4 citations · 8 across the 11 of their papers we have counts for
11 papers
Do Reasoning Models Enhance Embedding Models?
Wun Yu Chan, Shaojin Chen, Huihao Jing +5
State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning…
is Theoretically Large Enough for Embedding-based Top- Retrieval
Zihao Wang, Hang Yin, Lihui Liu +4
This paper studies the Minimal Embeddable Dimension (MED): the least dimension in which there exists a configuration of object vectors so that every subset of size at most …
Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering
Weizhi Fei, Zihao Wang, hang Yin +3
Complex Query Answering (CQA) is a crucial reasoning task over Knowledge Graphs (KGs), which aims to answer first-order logical queries from incomplete KGs. While existing neural-s…
Transformers for Complex Query Answering over Knowledge Hypergraphs
Hong Ting Tsang, Zihao Wang, Yangqiu Song
Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modali…
Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering
Yao Xu, Shizhu He, Jiabei Chen +6
To address the issues of insufficient knowledge and hallucination in Large Language Models (LLMs), numerous studies have explored integrating LLMs with Knowledge Graphs (KGs). Howe…
Meta Operator for Complex Query Answering on Knowledge Graphs
Hang Yin, Zihao Wang, Yangqiu Song
Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (…
Extending Complex Logical Queries on Uncertain Knowledge Graphs
Weizhi Fei, Zihao Wang, Hang Yin +2
The study of machine learning-based logical query answering enables reasoning with large-scale and incomplete knowledge graphs. This paper advances this area of research by address…
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
Qineng Wang, Zihao Wang, Ying Su +2
Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experime…
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
Ying Su, Tianqing Fang, Huiru Xiao +4
Commonsense knowledge graph completion is a new challenge for commonsense knowledge graph construction and application. In contrast to factual knowledge graphs such as Freebase and…
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs
Ying Su, Jipeng Zhang, Yangqiu Song +1
It is well acknowledged that incorporating explicit knowledge graphs (KGs) can benefit question answering. Existing approaches typically follow a grounding-reasoning pipeline in wh…
On the Discussion of Large Language Models: Symmetry of Agents and Interplay with Prompts
Qineng Wang, Zihao Wang, Ying Su +1
Two ways has been discussed to unlock the reasoning capability of a large language model. The first one is prompt engineering and the second one is to combine the multiple inferenc…