2 citations · 4 across the 11 of their papers we have counts for
11 papers
InsightChain: Optimized Chain-of-Insight Analytics for LLM-driven Data Visualization
Hanya Sun, Chen Zhang, Sheng Liang +2
Large language models (LLMs) are increasingly used for automated data visualization, yet existing approaches often frame visualization generation as a single-step mapping from user…
AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs
Sheng Liang, Yongyue Zhang, Nathanael Brian +4
Agentic LLM pipelines face escalating inference costs as context accumulates across retrieval, tool use, and multi-turn interactions. To control latency, deployments routinely comp…
SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation
Hang Lv, Sheng Liang, Hao Wang +6
Realizing personalized intelligence faces a core dilemma: sending user history to centralized large language models raises privacy concerns, while on-device small language models l…
MLDocRAG: Multimodal Long-Context Document Retrieval Augmented Generation
Yongyue Zhang, Yaxiong Wu
Understanding multimodal long-context documents that comprise multimodal chunks such as paragraphs, figures, and tables is challenging due to (1) cross-modal heterogeneity to local…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
Query-Centric Graph Retrieval Augmented Generation
Yaxiong Wu, Jianyuan Bo, Yongyue Zhang +2
Graph-based retrieval-augmented generation (RAG) enriches large language models (LLMs) with external knowledge for long-context understanding and multi-hop reasoning, but existing…
SGMem: Sentence Graph Memory for Long-Term Conversational Agents
Yaxiong Wu, Yongyue Zhang, Sheng Liang +1
Long-term conversational agents require effective memory management to handle dialogue histories that exceed the context window of large language models (LLMs). Existing methods ba…
Schema as Parameterized Tools for Universal Information Extraction
Sheng Liang, Yongyue Zhang, Yaxiong Wu +2
Universal information extraction (UIE) primarily employs an extractive generation approach with large language models (LLMs), typically outputting structured information based on p…
Effective and Efficient Schema-aware Information Extraction Using On-Device Large Language Models
Zhihao Wen, Sheng Liang, Yaxiong Wu +2
Information extraction (IE) plays a crucial role in natural language processing (NLP) by converting unstructured text into structured knowledge. Deploying computationally intensive…
Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation
Sheng Liang, Hang Lv, Zhihao Wen +4
Event extraction (EE) is a fundamental task in natural language processing (NLP) that involves identifying and extracting event information from unstructured text. Effective EE in…
From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
Yaxiong Wu, Sheng Liang, Chen Zhang +5
Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation…