6 citations · 9 across the 9 of their papers we have counts for
9 papers
Decoupled Mixture-of-Experts for Parametric Knowledge Injection
Baoqing Yue, Weihang Su, Qingyao Ai +5
Knowledge injection aims to equip large language models (LLMs) with external, domain-specific, or time-sensitive knowledge. Existing approaches typically face a trade-off between f…
Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models
Changyue Wang, Weihang Su, Qingyao Ai +5
Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have emerged as a popular paradigm to inj…
Skill Retrieval Augmentation for Agentic AI
Weihang Su, Jianming Long, Qingyao Ai +6
As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities…
Multi-Field Tool Retrieval
Yichen Tang, Weihang Su, Yiqun Liu +1
Integrating external tools enables Large Language Models (LLMs) to interact with real-world environments and solve complex tasks. Given the growing scale of available tools, effect…
MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
Qingyao Ai, Yichen Tang, Changyue Wang +3
Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are almost reached due to the gradual…
Augmenting Multi-Agent Communication with State Delta Trajectory
Yichen Tang, Weihang Su, Yujia Zhou +4
Multi-agent techniques such as role playing or multi-turn debates have been shown to be effective in improving the performance of large language models (LLMs) in downstream tasks.…
Parametric Retrieval Augmented Generation
Weihang Su, Yichen Tang, Qingyao Ai +6
Retrieval-augmented generation (RAG) techniques have emerged as a promising solution to enhance the reliability of large language models (LLMs) by addressing issues like hallucinat…
Mitigating Entity-Level Hallucination in Large Language Models
Weihang Su, Yichen Tang, Qingyao Ai +3
The emergence of Large Language Models (LLMs) has revolutionized how users access information, shifting from traditional search engines to direct question-and-answer interactions w…
DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models
Weihang Su, Yichen Tang, Qingyao Ai +2
Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). There are two k…