59 citations · 81 across the 7 of their papers we have counts for
17 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…
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…
Relative-Based Scaling Law for Neural Language Models
Baoqing Yue, Jinyuan Zhou, Zixi Wei +3
Scaling laws aim to accurately predict model performance across different scales. Existing scaling-law studies almost exclusively rely on cross-entropy as the evaluation metric. Ho…
What Scales in Cross-Entropy Scaling Law?
Junxi Yan, Zixi Wei, Qingyao Ai +2
The cross-entropy scaling law has long served as a key tool for guiding the development of large language models. It shows that cross-entropy loss decreases in a predictable power-…
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…
Evaluating Intelligence via Trial and Error
Jingtao Zhan, Jiahao Zhao, Jiayu Li +7
Intelligence is a crucial trait for species to find solutions within a limited number of trial-and-error attempts. Building on this idea, we introduce Survival Game as a framework…