5 citations · 9 across the 15 of their papers we have counts for
18 papers
Recognition Is Not Grounding: Benchmarking Multilingual Financial Evidence Extraction
Lingfei Qian, Yan Wang, Xueqing Peng +6
Comparing financial disclosures across languages requires identifying shared accounting concepts and recovering all numerical facts that express them. Existing benchmarks evaluate…
Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval
Linhai Ma, Ethan F. Wei, Xueqing Peng +3
Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however, the input is indirect evidence, such as a table cell whose me…
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…
Advances in Logic-Based Entity Resolution: Enhancing ASPEN with Local Merges and Optimality Criteria
Zhliang Xiang, Meghyn Bienvenu, Gianluca Cima +2
In this paper, we present ASPEN+, which extends an existing ASP-based system, ASPEN,for collective entity resolution with two important functionalities: support for local merges an…
From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMs
Jie He, Victor Gutiérrez-Basulto, Jeff Z. Pan
Reinforcement learning-based retrieval-augmented generation (RAG) methods enhance the reasoning abilities of large language models (LLMs). However, most rely only on final-answer r…
N2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning
Jie He, Simon Yu, Deyi Xiong +2
Recent advancements of in-context learning (ICL) show language models can significantly improve their performance when demonstrations are provided. However, little attention has be…
ASPEN: ASP-Based System for Collective Entity Resolution
Zhiliang Xiang, Meghyn Bienvenu, Gianluca Cima +2
In this paper, we present ASPEN, an answer set programming (ASP) implementation of a recently proposed declarative framework for collective entity resolution (ER). While an ASP enc…
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models
Xiongtao Zhou, Jie He, Yuhua Ke +3
Multimodal large language models (MLLMs) fine-tuned with multimodal instruction datasets have demonstrated remarkable capabilities in multimodal tasks. However, fine-tuning all par…
BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages
Junho Myung, Nayeon Lee, Yi Zhou +19
Large language models (LLMs) often lack culture-specific knowledge of daily life, especially across diverse regions and non-English languages. Existing benchmarks for evaluating LL…
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…
UniArk: Improving Generalisation and Consistency for Factual Knowledge Extraction through Debiasing
Yijun Yang, Jie He, Pinzhen Chen +2
Several recent papers have investigated the potential of language models as knowledge bases as well as the existence of severe biases when extracting factual knowledge. In this wor…
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…
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification
Simon Yu, Jie He, Víctor Gutiérrez-Basulto +1
Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification. Recent approaches to HMTC deal with the problem of imposing a…
Combining Global and Local Merges in Logic-based Entity Resolution
Meghyn Bienvenu, Gianluca Cima, Víctor Gutiérrez-Basulto +1
In the recently proposed Lace framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) t…
BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering
Jie He, Simon Chi Lok U, Víctor Gutiérrez-Basulto +1
Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the construction of commonsense reasoning datasets is expensive, and they are inevitably limited in the…
Transformer-based Entity Typing in Knowledge Graphs
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +2
We investigate the knowledge graph entity typing task which aims at inferring plausible entity types. In this paper, we propose a novel Transformer-based Entity Typing (TET) approa…