4 citations · 8 across the 6 of their papers we have counts for
7 papers
WritingBench: A Comprehensive Benchmark for Generative Writing
Yuning Wu, Jiahao Mei, Ming Yan +8
Recent advancements in large language models (LLMs) have significantly enhanced text generation capabilities, yet evaluating their performance in generative writing remains a chall…
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…
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding
Anwen Hu, Haiyang Xu, Jiabo Ye +8
Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for…
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents
Hongzhan Chen, Hehong Chen, Ming Yan +8
Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing conversational agents that mimic diverse characters and human…
Improving Cross-lingual Representation for Semantic Retrieval with Code-switching
Mieradilijiang Maimaiti, Yuanhang Zheng, Ji Zhang +3
Semantic Retrieval (SR) has become an indispensable part of the FAQ system in the task-oriented question-answering (QA) dialogue scenario. The demands for a cross-lingual smart-cus…
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…