1 citations · 2 across the 5 of their papers we have counts for
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Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework
Yanchen Wu, Tenghui Lin, Yingli Zhou +7
Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…
LightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data
Teng Lin
The scarcity of high-quality knowledge graphs (KGs) remains a critical bottleneck for downstream AI applications, as existing extraction methods rely heavily on error-prone pattern…
DataPuzzle: Breaking Free from the Hallucinated Promise of LLMs in Data Analysis
Zhengxuan Zhang, Zhuowen Liang, Yin Wu +3
Large language models (LLMs) are increasingly applied to multi-modal data analysis -- not necessarily because they offer the most precise answers, but because they provide fluent,…
MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering
Teng Lin, Yuyu Luo, Honglin Zhang +4
Multi-entity question answering (MEQA) represents significant challenges for large language models (LLM) and retrieval-augmented generation (RAG) systems, which frequently struggle…
SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph
Teng Lin, Yizhang Zhu, Yuyu Luo +1
Multi-entity question answering (MEQA) poses significant challenges for large language models (LLMs), which often struggle to consolidate scattered information across multiple docu…