8 papers
CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answering
Zhichao Yan, Shizhao Li, Jiapu Wang +5
Long-form question answering increasingly relies on retrieved evidence to make LLM outputs verifiable, with inline citations tracing claims to source documents. However, existing s…
Cumulative Path-Level Semantic Reasoning for Inductive Knowledge Graph Completion
Jiapu Wang, Xinghe Cheng, Zezheng Wu +6
Conventional Knowledge Graph Completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to pe…
Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScore
Zhichao Yan, Yunxiao Zhao, Jiapu Wang +4
Current evaluation methods for Retrieval Augmented Generation (RAG) suffer from \textit{factual myopia}: they relentlessly emphasize factual accuracy yet neglect global logical int…
A Large-Scale Chinese Knowledge Graph-Text Alignment Dataset for Benchmarking Knowledge-Grounded LLMs
Chengwei Wu, Xingrui Zhuo, Mingyang Gao +3
Reliable evaluation of knowledge-grounded Large Language Models (LLMs) in Chinese requires resources that explicitly align Chinese-language text with verifiable Knowledge Graph (KG…
Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities
Zhichao Yan, Jiapu Wang, Jiaoyan Chen +6
Retrieval-Augmented Generation (RAG) shows impressive performance by supplementing and substituting parametric knowledge in Large Language Models (LLMs). Retrieved knowledge can be…
: Multi-level Tree-based Automatic Program Repair with Large Language Models
Quanming Liu, Xupeng Bu, Zhichao Yan +1
Automatic Program Repair (APR) is a core technology in software development and maintenance, with aims to enable automated defect repair with minimal human intervention. In recent…
Atomic Fact Decomposition Helps Attributed Question Answering
Zhichao Yan, Jiapu Wang, Jiaoyan Chen +3
Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a given question. Retrieval is a widely adopted approach, includ…
Contrast All the Time: Learning Time Series Representation from Temporal Consistency
Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
Representation learning for time series using contrastive learning has emerged as a critical technique for improving the performance of downstream tasks. To advance this effective…