu +8
Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous dow…