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20192026
most citedRule Mining over Knowledge Graphs via Reinforcement Learning

23 citations · 35 across the 13 of their papers we have counts for

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9 papers · 1 filter

cs.CL2026

Controlled Self-Evolution for Algorithmic Code Optimization

Tu Hu, Ronghao Chen, Shuo Zhang +9

Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discove…

cs.CL2025

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…

cs.CL2025

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,…

cs.CL2025

CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation

Guangya Yu, Yanhao Li, Zongying Jiang +9

Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. With the impressive performance of large language mod…

cs.CL2024

Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency

Yuchen Shi, Deqing Yang, Jingping Liu +3

Previous works of negation understanding mainly focus on negation cue detection and scope resolution, without identifying negation subject which is also significant to the downstre…

cs.CL2024

Exploiting Duality in Open Information Extraction with Predicate Prompt

Zhen Chen, Jingping Liu, Deqing Yang +5

Open information extraction (OpenIE) aims to extract the schema-free triplets in the form of (\emph{subject}, \emph{predicate}, \emph{object}) from a given sentence. Compared with…