15 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…
Chain Of Thought Compression: A Theoretical Analysis
Juncai Li, Ru Li, Yuxiang Zhou +2
Chain-of-Thought (CoT) has unlocked advanced reasoning abilities of Large Language Models (LLMs) with intermediate steps, yet incurs prohibitive computational costs due to generati…
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…
Consistency-Aware Editing for Entity-level Unlearning in Language Models
Xiaoqi Han, Víctor Gutiérrez-Basulto, Ru Li +3
Large language models (LLMs) risk retaining sensitive, copyrighted, or harmful information from their training data. Entity-level unlearning addresses this issue by removing all kn…
Uncovering and Mitigating Transient Blindness in Multimodal Model Editing
Xiaoqi Han, Ru Li, Ran Yi +4
Multimodal Model Editing (MMED) aims to correct erroneous knowledge in multimodal models. Existing evaluation methods, adapted from textual model editing, overstate success by rely…
Plan Then Retrieve: Reinforcement Learning-Guided Complex Reasoning over Knowledge Graphs
Yanlin Song, Ben Liu, Víctor Gutiérrez-Basulto +5
Knowledge Graph Question Answering aims to answer natural language questions by reasoning over structured knowledge graphs. While large language models have advanced KGQA through t…
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 Mixture of Experts for Multimodal Entity Linking
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +2
Multimodal Entity Linking (MEL) aims to link ambiguous mentions within multimodal contexts to associated entities in a multimodal knowledge base. Existing approaches to MEL introdu…
Multi-level Matching Network for Multimodal Entity Linking
Zhiwei Hu, Víctor Gutiérrez-Basulto, Ru Li +1
Multimodal entity linking (MEL) aims to link ambiguous mentions within multimodal contexts to corresponding entities in a multimodal knowledge base. Most existing approaches to MEL…
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…
COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +2
Knowledge graph entity typing (KGET) aims to infer missing entity type instances in knowledge graphs. Previous research has predominantly centered around leveraging contextual info…
Leveraging Intra-modal and Inter-modal Interaction for Multi-Modal Entity Alignment
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +2
Multi-modal entity alignment (MMEA) aims to identify equivalent entity pairs across different multi-modal knowledge graphs (MMKGs). Existing approaches focus on how to better encod…
HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +2
In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple associated with attribute-value qualifiers, which express additional factual knowledge. The hype…
Knowledge-Aware Neuron Interpretation for Scene Classification
Yong Guan, Freddy Lecue, Jiaoyan Chen +2
Although neural models have achieved remarkable performance, they still encounter doubts due to the intransparency. To this end, model prediction explanation is attracting more and…
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +2
Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs. Existing approaches to KGET focus on how to better encode the knowledge prov…