6 papers
FGNet: Leveraging Feature-Guided Attention to Refine SAM2 for 3D EM Neuron Segmentation
Zhenghua Li, Hang Chen, Zihao Sun +2
Accurate segmentation of neural structures in Electron Microscopy (EM) images is paramount for neuroscience. However, this task is challenged by intricate morphologies, low signal-…
A Training-free LLM-based Approach to General Chinese Character Error Correction
Houquan Zhou, Bo Zhang, Zhenghua Li +2
Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in Chinese text. While conventional CSC focuses on character substitution errors caused by…
Mixture of Small and Large Models for Chinese Spelling Check
Ziheng Qiao, Houquan Zhou, Zhenghua Li
In the era of large language models (LLMs), the Chinese Spelling Check (CSC) task has seen various LLM methods developed, yet their performance remains unsatisfactory. In contrast,…
DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check
Ziheng Qiao, Houquan Zhou, Yumeng Liu +6
One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph. To accommodate th…
Capturing Nuanced Preferences: Preference-Aligned Distillation for Small Language Models
Yanggan Gu, Junzhuo Li, Sirui Huang +3
Aligning small language models (SLMs) with human values typically involves distilling preference knowledge from large language models (LLMs). However, existing distillation methods…
A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models
Houquan Zhou, Zhenghua Li, Bo Zhang +5
This work proposes a simple training-free prompt-free approach to leverage large language models (LLMs) for the Chinese spelling correction (CSC) task, which is totally different f…