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Shengnan Guo

ACL 2026×2KDD 2026×2AAAI 2026NeurIPS 2026检索与知识对齐与偏好效率与系统推理可解释性
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0 一作 · 6 篇 · ACL 2026 / KDD 2026 / AAAI 2026 / NeurIPS 2026

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2026 顶会论文

6
作者位次 #7/9ACL 2026Poster· Natural Language Processing

STK -Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation

Shuyuan Zhao · Wei Chen · Weijie Zhang · Xinrui Hou · Junfeng Shen · Boyan Shi · Shengnan Guo · Youfang Lin · Huaiyu Wan

摘要

Temporal Knowledge Graph (TKG) extrapolation aims to predict future events based on historical facts. Recent studies have attempted to enhance TKG extrapolation by integrating TKG’s evolving structural representations and textual event chains into Large Language Models (LLMs). Yet, two main chall...

检索与知识推理对齐与偏好OpenReview 项目页
作者位次 #5/7ACL 2026Findings· Findings of ACL

SAM o RA : Semantic-Aware Mixture of L o RA Experts for Task-Adaptive Learning

Boyan Shi · Wei Chen · Shuyuan Zhao · Junfeng Shen · Shengnan Guo · Shaojiang Wang · Huaiyu Wan

摘要

The combination of Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) has shown significant potential for enhancing the multi-task learning capabilities of Large Language Models. However, existing methods face two primary challenges: (1)Imprecise Routing in the current MoE-LoRA method fails...

效率与系统对齐与偏好可解释性OpenReview 项目页

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