1 citations · 2 across the 6 of their papers we have counts for
6 papers
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
Hongcheng Guo, Juntao Yao, Boyang Wang +5
Mixture-of-Experts (MoE) architectures have emerged as a promising paradigm for scaling large language models (LLMs) with sparse activation of task-specific experts. Despite their…
FuzzCoder: Byte-level Fuzzing Test via Large Language Model
Liqun Yang, Jian Yang, Chaoren Wei +13
Fuzzing is an important dynamic program analysis technique designed for finding vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malic…
Towards Real-world Scenario: Imbalanced New Intent Discovery
Shun Zhang, Chaoran Yan, Jian Yang +5
New Intent Discovery (NID) aims at detecting known and previously undefined categories of user intent by utilizing limited labeled and massive unlabeled data. Most prior works ofte…
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations
Shun Zhang, Chaoran Yan, Jian Yang +4
New Intent Discovery (NID) strives to identify known and reasonably deduce novel intent groups in the open-world scenario. But current methods face issues with inaccurate pseudo-la…
New Intent Discovery with Attracting and Dispersing Prototype
Shun Zhang, Jian Yang, Jiaqi Bai +4
New Intent Discovery (NID) aims to recognize known and infer new intent categories with the help of limited labeled and large-scale unlabeled data. The task is addressed as a featu…
C-ICL: Contrastive In-context Learning for Information Extraction
Ying Mo, Jiahao Liu, Jian Yang +4
There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on task…