26 citations · 28 across the 9 of their papers we have counts for
5 papers · 1 filter
Mem-: Adaptive Memory through Learning When and What to Generate
Xiaoqiang Wang, Chao Wang, Hadi Nekoei +5
We present Mem-, a framework for adaptive memory in large language model (LLM) agents, where useful guidance is generated on demand rather than retrieved from external memory st…
FACE: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition
Xiaoqiang Wang, Lingfei Wu, Tengfei Ma +1
Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. However, such a paradigm fails to comprehensively di…
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
Xiaoqiang Wang, Bang Liu, Siliang Tang +1
We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. Ex…
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Xiaoqiang Wang, Bang Liu, Siliang Tang +1
Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input context of generation, rendering the lack of…
Feeding What You Need by Understanding What You Learned
Xiaoqiang Wang, Bang Liu, Fangli Xu +3
Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-si…