14 citations · 77 across the 31 of their papers we have counts for
7 papers · 1 filter
KG-o1: Enhancing Multi-hop Question Answering in Large Language Models via Knowledge Graph Integration
Nan Wang, Yongqi Fan, yansha zhu +6
Large Language Models (LLMs) face challenges in knowledge-intensive reasoning tasks like classic multi-hop question and answering, which involves reasoning across multiple facts. T…
I2CR: Intra- and Inter-modal Collaborative Reflections for Multimodal Entity Linking
Ziyan Liu, Junwen Li, Kaiwen Li +6
Multimodal entity linking plays a crucial role in a wide range of applications. Recent advances in large language model-based methods have become the dominant paradigm for this tas…
TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking
Yongqi Fan, Xiaoyang Chen, Dezhi Ye +6
Reasoning-intensive ranking models built on Large Language Models (LLMs) have made notable progress. However, existing approaches often rely on large-scale LLMs and explicit Chain-…
LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review
Cheng Yuan, Xinkai Rui, Yongqi Fan +5
Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination issues, such as generating inaccu…
MinosEval: Distinguishing Factoid and Non-Factoid for Tailored Open-Ended QA Evaluation with LLMs
Yongqi Fan, Yating Wang, Guandong Wang +4
Open-ended question answering (QA) is a key task for evaluating the capabilities of large language models (LLMs). Compared to closed-ended QA, it demands longer answer statements,…
Can Multimodal Large Language Models Understand Spatial Relations?
Jingping Liu, Ziyan Liu, Zhedong Cen +5
Spatial relation reasoning is a crucial task for multimodal large language models (MLLMs) to understand the objective world. However, current benchmarks have issues like relying on…