Portrait de Huan Zhang

Huan Zhang

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Bang Liu
Sujets de recherche
Interaction humain-machine (IHM)
Modèles génératifs
Traitement du langage naturel
Site web
Google Scholar

Publications

Sparsity-Aware Evolution for Model Merging
Huan Zhang
Yanjian Zhang
Nadi Tomeh
Guillaume Wisniewski
Bang Liu
We propose a sparsity-aware evolutionary (SAE) framework for model merging that involves iterative pruning-merging cycles to act as a novel … (voir plus)mutation operator. We incorporate the sparsity constraints into the score function, which steers the evolutionary process to favor more sparse models, in addition to other conventional performance scores. Interestingly, the by-product of \textit{competition} for sparsity introduces an extra local \textit{attraction} and interplay into the evolutionary process: if one competitor has more zero elements, the other competitor's non-zero elements will occupy those positions, even though the less sparse competitor loses to the more sparse competitor in other positions. The proposed pipeline is evaluated on a variety of large-scale LLM benchmarks. Experiments demonstrate that our approach can improve model merging reliability across multiple benchmarks, and is easy to incorporate due to its simplicity and being orthogonal to most existing approaches.
2026-02-08
arXiv (prépublication)
doi.org
arxiv.org
HoneyComb: A Flexible LLM-Based Agent System for Materials Science
Huan Zhang
Yu Song
Ziyu Hou
Santiago Miret
Bang Liu
The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks in materials science. Many LLMs, how… (voir plus)ever, often struggle with the distinct complexities of materials science tasks, such as computational challenges, and rely heavily on outdated implicit knowledge, leading to inaccuracies and hallucinations. To address these challenges, we introduce HoneyComb, the first LLM-based agent system specifically designed for materials science. HoneyComb leverages a reliable, high-quality materials science knowledge base (MatSciKB) and a sophisticated tool hub (ToolHub) tailored specifically for materials science to enhance its reasoning and computational capabilities. MatSciKB is a curated, structured knowledge collection based on reliable literature, while ToolHub employs an Inductive Tool Construction method to generate, decompose, and refine API tools for materials science. Additionally, HoneyComb leverages a retriever module that adaptively selects the appropriate knowledge source or tools for specific tasks, thereby ensuring accuracy and relevance. Our results demonstrate that HoneyComb significantly outperforms baseline models across various tasks in materials science, effectively bridging the gap between current LLM capabilities and the specialized needs of this domain. Furthermore, our adaptable framework can be easily extended to other scientific domains, highlighting its potential for broad applicability in advancing scientific research and applications.
2024-10-06
NeurIPS.cc/2024/Workshop/AI4Mat (spotlight)
openreview.net
openreview.net
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
Yu Song
Santiago Miret
Huan Zhang
Bang Liu
2023-10-06
EMNLP/2023/Conference (publié)
openreview.net
openreview.net