1 citations · 2 across the 6 of their papers we have counts for
6 papers
CONCAT: Consensus- and Confidence-Driven Ad Hoc Teaming for Efficient LLM-Based Multi-Agent Systems
Ziyang Ma, Dingyi Zhang, Sichu Liang +4
Although large language model (LLM) based multi-agent systems (MAS) show their capability to solve complex tasks and achieve higher performance over single agent systems, they lead…
From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development
Xuan Liu, Ziyu Li, Mu He +10
Ontologies are essential for structuring domain knowledge, improving accessibility, sharing, and reuse. However, traditional ontology construction relies on manual annotation and c…
Mini-Omni-Reasoner: Token-Level Thinking-in-Speaking in Large Speech Models
Zhifei Xie, Ziyang Ma, Zihang Liu +7
Reasoning is essential for effective communication and decision-making. While recent advances in LLMs and MLLMs have shown that incorporating explicit reasoning significantly impro…
Slow Tuning and Low-Entropy Masking for Safe Chain-of-Thought Distillation
Ziyang Ma, Qingyue Yuan, Linhai Zhang +1
Previous chain-of-thought (CoT) distillation methods primarily focused on enhancing the reasoning capabilities of Small Language Models (SLMs) by utilizing high-quality rationales…
From Text to Trajectories: GPT-2 as an ODE Solver via In-Context
Ziyang Ma, Baojian Zhou, Deqing Yang +1
In-Context Learning (ICL) has emerged as a new paradigm in large language models (LLMs), enabling them to perform novel tasks by conditioning on a few examples embedded in the prom…
Large Language Models Have Intrinsic Meta-Cognition, but Need a Good Lens
Ziyang Ma, Qingyue Yuan, Zhenglin Wang +1
Previous research has primarily focused on the cognitive error detection capabilities of Large Language Models (LLMs), often prompting them to analyze mistakes in reasoning chains.…