59 citations · 110 across the 21 of their papers we have counts for
6 papers · 1 filter
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…
TEC: A Collection of Human Trial-and-error Trajectories for Problem Solving
Xinkai Zhang, Jingtao Zhan, Yiqun Liu +1
Trial-and-error is a fundamental strategy for humans to solve complex problems and a necessary capability for Artificial Intelligence (AI) systems operating in real-world environme…
Dynamic and Parametric Retrieval-Augmented Generation
Weihang Su, Qingyao Ai, Jingtao Zhan +2
Retrieval-Augmented Generation (RAG) has become a foundational paradigm for equipping large language models (LLMs) with external knowledge, playing a critical role in information r…
StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning
Yuanqing Yu, Zhefan Wang, Weizhi Ma +4
Despite their powerful text generation capabilities, large language models (LLMs) still struggle to effectively utilize external tools to solve complex tasks, a challenge known as…
Capability-aware Prompt Reformulation Learning for Text-to-Image Generation
Jingtao Zhan, Qingyao Ai, Yiqun Liu +2
Text-to-image generation systems have emerged as revolutionary tools in the realm of artistic creation, offering unprecedented ease in transforming textual prompts into visual art.…
Query Augmentation by Decoding Semantics from Brain Signals
Ziyi Ye, Jingtao Zhan, Qingyao Ai +4
Query augmentation is a crucial technique for refining semantically imprecise queries. Traditionally, query augmentation relies on extracting information from initially retrieved,…