collaborators

7 papers

cs.AI2026

Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinement

Yufeng Han, Lifan Deng, Cunliang Kong +5

Classical Chinese poetry composition has long valued Tuiqiao, the iterative refinement of words, imagery, and prosody. However, many current AI poetry systems follow a one-shot gen…

cs.CL2026

InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs

Yuzhuo Bai, Shuzheng Si, Kangyang Luo +5

Large language models (LLMs) often hallucinate, yet most existing fact-checking methods treat factuality evaluation as a binary classification problem, offering limited interpretab…

cs.IR2025

MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints

Kangyang Luo, Shuzheng Si, Yuzhuo Bai +8

In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, par…

cs.CL2025

ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement

Kangyang Luo, Yuzhuo Bai, Shuzheng Si +9

Coreference Resolution (CR) is a critical task in Natural Language Processing (NLP). Current research faces a key dilemma: whether to further explore the potential of supervised ne…

cs.CL2025

MiniCPM4: Ultra-Efficient LLMs on End Devices

MiniCPM Team, Chaojun Xiao, Yuxuan Li +80

This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in…

cs.CL2025

Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning

Shuzheng Si, Haozhe Zhao, Cheng Gao +11

Teaching large language models (LLMs) to be faithful in the provided context is crucial for building reliable information-seeking systems. Therefore, we propose a systematic framew…

cs.CL2025

GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion

Kangyang Luo, Yuzhuo Bai, Cheng Gao +11

Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs. However, integrating the vital structural information of KGs into Larg…