22 citations · 34 across the 11 of their papers we have counts for
11 papers
WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research
Zijian Li, Xin Guan, Bo Zhang +9
This paper tackles \textbf{open-ended deep research (OEDR)}, a complex challenge where AI agents must synthesize vast web-scale information into insightful reports. Current approac…
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,…
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…
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…
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…
How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions
Houquan Zhou, Yang Hou, Zhenghua Li +4
While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or…
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
Houquan Zhou, Yumeng Liu, Zhenghua Li +5
The sequence-to-sequence (Seq2Seq) approach has recently been widely used in grammatical error correction (GEC) and shows promising performance. However, the Seq2Seq GEC approach s…
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging
Houquan Zhou, Yang Li, Zhenghua Li +1
In recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks. But, in the unsupervised POS tagging task, works utilizing PLMs…
An In-depth Study on Internal Structure of Chinese Words
Chen Gong, Saihao Huang, Houquan Zhou +5
Unlike English letters, Chinese characters have rich and specific meanings. Usually, the meaning of a word can be derived from its constituent characters in some way. Several previ…
Fast and Accurate Neural CRF Constituency Parsing
Yu Zhang, Houquan Zhou, Zhenghua Li
Estimating probability distribution is one of the core issues in the NLP field. However, in both deep learning (DL) and pre-DL eras, unlike the vast applications of linear-chain CR…
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
Houquan Zhou, Yu Zhang, Zhenghua Li +1
In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing. But quite a few works focus on jo…