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Shengji Tang

ACL 2026×2ICML 2026评测与基准数据与预训练智能体与工具强化学习与后训练可解释性
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一作贡献70
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方向广度13
合作网络12
2 一作 · 3 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

3
一作ACL 2026Poster· Natural Language Processing

A Scalable Multi- LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement

Shengji Tang · Jianjian Cao · Weihao Lin · Jiale Hong · Bo Zhang · Shuyue Hu · LEI BAI · Tao Chen · Wanli Ouyang · Peng Ye

摘要

Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks, leading to suboptimal performance. To address this, we propose SMCS, a Scalable Multi-LLM Collaboration System designed to effectively coordinate multiple open-source LLMs. The sys...

可解释性评测与基准数据与预训练OpenReview 项目页
作者位次 #5/12ACL 2026Findings· Findings of ACL

LLMR outer B ench: A Massive Benchmark and Unified Framework for LLM Routing

Hao Li · Yiqun Zhang · Zhaoyan Guo · Chenxu Wang · Shengji Tang · Qiaosheng Zhang · Yang Chen · Biqing Qi · Peng Ye · LEI BAI · Zhen Wang · Shuyue Hu

摘要

Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large-scale benchmark and unified framework for LLM routing. It comprises over 400K instances from 21 datasets and 33 models. Moreover, it provides comprehensive metri...

评测与基准效率与系统数据与预训练OpenReview 项目页

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