41 citations · 122 across the 21 of their papers we have counts for
21 papers
DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis
Zhichao Shi, Cehao Yang, Hao Zhou +6
Synthetic data has emerged as a crucial solution to the data scarcity bottleneck in large language models (LLMs), particularly for specialized domains and low-resource languages. H…
ROMA: Real-time Omni-Multimodal Assistant with Interactive Streaming Understanding
Xueyun Tian, Wei Li, Bingbing Xu +3
Recent Omni-multimodal Large Language Models show promise in unified audio, vision, and text modeling. However, streaming audio-video understanding remains challenging, as existing…
Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization
Xueyun Tian, Minghua Ma, Bingbing Xu +6
Supervised fine-tuning (SFT) on chain-of-thought (CoT) trajectories demonstrations is a common approach for enabling reasoning in large language models. Standard practices typicall…
BaseCal: Unsupervised Confidence Calibration via Base Model Signals
Hexiang Tan, Wanli Yang, Junwei Zhang +7
Reliable confidence is essential for trusting the outputs of LLMs, yet widely deployed post-trained LLMs (PoLLMs) typically compromise this trust with severe overconfidence. In con…
JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation
Zhichao Shi, Xuhui Jiang, Chengjin Xu +6
Current evaluation methods for large language models (LLMs) primarily rely on static benchmarks, presenting two major challenges: limited knowledge coverage and fixed difficulties…
Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
Juan Chen, Baolong Bi, Wei Zhang +5
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating their parametric knowledge with external retrieved content. However, knowledge conflicts c…
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs
Hexiang Tan, Fei Sun, Sha Liu +8
As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness. However, existing detection…
MIGE: Mutually Enhanced Multimodal Instruction-Based Image Generation and Editing
Xueyun Tian, Wei Li, Bingbing Xu +3
Despite significant progress in diffusion-based image generation, subject-driven generation and instruction-based editing remain challenging. Existing methods typically treat them…
A Survey on LLM-as-a-Judge
Jiawei Gu, Xuhui Jiang, Zhichao Shi +13
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. La…
Qsnail: A Questionnaire Dataset for Sequential Question Generation
Yan Lei, Liang Pang, Yuanzhuo Wang +2
The questionnaire is a professional research methodology used for both qualitative and quantitative analysis of human opinions, preferences, attitudes, and behaviors. However, desi…
A Survey on Large Language Model Hallucination via a Creativity Perspective
Xuhui Jiang, Yuxing Tian, Fengrui Hua +3
Hallucinations in large language models (LLMs) are always seen as limitations. However, could they also be a source of creativity? This survey explores this possibility, suggesting…
Unlocking the Power of Large Language Models for Entity Alignment
Xuhui Jiang, Yinghan Shen, Zhichao Shi +6
Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on co…
Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?
Hexiang Tan, Fei Sun, Wanli Yang +3
While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts genera…
On the Evolution of Knowledge Graphs: A Survey and Perspective
Xuhui Jiang, Chengjin Xu, Yinghan Shen +6
Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensiv…
Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets
Xuhui Jiang, Chengjin Xu, Yinghan Shen +7
The flourishing of knowledge graph applications has driven the need for entity alignment (EA) across KGs. However, the heterogeneity of practical KGs, characterized by differing sc…
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Zixuan Li, Xiaolong Jin, Saiping Guan +4
Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenge…
Link Prediction on N-ary Relational Data Based on Relatedness Evaluation
Saiping Guan, Xiaolong Jin, Jiafeng Guo +2
With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focu…
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
Zixuan Li, Xiaolong Jin, Wei Li +5
Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future…
Path-Based Attention Neural Model for Fine-Grained Entity Typing
Denghui Zhang, Pengshan Cai, Yantao Jia +3
Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure. Traditional distant supervision based methods employ a s…
Efficient Parallel Translating Embedding For Knowledge Graphs
Denghui Zhang, Manling Li, Yantao Jia +2
Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the transla…
Locally Adaptive Translation for Knowledge Graph Embedding
Yantao Jia, Yuanzhuo Wang, Hailun Lin +2
Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and Tra…